Sep 4, 2014 | Author Videos, Volume 1 - Issue 2
Robert C. Reardon, Sara C. Bertoch
Educational counseling has declined as a counseling specialization in the United States, although the need for this intervention persists and is being met by other providers. This article illustrates how career theories such as Holland’s RIASEC theory can inform a revitalized educational counseling practice in secondary and postsecondary settings. The theory suggests that six personality types—Realistic, Investigative, Artistic, Social, Enterprising, and Conventional—have varying relationships with one another and that they can be associated to the same six environmental areas to assess educational and vocational adjustment. Although educational counseling can be viewed as distinctive from mental health counseling and/or career counseling, modern career theories can inform the practice of educational counseling for the benefit of students and schools.
Keywords: educational counseling, career theory, Holland, secondary education, postsecondary education
In searching for a formal definition of educational counseling, we found only one in the APA Dictionary of Psychology (VandenBos, 2007):
The counseling specialty concerned with providing advice and assistance to students in the development of their educational plans, choice of appropriate courses, and choice of college or technical school. Counseling may also be applied to improve study skills or provide assistance with school-related problems that interfere with performance, for example, learning disabilities. Educational counseling is closely associated with vocational counseling because of the relationship between educational training and occupational choice. (p. 314)
The Counseling Dictionary (Gladding, 2006) does not mention the term “educational counseling” in the following definition of counseling.
The application of mental health, psychological or human development principles, through cognitive, affective, behavioral or systemic interventions, strategies that address wellness, personal growth, or career development, as well as pathology. (Gladding, 2006, p. 37)
A renewed focus on educational counseling may be underway. The American Counseling Association meeting in Pittsburgh in 2010 brought together delegates from 29 major counseling organizations who agreed for the first time on a common definition of counseling. Educational goals were explicitly included in this definition: “Counseling is a professional relationship that empowers diverse individuals, families and groups to accomplish mental health, wellness, education, and career goals” (Breaking News, May 7, 2010).
The purpose of this article is to describe five functions essential for educational counseling (Hutson, 1958) and to use them to illustrate how Holland’s RIASEC theory might inform this counseling practice: (a) choosing a college or school for postsecondary training, (b) selecting an academic program or major, (c) adjusting to the college or academic program, (d) assessing academic performance, and (e) connecting education, career, and life decisions.
Historical Perspective
In tracing what has happened to educational counseling, a brief historical review can be helpful. In the early days of the vocational guidance movement, Brewer (1932) shifted the focus of guidance from vocation and occupation to education and instruction. He went so far as to institutionalize guidance as a professional field by linking the terms education and guidance and even using them synonymously. This could have elevated educational counseling to a more prominent position in the profession, but that did not happen. Brewer and others viewed guidance as limited by the descriptive adjective “vocational” with an emphasis on occupational choice (Shertzer & Stone, 1976), and this resulted in an estrangement between vocational and educational counseling.
Shertzer and Stone (1976) reported that the term “educational guidance” was first used in a doctoral dissertation by Truman L. Kelley at Teachers College, Columbia University, in 1914, and that he used it to describe the help given to students who had questions about choice of studies and school adjustment. Stephens (1970) pointed out that the shift from vocational choice to “guidance as education” ruptured the basic nature of the vocational guidance movement, separating the focus on “vocation” to “education.” Thus, vocational theory became associated with occupational choice and only tangentially related to educational choice, and we view this as leading to the separation of educational guidance and counseling from career theory.
In a comprehensive review of educational guidance literature published from 1933–1956, Hutson (1958) saw the counseling element of the educational guidance program as its most important function. He devoted a chapter to “Counseling for Some Common Problems” in which he identified 10 discrete but overlapping counseling situations. Several elements focused on educational counseling, including choice of subjects and curriculums, college-going (choice of going to college or working; choice of a particular college), and length of stay in school. Each of these problem areas involved counseling related to student psychological and educational characteristics, goals, and decision-making skills. Of relevance to this article, Hutson identified no theory related to educational counseling and cited only the vocational theory of Eli Ginzberg (Ginzberg, Ginsburg, Axelrad, & Herma, 1946) as informing vocational counseling. Theory-based educational counseling had not yet arrived.
The practice of educational counseling has faded from view in contemporary guidance and counseling literature. We conducted a search of journal titles and abstracts within the social sciences area using the term “educational counseling” and our university’s online library database system using Cambridge Scientific Abstracts (CSA) and PsychInfo. We were interested in how many “hits” for the past 10 years we would find in the following journals: Career Development Quarterly, Journal of Career Assessment, Journal of College Counseling, Journal of College Student Development, Journal of Counseling & Development, and Journal of Counseling Psychology. The search provided a total of seven results with only four falling into one of these six journals.
Advising, Coaching, Brokering
While the field of educational counseling seems to have been in decline for the past 50 years, other specialties have emerged to take its place, including academic advising, academic coaching, and educational brokering.
The field of academic advising has been very active in the past 30 years. Ender, Winston, and Miller (1984) defined developmental academic advising as “a systematic process based on a close student-advisor relationship intended to aid students in achieving educational, career, and personal goals through the utilization of the full range of institutional and community resources” (p. 19). Later, Creamer (2000) defined it as “an educational activity that depends on valid explanations of complex student behaviors and institutional conditions to assist college students in making and executing educational and life plans” (p. 18). While generally careful to distinguish between the terms advising and counseling, the National Academic Advising Association (NACADA; http://www.nacada.ksu.edu/index.htm) has fully embraced most of the educational planning and adjustment issues faced by postsecondary students that heretofore might have been included in the domain of educational counseling.
It is beyond the scope of this article to fully explore the notion of academic coaching, so we will limit our comments to the general field of life and career coaching (Chung & Gfroerer, 2003; Patterson, 2008). In general, proponents view coaching as a service focused on a student’s future goals and the creation of a new life path based on less formal collegial mentoring relationships and a positive, preventive wellness model. Opponents view coaching as practicing counseling without proper training or certification because there are limited professional standards or requirements in the coaching field.
Finally, the educational brokering movement in the 1970s was focused on helping adult learners navigate their way through postsecondary educational experiences (Heffernan, 1981). The educational broker independently assisted learners in the process of exploring, researching, and deciding on educational alternatives available. Some educational brokering proponents (Heffernan, 1981) held the view that an educational counselor employed by a specific institution would be biased and “guide” prospective students into the academic programs offered by the employing organization. Brokers were seen as neutral guides to the full range of educational options available to postsecondary learners.
Modern Career Theories
In this article, we examine the topic of educational counseling and suggest that modern career theories could contribute to a revitalization of this function. These theories, identified and described by Brown (2002), include career contextualist theory (Young, Valach, & Collin, 2002); Gottfredson’s theory of circumscription, compromise, and self-creation (L. Gottfredson, (2002); cognitive information processing theory (Sampson, Reardon, Peterson & Lenz, 2004); life stage/life space theory (Super, Savickas, & Super, 1996); narrative construction theory (Savickas, 2002); person-environment correspondence theory (Dawis, 2002); RIASEC theory (Holland, 1997); and social cognitive career theory (Lent, Brown, & Hackett, 2002). We illustrate our idea of how career theory might be useful in educational guidance and counseling programs using Holland’s (1997) RIASEC theory, emphasizing the environmental aspect of the theory.
Thus far, we have identified the function of educational counseling as an early component of the developing field of guidance and counseling, and we have outlined trends that have negated that function more recently. The irony is that the need for educational counseling services remains strong today, but it needs revitalization. We believe that the application of new theory, especially career theory, would be useful in that process and inform practice and research in the field. In this article, we focus on Holland’s RIASEC theory as one theory for accomplishing this revitalization. At the same time, we draw upon some of the basic functions of educational counseling drawn from the literature (Hutson, 1958; VandenBos, 2007).
Holland’s RIASEC Theory
Holland’s theory and the related tools such as the Self-Directed Search (SDS; Holland, 1994) have become familiar icons in the career counseling field. Since the introduction of the SDS in 1972 and its use with over 29 million people worldwide (Psychological Assessment Resources, 2009), its incorporation into the Strong Interest Inventory (Harmon, Hansen, Borgen, & Hammer, 1994) and many other tools, we believe that most counselors feel comfortable and knowledgeable about this system. However, we also believe that the widespread familiarity with the hexagon and SDS is based on incomplete and outdated understandings of Holland’s contributions. For many, the theory is viewed as a simple matching model of three personality types, e.g., the three-letter SDS summary code, and the codes of occupations taken from some source, e.g., O*Net (http://online.onetcenter.org/), Occupations Finder (Holland, 2000).
One reason for the partial understanding of Holland’s theory and applications may be the result of the massive volume of research and literature that has been produced since 1957. Authors (2008) reported 1,609 reference citations from 1953–2007 in 197 different journals which make it extremely difficult to fully understand and utilize this body of work. Moreover, many articles have appeared in education journals not often read by counselors, e.g., Journal of Higher Education, Research in Higher Education, Higher Education, and the Review of Higher Education. It is no small irony that Holland’s early work was undertaken in educational settings examining students undecided about their major, adjustment to college, the nature of academic environments, and the work of the faculty within disciplines. Smart, Feldman, and Ethington (2000) recognized this gap in applying Holland’s work to higher education, and their research collaborators have published over 20 articles seeking to address it.
This article focuses on how college students struggle with varied educational decisions, e.g., undecided about their college major, and then examines the ways in which Holland’s RIASEC theory might be used in educational interventions. We begin with a review of Holland’s theory with respect to personality and environment, and then describe several practical tools based on the theory that might be used in educational counseling.
Personality
Holland’s typological theory (Holland, 1997) specifies a theoretical connection between personality and environment that makes it possible to use the same RIASEC classification system for both. Many inventories and career assessment tools use the typology to enable individuals to categorize their interests and personal characteristics in terms of combinations of the six types: Realistic (R), Investigative (I), Artistic (A), Social (S), Enterprising (E), or Conventional (C). These six types are briefly defined in relation to educational options in Table 1.
According to RIASEC theory, if a person and an environment have the same or similar codes, e.g., an Investigative person in an Investigative environment, then the person will likely be satisfied and persist in that environment (Holland, 1997). This satisfaction will result from individuals being able to express their personality in an environment that is supportive and includes other persons who have the same or similar personality traits. It should be noted that neither people nor environments are exclusively one type, but rather combinations of all six types. Their dominant type is an approximation of an ideal, modal type.
The profile of the six types can be described in terms of a number of secondary constructs, e.g., the degree of differentiation (flat or uneven profile), consistency (level of similarity of interests or characteristics on the RIASEC hexagon for the first two letters of a three-letter Holland code), or identity (stability characteristics of the type). Each of these factors moderates predictions about the behavior related to the congruence level between a person and an environment. These secondary constructs provide an in-depth schema for understanding a person’s SDS results with diagnostic implications regarding the amount of counselor involvement and skill that may be needed for an intervention (Reardon & Lenz, 1999). Given extended discussion of these ideas in other literature (Reardon & Lenz, 1998), we will not focus on them here but concentrate our attention on the environmental aspects of RIASEC theory in education.
Environments
While the personality aspects of Holland’s theory are widely known, the environmental aspects—especially of college campuses, fields of study, and work positions—are less well understood and appreciated (Gottfredson & Holland, 1996). Holland’s early efforts with the National Merit Scholarship Corporation (NMSC) and the American College Testing Program enabled him to look at colleges and academic disciplines as environments. It is important to note that RIASEC theory had its roots in higher education and later focused on occupations.
Gottfredson and Richards (1999) traced the history of Holland’s efforts to classify educational and occupational environments. Holland initially studied the numbers of incumbents in a particular environment to classify occupations or colleges in terms of RIASEC categories, but he later moved to study the characteristics of the environment independent of the persons in it. College catalogs and descriptions of academic disciplines were among the public records used to study institutional environments. Astin and Holland (1961) developed the Environmental Assessment Technique (EAT) while at the NMSC as a method for measuring college RIASEC environments.
Smart et al. (2000) presented evidence concerning the way academic departments socialize students. They reported that “faculty members in different clusters of academic disciplines create distinctly different academic environments as a consequence of their preference for alternative goals for undergraduate education, their emphasis on alternative teaching goals and student competencies in their respective classes, and their reliance on different approaches to classroom instruction and ways of interacting with students inside and outside their classes” (p. 238). Furthermore, these environments “have a strong socializing influence on change and the stability of students’ abilities and interests—that is, what students do and do not learn or acquire as a consequence of their collegiate experiences” (p. 238). Smart et al. noted that faculty in Investigative, Artistic, Social, and Enterprising disciplines create academic environments in a manner consistent with Holland’s theory, and “the degree to which academic environments are ‘successful’ in their efforts to socialize students to their respective patterns of abilities and interests thus appears to differ considerably, with Artistic and Investigative environments being the most ‘successful’ and the Social and Enterprising environments being less ‘successful’” (p. 146).
These findings suggest that students might best view academic programs in terms of the IASE schema and focus on the kinds of abilities and interests they wish to develop while in college. Such understandings and goal setting could be explored in educational counseling.
Finally, Tracey and Darcy (2002) reported that college students without an intuitive RIASEC schema for organizing information about interests and occupations experience greater career indecision. This finding suggests that the RIASEC hexagon may have a normative benefit regarding the classification of occupations and fields of study. There is increasing evidence that a RIASEC cognitive structure is associated with positive career decision variables (Tracey, 2008). Persons adhering to this structure had stronger career certainty, interest-occupation congruence, and career decision-making self-efficacy at the beginning of a career course than those not using the RIASEC structure. Moreover, teaching this structure in a career course led to increased certainty, congruence, and self-efficacy at the end of the course for those adhering to the model.
Using RIASEC Theory in Educational Counseling
In this section, we discuss the five basic educational counseling functions identified by Hutson (1958), and how Holland’s RIASEC theory might inform this practice. To address these five problems in educational counseling from a RIASEC perspective, it would be important for the counselor to have a basic understanding of Holland’s theory (Holland, 1997). The client might complete the Self-Directed Search (Holland, 1994) and review the Occupations Finder (Holland, 2000), Educational Opportunities Finder (Rosen, Holmberg, & Holland, 1997), and You and Your Career (Holland, 1994) booklets. These materials operationalize and explain the theory in client terms. Armed with this basic information and these tools, the counselor and client can enter into a collaborative relationship to resolve educational problems and make educational decisions.
Choosing a College or School
The number of options for education and training is very large. Choices Planner (Bridges, 2009) was examined for one state and 196 postsecondary schools offering associate, bachelors, and professional (postgraduate) degrees were found. The Choices system makes it possible to use varied criteria for selecting among these options, including five school types, (e.g., public, private), specific miles from a designated ZIP postal code, six regions of the state, five campus or town settings of the school, eight tuition ranges, five affiliations (e.g., women, religious), on-campus housing, and over 30 sports options for men or women. If the student wanted to explore options in additional states the number of options would grow exponentially.
The array of postsecondary schools has very limited options for Realistic and Conventional types, which led Smart et al. (2000) to exclude these areas from their study of baccalaureate level colleges and universities. College level occupations are least frequently associated with the Conventional and Realistic categories, while Investigative and Artistic work are most likely associated with college level employment or the highest level of cognitive ability. Smart et al. found few college majors, faculty, or students in their samples categorized as Realistic or Conventional.
Taking this a step further, the number of associate, bachelors, and professional academic programs listed in the Educational Opportunities Finder (EOF; Rosen et al., 1997) were tabulated in relation to RIASEC categories. Of the 750 postsecondary programs of study listed in the EOF, there were 296 offered at the associate level, 492 at the bachelor’s level, and 645 at the professional level. Because some programs are offered at more than one degree level, the resulting total degree programs listed in the EOF number 1,517. Inspection of Figure 1 shows proportionally more Realistic and Conventional programs are available at the associate degree level in comparison to the other two degrees. Conversely, more professional degrees are offered in the IAS categories. This suggests that vocational technical schools and community colleges would be the types of schools most likely offering programs in these two areas. In this way, RIASEC theory could be used to guide selection of a school.
Authors (1996) documented this phenomenon in their research and reported that the student body at their postsecondary institution was composed predominately of S, E, and I types, creating an SEI-type school. They reported 153 fields of study at the university enrolled 10,439 students with declared majors in the following categories: R, 5%; I, 19%; A, 13%; S, 34%; E, 19%; and C, 10%. This suggests a student body with a profile of SEIACR. Such a student population would find C and R types in a minority.
RIASEC theory can inform the process of choosing a college by providing a conceptual schema of six environments and judging the priority and influence of each in socializing enrolled students. Students with E-type personalities (e.g., interests and skills) might have the best fit in a school that reinforced and prized those traits, and the same would be true for the remaining RIASC environments. In the following sections we will explain more how the environmental aspect of RIASEC theory may be used in educational counseling.
Selecting an Academic Program or Major
The Choices Planner (Bridges, 2009) lists over 780 specific academic programs or fields of study (majors) for students for the selected state. Large universities may have several hundred undergraduate majors and this can be overwhelming to students required to pick one field. Holland’s RIASEC schema can help to make the process of exploring and selecting options less daunting. This section describes some ways this might happen.
First, when students understand the basic elements of RIASEC theory they are armed with a schema for categorizing a great amount of academic information. Table 1 illustrates the operation of this schema in practical terms. Students intent on pursuing a bachelor’s degree can be informed that most college fields of study or disciplines are concentrated in Holland’s Investigative, Artistic, Social, and Enterprising areas (Smart et al., 2000), which reduces hundreds of options to four areas.
Second, the research by Smart et al. (2000) of bachelor’s programs was based on the idea that “faculty create academic environments inclined to require, reinforce, and reward the distinctive patterns of abilities and interests of students in a manner consistent with Holland’s theory” (p. 96). Moreover, “students are not passive participants in the search for academic majors and careers; rather, they actively search for and select academic environments that encourage them to develop further their characteristic interests and abilities and to enter (and be successful in) their chosen career fields” (p. 52). This is an important idea because it puts the power of informed choice in the hands of students as they explore educational options. They can actively select the type of environment in which they desire to spend their time and in which they wish to learn while in college.
Third, Smart et al. (2000) described primary and secondary recruits entering bachelor’s level academic programs. Primary recruits were freshmen entering disciplines directly from secondary school (discussed in this section) and secondary recruits (discussed in the next section) were those who changed their minds after entering college. Based on their research, Smart et al. found that two-thirds of freshmen (primary recruits) initially selected majors in the Social area and remained in that area over four years, while only slightly more than half of the students in the Enterprising area persisted in that area over four years. Students in the Artistic and Investigative areas both persisted over four years at 64%. Overall, about two-thirds of freshmen (primary recruits) persisted in one of the four disciplines initially selected and about 30% changed to another area.
The information gleaned from research by Smart and his colleagues of bachelor’s level programs can help inoculate students for relief of some of the anxiety regarding the selection of an academic program. Rather than simply focusing on the occupations related to a major in making a choice, students can focus on the nature and characteristics of the IASE environments and prioritize them according to their goals, interests, values, and skills. These understandings would also help students search for information about academic programs that provide details about whether or not the way life in the program is consistent or inconsistent with the theoretical RIASEC environment characteristics, e.g., student relationships with professors, classroom activities, nature of learning projects, leadership styles favored.
Adjusting to the College or Academic Program
Faculty in IASE disciplines create specialized academic environments that are shared by the students selecting these majors. The variability in the socialization styles and the effects of the environments on student behaviors and thinking were described by Smart et al. (2000) and are summarized below. Increased understanding of these environmental characteristics is important in educational counseling and for student decisions about preferred fields of study.
Faculty in Investigative environments place primary attention on developing analytical, mathematical, and scientific competencies, with little attention given to character and career development. They rely more than other faculty on formal and structured teaching and learning, they are subject-matter centered, and they have specific course requirements. They focus on examinations and grades. This environment has the highest percentage of primary recruits (e.g., students select it as freshmen).
Faculty in Artistic environments focus on aesthetics and with an emphasis on emotions, sensations, and the mind. The curriculum stresses learning about literature and the arts, as well as becoming a creative thinker. Faculty also emphasize character development, along with student freedom and independence in learning.
Varied instructional strategies are used in these disciplines.
Faculty in Social environments have a strong community orientation characterized by friendliness and warmth. Like the Artistic environment, faculty place value on developing a historical perspective of the field and an emphasis on student values and character development. Unlike the Artistic environment, faculty also place value on humanitarian, teaching, and interpersonal competencies. Colleagueship and student independence and freedom are supported, and informal small group teaching is employed.
The Enterprising environment has a strong orientation to career preparation and status acquisition. Faculty focus on leadership development, the development and use of social power to attain career goals, and striving for common indicators of organizational and career success. Teaching strategies in this environment are very balanced, but faculty like most to work with career-oriented students regarding specialized issues related to organizational and individual achievement.
Once an academic program is selected as a major field of study and the student begins to interact with other students and faculty in the program, more information of a personal nature is acquired which can lead to adjustments that the student will need to make to excel in that environment. For example, when Smart et al. (2000) examined college environments (the percentage of seniors in each of the IASE areas), they found that from 30–50% of the four environments were composed of primary recruits and about half were secondary recruits, e.g., the seniors who had changed their majors. This means that almost half the seniors ended up in an IASE discipline that was different from their initial choice.
Students migrated to and from the four environments in different ways. For example, two-thirds of the seniors in the Artistic environment were secondary recruits from one of the other areas; they did not intend to major in the Artistic area in their freshman year. In addition, about one third of the students migrating into the Social area came from Investigative, Enterprising, or undecided areas. Stated another way, the Social environments appear to be the most accepting and least demanding of the four environments studied by Smart et al. (2000) and Social disciplines seem to have the least impact and the least gains in related interests and abilities. Students moving into the Investigative area were most likely to come from the Enterprising area, and vice versa.
These findings (Smart et al., 2000) reveal the fluid nature of students’ major selections and the heterogeneous nature of the four environments with respect to the students’ initial major preferences. They also provide information regarding the migration of students among the IASE disciplines, and this can inform educational planning for students and counselors about the way in which these four disciplines interact with different types of students.
In summary, Smart et al. (2000) found that congruent students in Investigative, Artistic, and Enterprising environments increased their pattern of self-reported interests and abilities over four years by further developing what was already present in their personality. These three environments also increased the related traits for incongruent students, but the gap between the congruent and incongruent students did not decrease over time. In other words, students in both congruent and incongruent environments made equivalent or parallel changes in self-reported abilities and interests over four years, but students in congruent environments had higher levels of interests and abilities at the end of four years. Investigative and Enterprising environments had the most impact on student characteristics. These findings, if communicated to students in educational counseling, could affect the nature of discussions about students’ educational goals in college.
Assessing Academic Performance
Early in his career, Holland (1957) began to discuss the impact of college on students and how varied personality traits and beliefs other than aptitude were associated with success. Gottfredson (1999) noted that Holland’s early research demonstrated that much of the output from the college experience was related to what students brought into that experience. According to Gottfredson, Holland promoted the idea that college selection practices relying heavily on measures of academic potential resulted in much lost talent, e.g., selection of the top 10% of high school students based only on grades would exclude about 86% of high school class presidents (Enterprising types). The idea that noncognitive traits (e.g., RIASEC personality types) would be important in assessing academic performance is a noteworthy contribution of Holland’s theorizing and research.
Academic success is sometimes measured in terms of persistence on the part of the student or retention on the part of the institution. Other immediate outcome measures might include the grade point average, student satisfaction, awards received, or engagement in program activities, while longer term outcomes might include professional accomplishments, contributions, and recognitions. It should be noted that while all academic programs require cognitive skill and ability, some programs further emphasize interests and abilities related to the RIASEC areas identified in Table 1. These could include creativity, leadership, community service, and the like.
According to RIASEC theory, students in an environment that is highly congruent or matches with their personality will persist in that environment and achieve awards and recognition from the environment. In the process of educational counseling, students should have opportunities to clarify what it means to be in, or move to or out of, an environment that either matches their type or provides an opportunity to develop desired skills and interests. Their achievements and satisfaction would theoretically be related to the quality of the match between their personality and the environmental characteristics.
Connecting Education to Career and Life
Holland’s RIASEC theory provides a relatively simple, effective scheme for thinking about people (e.g., personalities, traits, interests, values, behaviors, attitudes) and their options (e.g., educational programs, occupations, work organizations, leisure activities). Conceptualizing people and options in these six areas can improve personal and career decision making.
Several examples of this strategy are apparent. For example, when students conduct information interviews they might structure questions and make observations about the degree to which the various RIASEC codes are prevalent in the life of the interviewee or characterize the organizational setting. In considering job offers, students might use the RIASEC schema to assess the quality of the fit between their personality and the culture of the organization, or more particularly, the personality of their immediate supervisor.
The UMaps project at the University of Maryland is a good example of applying RIASEC theory to life/career options (Jacoby, Rue, & Allen, 1984). The UMaps program operated out of the Office of Commuter Affairs in the Division of Student Affairs and was designed to help students become aware of diverse campus opportunities, options, and resources related to RIASEC types. Using both large posters displayed on bulletin boards and brochures distributed by advisors, each of the six RIASEC UMaps had a standard layout including areas of study (with office locations and phone numbers), sample career possibilities, internship and volunteer options, and student organizations and activities related to each type. Each map also had a brief description of the RIASEC type and a brief self-assessment related to interests and skills.
As reported earlier, Reardon, Lenz, and Strausberger (1996) used an earlier version of the Educational Opportunities Finder (Rosen et al., 1997) to classify all of the majors at a large university, and then used these data to assess the types of students seeking services in the career center and to design appropriate interventions. For example, it was judged that Realistic and Investigative students might prefer independent career planning using a computer-assisted guidance system, e.g., Choices Planner, rather than an individual counseling session.
Descriptive information about college majors could include the kinds of information summarized by Smart et al. (2000) about course structures, learning style expectations, faculty interests and activities, and program objectives. Other student information materials could list volunteer experiences related to the discipline (if any), introductory classes, sample employment opportunities, and profiles of graduates. Brochures and other descriptive information used in academic advising and educational counseling could be indexed or include information about Holland codes. These examples illustrate the ways in which RIASEC theory applied in educational counseling might be extended to broader life and career decisions.
Summary and Implications
This article illustrates how the educational counseling function has become estranged or lost in traditional counseling practice in secondary and postsecondary settings. While educational counseling can be viewed as distinctive from mental health counseling and/or career counseling, modern career theories can inform the practice of educational counseling for the benefit of students and schools. Holland’s RIASEC career theory, especially the extensive research on educational environments conducted by Smart and his associates (2000) and reported in more than six different journals, was used to illustrate this idea.
Educational counselors using RIASEC theory need to be fully informed about the theory, the research that supports it, the instruments that are based upon it, and the counseling techniques that could be derived from it. Such theory-driven practice might represent a new paradigm in educational counseling. Holland’s (1997) theory, like other career theories, has the most power when the extremes of wealth, social class, genetic traits, and health are not in effect. In other words, career theory probably works best in educational counseling for students in general rather than those at the extremes of any personal trait or situation.
RIASEC theory can be useful in educational counseling by specifying the kinds of conditions and traits associated with difficulties in educational decision making. Authors (1998, 1999) and Holland, Gottfredson, and Nafziger (1975) indicated that persons with poor diagnostic signs on the Self-Directed Search, e.g., lack of congruence between expressed and assessed summary codes, low differentiation, low consistency, low coherence among aspirations, low profile elevation, and a high point code in the Realistic or Conventional area, were likely candidates for more intensive counseling interventions. This is a special province of educational counselors because of their professional counselor training as opposed to the standard training for academic advisors or coaches. Students with high Artistic codes also may be problematic because of their preference for a non-rational approach to decision making (Holland et al., 1975). Persons with such diagnostic signs will likely need more time and professional, individualized counselor assistance in career problem solving and decision making.
Smart et al.’s (2000) research reveals some of the variations in academic departments and suggests implications for college and university organizational systems. It is important for counselors and other staff to inform students about the impact of majors and academic disciplines on the development of student interests and skills. At present, advisors make students aware of many aspects of a major, e.g., required courses, prerequisites, entrance requirements, and the occupations most closely aligned with the major. Providing additional information based on the research findings by Smart et al. regarding the way academic environments socialize or affect students pursuing that major will make students better “consumers” of majors or “shoppers” of academic programs.
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Robert C. Reardon, NCC, is Professor Emeritus and Sara C. Bertoch, NCC, is a career advisor, both at the Career Center at Florida State University. Correspondence can be addressed to Robert C. Reardon, Florida State University Career Center,
PO Box 3064162, Tallahassee, FL, 32306, rreardon@fsu.edu.
Sep 4, 2014 | Article, Volume 1 - Issue 1
John E. Mabey
Consideration of older adult lesbian, gay, bisexual, and transgender (LGBT) persons in gerontological research is lacking, leaving professional counselors without a substantive bridge with which to connect resources with treatment planning when working with sexual minorities. Therefore, presented here is an overview of aging research related to older adult LGBT individuals. The importance of individuality among LGBT individuals and suggestions for professional counselors who work with both individuals and couples in these populations also are presented.
Keywords: LGBT, older adults, gerontology, aging research, individuality
Multidisciplinary in nature, gerontology encompasses the study of dynamic processes of aging as experienced on the social, psychological, and biological levels (Hooyman & Kiyak, 2008). Knowledge of gerontology therefore enables professional counselors to work more effectively with older clients by facilitating understanding of their worldview. Professional counselors thus are better able to contextualize how aging itself is not the pathology, but rather the context that influences other aspects of the client’s life.
Due to advances in medical care and quality of life, the average lifespan in the U.S. is being prolonged and the percentage of those reaching old age is increasing dramatically (Dobrof, 2001). According to recent U.S. Census data (2008), the number of Americans aged 85 years and older will increase from 5.4 million in 2008 to 19 million by the year 2050. In addition, about 1 in 5 U.S. residents will be age 65 or older by 2030. It is not uncommon in professional literature and research to differentiate old age into categories, such as the young old, typically between 60 to 79, and the old old, typically 80 and above, to capture more accurate developmental data at different stages of the life cycle (Grossman, 2008; McFarland & Sanders, 2003; Quam, 1993; Quam, 2004; Quam & Whitford, 2007). Although relatively arbitrary, such categories do point to the fact that there are developmental differences even among older adults.
Older adult sexual minorities have been relatively ignored in gerontological research (Apuzzo, 2001; Cook-Daniels, 1997; Grossman, 2008; Kimmel, 1979; Orel, 2004; Quam, 2004). It is estimated that there are between 1 and 3 million individuals in the U.S. over age 65 who identify as lesbian, gay, bisexual, or transgender (LGBT) (Jackson, Johnson, & Roberts, 2008; McFarland & Sanders, 2003), and that number is expected to increase substantially in the next 15 years (Penn, 2004). Unfortunately, whether because of discriminatory bias against LGBT individuals or the invisibility of sexual identity within older adult populations in the larger society, most professional counselors find themselves lacking in general knowledge about this growing population and therefore ill-equipped to provide professional services for them.
Older adults, whether heterosexual or part of the LGBT community, confront many concerns about aging, including financial matters, health, companionship, independence (Quam & Whitford, 1992), loss, and residence concerns (MetLife, 2006). All older adults also face issues and stereotypes surrounding ageism (Wright & Canetto, 2009), including discriminatory attitudes and behaviors against older persons (Hooyman & Kiyak, 2008). However, ageism as experienced in LGBT communities has the additional impact of making a stigmatized group feel even more of a minority (Brown, Alley, Sarosy, Quarto, & Cook, 2001; Drumm, 2005; Jones, 2001; Jones & Pugh, 2005; Kimmel, Rose, Orel, & Greene, 2006; Meris, 2001) .
Additional concerns unique to older adult LGBT individuals include the ability to make legal decisions for each other as couples/partners, lack of support from family who might not recognize or respect their sexuality, and homophobic discrimination in healthcare and other services. Older adult LGBT persons often face unparalleled discrimination and harassment in residential care facilities (Johnson, Jackson, Arnette, & Koffman, 2005; Phillips & Marks, 2008). While elder abuse is recognized as a significant problem among older adults in general, unfortunately there is a deficiency of specific knowledge about abuse for older adult LGBT persons (Moore, 2000). Thus, in the vast majority of situations, mainstream services for older adults are not meeting the specific and unique needs of the older adult LGBT population (Slusher, Mayer, & Dunkle, 1996).
Older adult LGBT individuals have lived through distinctively oppressive social climates for sexual minorities compared to more recent generations. Their early developmental years were marked by a typically homophobic culture in which homosexuality was overtly and profoundly admonished, and included messages from national and local leaders that their sexuality was immoral, pathological, and often illegal. For example, the old old grew up in an era during which President Eisenhower ordered all homosexuals to be fired from government jobs and Senator McCarthy sought to ‘expose’ communists and homosexuals (Kimmel, 2002). Without a more organized movement in place in that era to combat the rampant homophobia and negative stereotyping, blatant fear and dislike of homosexuality was seen in nearly all political, educational, and religious institutions. Indeed, the general lack of support for LGBT individuals in religious institutions continues today, leaving many in the position of a forced choice between two fundamental components of their sense of self: spirituality and sexuality. “In turn, this conflict can manifest itself through internalized disorders, such as depression, or through externalized disorders, such as risky or suicidal behavior” (Mabey, 2007, p. 226). However, it is important for professional counselors to be aware of the distinction many older adult LGBT persons make between spirituality and religiosity; religious dogma against homosexuality does not prevent many LGBT individuals from maintaining a strong spiritual identity (Mabey, 2007; Orel, 2004).
The young old, though, became adults during a time of more relatively progressive changes in society. The Stonewall riots in Greenwich Village in 1969, in which gay and transgender individuals physically fought back against unjust police harassment, marked a milestone in what would eventually become the modern gay rights movement. In the mid-1970s, homosexuality was finally declassified as a mental disorder within both the American psychiatric and psychological professional communities (but only after decades of miseducating medical and mental health professionals about the pathologic nature of sexual minorities).
As professional counselors work with an aging LGBT population, it is important to consider this historically negative climate which shaped an individual’s experiences with, and impressions of, her or his own sexual identity (Berger, 1982). For the older adult LGBT individual, consequently, there might exist a sense of internalized homophobia (D’Augelli, Grossman, Hershberger, & O’Connell, 2001; Heaphy, 2007; Porter, Russell, & Sullivan, 2004) that contributes to nonparticipation in LGBT-supportive services and associated diminished overall mental health. These individuals also are less likely to seek any general health services for fear of having to disclose their sexual orientation to a possibly homophobic provider (Brotman, Ryan, & Cormier, R., 2003; Grossman, D’Augelli, & Dragowski, 2007; Sussman-Skalka, 2001). For example, refer to Zodikoff (2006) for vignettes that highlight unique aspects of social work practice with a diverse and aging LGBT population.
Aging and Individuality
Professional counselors should recognize that an older adult LGBT individual does not belong to one homogenous group within the LGBT acronym. For example, a gay youth living in New York City at the time of the Stonewall Riots will have experienced the movement in vastly different ways than, say, a gay youth then living in the rural Midwest. Similarly, a transgender individual involved in the Stonewall Riots will have faced different experiences than a gay male in those same riots because of the greater concealment of transgender individuals. Cook-Daniels (1997) wrote, “Lesbian and Gay male elders have been called an ‘invisible’ population (Cruikshank, 1991). If they are invisible, then transgendered elders have been inconceivable” (p. 35).
Transgender older adults also face unique challenges apart from those who are lesbian, gay, or bisexual (Cook-Daniels, 2006). For example, health concerns for those transitioning from male to female (MTF) or female to male (FTM) are greater because surgeries become more complicated with age. However, there has been a significant increase in the number of those willing to face the risk of transitioning in later life because of vastly improved methods of electronic communication about options, new research, and medical procedures (Cook-Daniels, 2006).
Another challenge to older adult transgender individuals is that most older adults in society, including gay and lesbian older adults, have well-established social roles and relationships. Thus, MTF or FTM transitioning becomes more difficult with age because of the need for changed manners of speech and gesticulations. Legal issues include additional unique challenges as a change in gender is often associated with changed governmental benefits. For example, a formerly heterosexual marriage might be seen as an illegal same-sex marriage after one spouse transitions, and then formerly anticipated benefits, such as Social Security, might be revoked.
As professional counselors work with the older adult transgender population, there are several important aspects about this community to be considered in treatment planning (Cook-Daniels, 2006). First, although transphobia in the medical community and healthcare facilities has not been adequately researched, it is well-documented (Donovan, 2001). Therefore, making effective referrals necessitates that the new service provider be familiar and comfortable with the transgender population. Professional counselors also should understand the roadmap for individuals who are transitioning, and in particular how they need to be declared mentally fit as well as diagnosed with Gender Identity Disorder before any treatment for transitioning may commence. Professional counselors also should understand that persons in MTF or FTM are often perceived to be, “…mentally ill until proven otherwise, and they are fearful and angry that—to a degree that is rivaled perhaps only by prisoners and the severely domestically abused—their life choices are under someone else’s control” (Cook-Daniels, 2006, p. 25). To the extent that a transgender person holds this perspective, it might interfere with his or her level of comfort in seeking the services of a mental health professional at all.
Transgendered individuals also cannot control the coming-out process of their gender identity because visual or auditory cues may expose their status, and therefore they are left open to the opinions and reactions of others they encounter. Thus, it is important for professional counselors to assess their own comfort levels, and meeting transgender individuals or volunteering in an organization that serves this population is a great way to increase familiarity with and knowledge about this group. It also is important to recognize that transgendered individuals face financial constraints that are usually greater than those typically encountered by other gay, lesbian, or bisexual elders due to hormone medication or surgical procedures that are usually not covered by insurance. Therefore, as with other clients experiencing financial constraints, professional counselors might employ a sliding-fee scale depending on their client’s stage of transition and/or individual circumstances.
Bisexual individuals also experience a sense of invisibility within the LGBT community. As another underrepresented group in professional research literature, the needs and experiences of bisexual older adults also are often misunderstood. Professional counselors likely will work with bisexual clients during their careers, and should approach treatment without the erroneous assumption that sexuality is necessarily dichotomous (Dworkin, 2006).
Ageism typically precludes recognizing the sexuality of older adults (Hooyman & Kiyak, 2008). However, it is an important element. Consider a professional counselor who meets an older adult client who is happily married to a member of the opposite sex. That counselor likely will not consider that the client may in fact be bisexual—but it may be the case. Indeed, coming out as bisexual during a heretofore heterosexual marriage is the point at which a professional counselor might most be needed as issues of intimacy and restructuring of familial dynamics are addressed.
There also is the myth of the impossibility of monogamous relationships for bisexual individuals that should be considered by professional counselors (Dworkin, 2006). Simply because a person has the capacity for attraction and/or commitment to both males and females does not mean that the individual is unfulfilled with a monogamous relationship or that polyamorous relationships are necessarily seen as negative.
Aging Research and Identity
Differences among individuals within the “LGBT” acronym highlight the necessity for a professional counselor to understand the complex nature of identity. Through a shared history, current activism, and support networks, individuals within the LGBT community have much in common with one another. However, they also have differences. In building rapport with an older adult client, a professional counselor should recognize these differences (beyond commonly understood stereotypes). For an older adult LGBT client, having a well-informed professional counselor is essential to relationship-building and establishing trust, i.e., a comfortable environment in which LGBT history can be addressed and acknowledged.
Comprised of persons of every nationality, socioeconomic status, gender, ability level, race and ethnicity, the older adult LGBT population cannot be grouped or treated as one cohesive category. Unfortunately, research about LGBT elders is still underrepresented in gerontological literature, and representative samples of populations within that body of research are even more limited (Berger & Kelly, 2001; Butler, 2006; Grossman, D’Augelli & Hershberger, 2000; Jackson, et al., 2008; Kimmel, 2002; Quam & Whitford, 1992). Indeed, because of a variety of factors, such as “closeted” older adults and the lack of organized LGBT communities in some areas, no economically feasible method is available to generate a random sample of older LGB(T) individuals (Grossman, et al., 2000). Professional counselors must also consider this limitation when reviewing research, and how a significant number of studies have been conducted with LGBT individuals with limited sample sizes (and who primarily were Caucasian, highly educated, affluent, self-identified, younger, male individuals living in urban areas) (Dworkin, 2006; Grossman, D’Augelli, & O’Connell, 2001; Hash, 2006; McFarland, & Sanders, 2003; Porter, et al., 2004). Within the professional research and literature on older adult LGBT individuals, there exists a substantial gap in representation of people of color, the old old, and those living in rural areas.
Professional counselors should inquire of each older adult LGBT client about level of identification with an LGBT identity or community. Indeed, a professional counselor may be better educated about LGBT history and circumstances than the client, and therefore may be able to facilitate the older adult LGBT client’s identity development. Indeed, it is rare for an older adult LGBT individual to have had LGBT parents, and therefore they are not necessarily taught this cultural history or coping strategies for overcoming homophobia, biphobia, or transphobia in the traditional family setting. Regardless, the ability of a professional counselor to access such information during a session is an important skill for relationship-building and even for educating the client regarding homework or making referrals.
As professional counselors consider the impact of an LGBT identity for the older adult individual, it also is important to not view that identity as necessarily problematic (Berger, 1982). In fact, researchers point to the idea of “crisis competence,” in which the coming-out process enables the individual to develop a competency for dealing with other crises in the lifespan, including difficulties associated with the adjustment to aging (Heaphy, 2007; Kimmel, 2002; McFarland & Sanders, 2003; MetLife, 2006; Morrow, 2001; Quam, 1993).
Additional Skills for Professional Counselors
Sometimes an older adult individual in the LGBT community has difficulty coping with the stressors of homophobia and coming-out, and professional counselors might witness psychological distress or unhealthy behaviors. Kimmel (2002) outlines suggestions that can be adapted by mental health professionals to enhance the development of crisis competency and combat maladaptive thoughts and behaviors with this population. The suggestions include to:
• Aid the client to discover any familial or peer support.
• Identify positive role models locally or nationally that embody characteristics to which the client would aspire.
• Practice the use of effective coping skills.
• Assist in managing the integration of their multiple identities to enhance their sense of self.
Because the number of older adult individuals in the U.S. is expected to increase dramatically in the next 20 to 50 years, the number of older adult LGBT individuals will continue to grow as well. Professional counselors, working with these often misunderstood populations, face the additional challenge of treating LGBT elders with limited research or experience. Quam, Knochel, Dziengel, and Whitford, (2008) offer practical suggestions for working with same-sex couples that are adapted for work with older adult LGBT individuals:
• Your older adult client may define “family” as close friends who have assumed the role of absent families of origin. These fictive kin must be treated with the same respect as other family members.
• Because of anti-LGBT attitudes, your older adult client’s biological or adoptive family may not be providing elder care. This care might instead be provided by fictive kin or not at all.
• Your older adult client might also be a caregiver for another elderly individual, especially as fictive kin play an important role in LGBT communities and caregiving.
• Your older adult client may have biological or adoptive children.
• Be knowledgeable about legal protections such as a will, power of attorney and a health care directive, as there are limited benefits for same sex couples (being denied visitation rights in a hospital when their partner is injured or gravely ill is a possibility).
• Confidentiality is essential when working with an older adult LGBT individual, specifically because of realistic fears about anti-LGBT attitudes in the medical field or treatment facilities. Therefore, disclosing your client’s sexual orientation without permission, even to another LGBT individual, should be strictly avoided.
• Familiarize yourself with older adult LGBT services and communities. An example is SAGE (Services and Advocacy for Gay, Lesbian, Bisexual, and Transgender Elders), a comprehensive social service agency with chapters across the country (http://www.sageusa.org).
As professional counselors continue to balance a scholar-practitioner role, increased research and experience with LGBT older adults and their aging will promote and elevate the counseling profession. It also will serve to enrich the lives of millions of LGBT older adults and their supporters. Both historically and in contemporary times, the counseling profession thrives as a fertile ground for pioneering and ground-breaking research; LGBT aging represents a generally underexplored but vital new challenge. Indeed, the dynamic and diverse nature of older adult LGBT communities provides opportunity for expanding academic inquiry and new and innovative treatment modalities in the counseling profession.
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Wright, S. L., & Canetto, S. S. (2009). Stereotypes of older lesbians and gay men. Educational Gerontology, 35, 424–452.
Zodikoff, B. D. (2006). Services for lesbian, gay, bisexual, and transgender older adults. In B. Berkman & S. D’Ambruoso (Eds.), Handbook of social work in health and aging (pp. 569–575). New York, NY: Oxford University Press.
John E. Mabey, NCC, is Editor and Facilitator at University-Community Partnership for Social Action Research Network (UCP-SARnet). Correspondence can be addressed to John E. Mabey, University-Community Partnership for Social Action Research Network, Arizona State University, P.O. Box 871104, Tempe, AZ, 85287, johnmabeyadvisor@hotmail.com.
Sep 3, 2014 | Article, Volume 1 - Issue 3
Cody J. Sanders
Given the increasing popularity of narrative and collaborative therapies, this article undertakes an examination of the postmodern theory underlying these therapies. This consideration gives particular attention to issues of power and knowledge in therapeutic practice, examined both within clients’ narratives and within the therapeutic alliance. Implications on the role of counselors in recognizing and addressing power within clinical practice is likewise detailed.
Keywords: collaborative therapies, postmodern theory, narrative, knowledge, power, stories
Knowledge and power, terms used frequently in everyday vernacular, carry particular nuanced meaning and significant weight in discussions of postmodern therapies. Narrative therapies, in particular, bear the marks of significant shaping by notions of knowledge and power that are given particular form through a process of postmodern critique. While narrative therapy and other collaborative-based postmodern therapies have much to offer in the way of method for counseling practice, one would miss the significance of methodological structure without first understanding the philosophical underpinnings. While postmodern thought is often referred to in a unified manner, it is important to note that postmodern influences on therapy do not stem from a unified system or philosophy called “postmodernism.” Instead, postmodern influences may be most clearly articulated as a critique of the assumptions of modernism. Modernist thought can be traced throughout the foundations of the psychotherapeutic theories and modalities that have dominated the field from Freud to the present. While narrative therapy certainly runs counter to many modernist assumptions in counseling, it is sometimes difficult to see the significance of postmodern influences without first illustrating how they provide a critique to modernist assumptions. As McNamee (1996) states, “We often do not recognize the mark of modernism because it has inscribed itself on our way of living” (p. 121). Thus, it is necessary at the outset of this exploration to provide a contrast between modern and postmodern thought as it relates to the field of counseling in order to more fully articulate the importance of the concepts of knowledge and power in the theory and practice of narrative therapy.
Modern and Postmodern Thought in Counseling
“The pursuit of truth over meaning as humankind’s highest achievement,” as Parry and Doan (1994) characterize the modern turn in history, “probably began with Plato” (p. 2). In this pursuit of truth, modernist thought conceives of knowledge as pointing to or representing an objective world that exists independently of the mind and feelings of the individual. In this framework in which knowledge is attained through the process of observation and verification, “the knower is autonomous and separate from that which he or she observes, describes, and explains” (Anderson, 1997, p. 30). From this perspective, Anderson (1997) pictures modern knowledge as a pyramidal structure with a hierarchy of truth. Barbara Held (1995), a critic of postmodernist influences in counseling, characterizes modernism and postmodernism in terms of realist/antirealist divide. “The realist doctrine,” she holds, “states that the knower can attain knowledge of an independent reality—that is, reality that is objective in the sense that it does not originate in the knower, or knowing subject” (Held, 1995, p. 4).
Michael White and David Epston (1990), proponents of the turn to postmodern thought in counseling, argue that social scientists turned very early on to the positivist physical sciences for examples upon which to base their own work in the interpretation of the social systems. This, they believed, would provide necessary legitimacy for their own work as a science. This positivist commitment leads counselors into the process of observing, describing and explaining human behavior in ways that are deemed objective and places them in the position of master observer describing and assessing the client’s story as it “really is and ought to be” (Anderson, 1997, p. 31). According to Anderson (1997), this places both counselor and client on the hierarchy of knowledge and truth, marking the client as the subject of inquiry and observation and the counselor as the superior expert observer. In this modern construction of the therapeutic alliance, counselor and client are “separate static entities…and not interactive participants in a joint endeavor” (Anderson, 1997, p. 32). Furthermore, the language often used to describe the client’s reality is a deficiency-based language assumed to be an accurate representation of behavioral and mental reality.
It is a turn away from this pursuit of a hierarchy of knowledge and the positivist search for objective reality that is at the heart of narrative therapy’s conceptualization of knowledge and power. What narrative, collaborative-based and nearly all postmodern models of therapy hold in common is the belief that an objective reality that stands apart from the knowing subject can never be fully arrived upon. Simon (1994) postulates, “The first, and perhaps defining attitude of postmodernism is the belief that knowledge is power and hidden concepts may exist in a theory or text that justifies the use of power” (p. 2). Narrative theorists are indebted to Michel Foucault, notes Catrina Brown (2007), when it comes to Foucault’s “insistence on the inseparability of power and knowledge and his efforts to study the way humans govern and regulate themselves and others through the production of truth” (p. 3). By way of Foucault and a myriad of other philosophers who influenced the postmodern critique of modernism, narrative theorists see through “a postmodern lens” in which knowledge is not hierarchical, objective or observable by an expert; rather it is “multiple and only ever partial” and “understood to be socially and historically specific and inseparable from social relations of power” (Brown, 2007, p. 5).
Foucault (1977/1994) attempts to trace how hierarchies of power are constructed in modernist thought and to uncover their effects on individuals. He sees the process of power relations taking place when modes of inquiry try to give themselves the status of sciences through objectivizing ways of approaching subjects under study. Secondly, Foucault (1982/1994) points to “dividing practices” within society and scientific disciplines that seek to divide the subject inside him- or herself or from others. Finally, he points to the ways in which individual human beings are made into subjects through these practices. In examining the power relations at work in this process, he states:
This form of power that applies itself to immediate everyday life categorizes the individual, marks him by his own individuality, attaches him to his own identity, imposes a law of truth on him that he must recognize and others have to recognize in him. It is a form of power that makes individuals subjects. There are two meanings of the word “subject”: subject to someone else by control and dependency, and tied to his own identity by a conscience or self-knowledge. Both meanings suggest a form of power that subjugates and makes subject to. (Foucault, 1982/ 1994, p. 331)
This understanding of power and knowledge put forth by Foucault is not the repressive power of force so commonly spoken of in everyday uses of the term “power.” Rather, as White and Epston (1990) point out, “Foucault argues that we predominantly experience the positive or constitutive effects of power, that we are subject to power through normalizing ‘truths’ that shape our lives and relationships” (p. 19). This power makes us into subjects by delimiting the ways in which we are able to conceive of our identities; it provides the language with which we determine the content of our self-knowledge and self-concepts. Foucault (1977/1997) argues that we must cease describing power in negative, repressive terms and instead see that “it ‘excludes,’ it ‘represses,’ it ‘censors,’ it ‘abstracts,’ it ‘masks,’ it ‘conceals.’ In fact,” says Foucault, “power produces; it produces reality; it produces domains of objects and rituals of truth” (p. 194).
White and Epston (1990) explain that Foucault is postulating ideas about human beings that claim the status of objective truth are, in fact, constructed ideas that are given the status of truth. These “truths” are then used as the norms around which persons shape or constitute their lives, thus making them subjects. White and Epston (1990) further state that this subjugating knowledge “forges persons as ‘docile bodies’ and conscripts them into activities that support the proliferation of ‘global’ and ‘unitary’ knowledges, as well as the techniques of power” (p. 20). Thus, power as Foucault describes it is inseparable from knowledge and is sometimes represented in Foucault’s writing as “power/knowledge” or “knowledge/power.” This construction of power/knowledge in the work of Foucault and in postmodern thought in general prompts Neimeyer (1995) to claim that the modern notion of an existential self—“an individual ego who is the locus of choice, action, and rational self-appraisal” (p. 13)—no longer carries any weight in a landscape influenced by postmodernism.
Holzman, Newman, and Strong (2004) argue that some counselors and theorists have attempted to avoid the topic of power altogether, believing it to be the fundamental flaw of modernism. Postmodern theories such as narrative therapy, however, cannot simply eliminate power by denying its presence. Anderson (1997) views narrative, collaborative-based, postmodern therapies taking account of power by recognizing within the therapeutic relationship how power evolves from interaction between the client and others (including the counselor) and through communication between persons. She states, “Knowledge, including self-knowledge or self-narrative, is a communal construction, a product of social exchange…From this perspective ideas, truths, or self-identities, for instance, are the products of human relationships. That is, everything is authored, or more precisely, multi-authored, in a community of persons and relationships” (Anderson, 1997, p. 41). The intended focus of the remainder of this exploration is to locate issues of power and knowledge within clients’ narratives and to examine how narrative therapy approaches these relations of power, as well as to examine the dynamics of power and knowledge that exist within the therapeutic alliance and the ways in which narrative counselors conceive of and deal with these relations of power.
Power and Knowledge in Clients’ Narratives
The telling of stories by clients is a key component of nearly every theory of counseling and psychotherapy. Whether one attends to the unconscious psychodynamics, the rationality of thought processes, the behavioral outcomes, or the affective dimensions of a client’s story largely depends upon one’s theoretical orientation. From a narrative perspective, Brown (2007) suggests that hearing clients’ narratives must move beyond a simple telling of clients’ stories “to an active deconstruction of oppressive and unhelpful discourses” (p. 3). As postmodern critique has demonstrated, the relationship between knowledge and power is inextricable and takes shape in the form of discursive systems in which individuals are divided, classified and come to author their lives through narrative components that are available within dominant discourse. Discourses, Brown (2007) reminds us, are both constituting—giving us language and concepts by which we come to know ourselves as subjects—and constraining—allowing some stories to be told and affirmed and others to be hidden and overlooked.
The process of deconstruction in therapy involves the analysis of these discourses and the “hidden element of power in all organized systems of knowledge” (Simon, 1994, p. 2), making relations of power clear and bringing them into the awareness of those involved with a system of knowledge. Bringing to awareness the hidden elements of power and the effects of discourse on the lives of clients does not have as its aim escape from relations of power. Indeed, escape from power relations is impossible. Awareness of these relations of power within systems of knowledge, however, allows for a fuller range of actions to be taken by the client and uncovers diverse narratives that are often subjugated or hidden by the normalizing effect of discourse. In this way, Winslade, Crocket, and Monk (1997) point to the significance of listening closely to a client’s story—so closely, one would presume, as to hear the hidden elements of power and the effects of discourse. They indicate that the act of listening calls for the power of the counselor to be concentrated toward the legitimization of the client’s voice where it has been previously excluded and denied. Foucault was closely attentive to the knowledges within society that are silenced due to the fact that the voices of some (inmates in prisons, patients in mental hospitals, etc.) are never given a hearing. He called these “subjugated knowledges” (Jardine, 2005, p. 21).
Indeed, power and knowledge in the practice of narrative therapy can be largely located within the narrative the client brings into the consultation room. Brown (2007) argues that the work of the narrative counselor is to unpack and reconstruct these stories, rather than leaving them intact, as these narratives so often reflect dominant discourses and relations of power. She further states that power can never be left out of the work of narrative therapy and that this particular approach is a highly politicized work that seeks to challenge oppression. Rather than inherent truths of early modernist therapies, narrative therapy is interested in the construction of stories. As a part of this interest in construction, narrative therapists are interested in the ways larger discourses—often presumed to be truth—are taken up by clients as formative in their own narratives. Culturally available discourses that shape and form clients’ narratives in various ways are often the sites of the deconstructive work of narrative counselors.
White and Epston (1990) describe the therapeutic methodology of narrative therapy with attention to how many of the techniques accomplish this deconstructive and reconstructive work around issues of power and knowledge. They describe mapping the influence of the problem as a way to expose unitary knowledge through an exploration of beliefs a client holds about him or herself, others and their relationships. Through the process of externalization, the client gains a new perspective of the problem and his or her own life and, accordingly, new options become available to challenge the “truths” that have previously impinged upon the narrative constructs. Exploring the effects of the problem allows the client to identify what might be necessary in order to survive the “problem story.” Finally, unique outcomes that are located by attending to times when the client was not subjected to the techniques of the problem narrative provide a further point of reflection upon the meaning of these times and how that meaning might emerge from subjugation to the dominant problem narrative.
While narrative therapy has well-articulated ways of addressing the dynamics and relations of power and knowledge in the client’s narrative, there remains the reality that the client and counselor are themselves caught up in relations of power. While narrative counselors attempt to deconstruct the power relations between counselor and client by conceiving of the client as expert, this leads to what Brown (2007) sees as the dangerous treatment of “experience” as uncontestable truth. Brown (2007) further explains that postmodern feminists caution against such a privileging of individual experience resulting in experience being separated from social construction. They further argue that clients’ stories should always be considered both social and political. The difficulty arises in determining how the counselor can serve in challenging the discursive realities within clients’ narratives without claiming expert knowledge for him or herself. Many counselors, including Anderson (1997), adopt a “not-knowing position” (p. 4) that consists of a distancing from strategies of power found in many traditional therapeutic theories. Brown (2007) is critical of this stance, however, stating that with a not-knowing position of the counselor, clients’ stories “escape the social processes that make knowledge and power inseparable. Seen somehow to be outside of the influence of power, these stories can be taken up as is, as self-legitimizing” (2007, p. 9). This not-knowing position can result in a focus on the individual and his or her story to the exclusion of the social context. If the social construction of clients’ narratives is left unexplored, Brown (2007) warns, “therapy participates in its reification of dominant and often unhelpful stories” (p. 9–10). While an all-knowing stance is certainly to be repudiated, the “‘not-knowing’ stance is not effective for challenging oppressive social discourses or, subsequently, for deconstructing negative identity conclusions or rewriting alternative identities” (Brown, 2007, p. 4). Brown (2007) resolves this difficulty by holding to modern ideas regarding the possibility of an emancipatory social vision, as well as the postmodern idea that knowledge is always partial, located and never neutral. Together, Brown (2007) sees this blend as helpful in taking a position regarding clients’ narratives without suggesting the position to be one of objectivity—at once being positioned while recognizing one’s partiality.
Power and Knowledge in the Therapeutic Alliance
While Brown’s (2007) position in relation to dynamics of power and knowledge in the client’s narrative is helpful, there are further questions that deserve exploration with regard to the relations of power and knowledge within the therapeutic alliance—the relationship between client and counselor. Simon (1994) notes that modernist ideas of rationality and objectivity allowed scientists to feign a transcendent, supracultural view of truth and reality. Consequently, a hierarchy was created in which the scientist—and counselor—was placed above those in society and in the consultation room as rational objective observer. The scientific viewpoint was privileged and as a result the scientist and counselor fell prey to the political, economic and social views of both the scientist and dominant discursive narratives. However, narrative counselors working from a postmodern viewpoint identify the problematic nature of scientific objectivity and recognize, as Mahoney (1995) states: “There is not, never was, and never can be a truly ‘nondirective’ or value-free form of human dialogue. All human perception, learning, knowing, and interaction is necessarily motivated by and permeated with biases, preferences, and valuations (which are usually implicit)” (p. 392).
Even while narrative counselors attempt to divest themselves of positions of power over the client seen in modernist psychotherapies, the issue of power and knowledge in the therapeutic alliance is still very present. Holzman, Newman, and Strong (2004) note that, if for no other reason, there is a dynamic of power and knowledge present in the fact that the client looks to the counselor for advice, solutions, interpretations, explanations or, in postmodern approaches, a collaborative process that may generate new understanding. This is no doubt still an appeal to the authority of the counselor and is an appeal that narrative counselors must creatively approach in order to attend to the implicit relations of power. While some would seek to divest the relationship of power altogether, this is a naïve and potentially harmful way of viewing power in the therapeutic alliance. As White and Epston (1990) warn:
If we accept that power and knowledge are inseparable…and if we accept that we are simultaneously undergoing the effects of power and exercising power over others, then we are unable to take a benign view of our own practices. Nor are we able simply to assume that our practices are primarily determined by our motives, or that we can avoid all participation in the field of power/knowledge through and examination of such personal motives. (p. 29)
Instead of an avoidance of the power and knowledge relations implicit in the therapeutic alliance, these authors suggest that narrative counselors must assume that we are always participating in such relations. Rather than avoiding this reality or trying to cover it over with a completely “not-knowing” position, White and Epston (1990) suggest that we critique our own practices and identify the contexts of ideas from which our practices come. This, they argue, enables the narrative counselor to identify effects, dangers and limitations in their ideas and practices and turns their attention toward the keen awareness that social control—though avoided—is always a strong possibility within the therapeutic alliance.
White and Epston (1990) further argue that if we are to “accept Foucault’s proposal that the techniques of power that ‘incite’ persons to constitute their lives through ‘truth’…, then, in joining with persons to challenge these practices, we also accept that we are inevitably engaged in a political activity” (p. 29). Foucault (1994b) explains the nature of truth that must be challenged through the political activity of the counselor in this way:
Truth is a thing of this world: it is produced only by virtue of multiple forms of constraint. And it induces regular effects of power. Each society has its regime of truth, its ‘general politics’ of truth—that is, the types of discourse it accepts and makes function as true; the mechanisms and instances that enable one to distinguish true and false statements; the means by which each is sanctioned; the techniques and procedures accorded value in the acquisition of truth; the status of those who are charged with saying what counts as true. (p. 131)
While White and Epston (1990) admit that the political activity of the narrative counselor does not involve proposing an alternative ideology to that implicit in the regime of truth, they do propose that the counselor challenges the techniques that subjugate clients to a dominant ideology. This involves what Brown (2007) argues is an acceptance of one’s position by which counselors acknowledge, rather than deny, their own knowledge and power and become more accountable for them.
What alternative exists, then, to the “not-knowing” position advocated for by many narrative and postmodern therapies? Brown (2007) notes that while a not-knowing position seeks to maximize clients’ power by positioning them as “expert,” “they often implicitly require practitioners to abdicate their own knowledge and power” in the process (p. 8). She further explains the problematic nature of the not-knowing position as one that (1) risks passivity and (2) involves little active problem solving or analysis on the counselor’s part. “In the first instance,” Brown (2007) argues, “expert knowledge and power, while practiced, are denied; and, in the second, the therapist is rendered virtually ineffective for fear of being too knowledgeable or too powerful” (p. 13). In a far more critical tone, Barbara Held (1995) posits that the antirealism within postmodern therapies “affords therapists…a legitimate way to diminish the discipline’s complexity, by diminishing if not eliminating what therapists need to know in advance of each case” (p. 14).
Brown (2007) argues that in order to practice effectively, the narrative counselor must have knowledge and power. The issue, then, is how knowledge and power are recognized and deployed in the therapeutic alliance. Winslade, Crocket, and Monk (1997) offer the image of coauthoring narratives within a collaborative relationship as a possibility for abandoning the all-knowing, but remaining free from the ineffective not-knowing position. Coauthoring, they postulate, “implies a shared responsibility for the shaping of the counseling conversation…[and] challenges the portrayal of counselors as followers, who must be very cautious about treading on the toes of clients” (Winslade, Crocket, & Monk, 1997, p. 55). At the same time, it challenges a modernist view of the counselor as a wise, all-knowing expert. In the collaborative relationship described by these authors, narrative counselors develop an awareness of aspects of professional discourse that set up harmful relations of power and authority and leave the client with little sense of agency. Instead, client and counselor take a position against the problems and deficit-inducing discourses and in this way create a relationship in which power is used in a positive manner in which the client has a voice that is offered legitimacy by the counselor’s hearing. Anderson (1997) further describes this collaborative therapeutic alliance as a partnership between people with different perspectives and expertise.
Even within a collaborative partnership, however, Mahoney (1995) argues that while clients show a degree of autonomy, this “does not negate the fact that clients’ values in some domains may be significantly influenced by the values expressed, affirmed and challenged by the professional practitioner” (p. 393). Taking note of this reality, Anderson (1997) posits that the focus in a collaborative approach to therapy is on the relationship system between client and counselor in which both the expertise of the client and that of the counselor combine and merge. While Anderson (1997) conceives of the respective domains of expertise as easily discernible—client as expert in his or her life experiences and counselor as expert in the area of the dialogical process—even this seems a false dichotomy. Whether or not the counselor is willing to recognize it, he or she cannot relinquish power over the domain of the client’s narrative. Even in the questions the counselor poses and those parts of the narrative that he or she chooses to attend to in detail, there exist relations of power that may serve to either reify or challenge the dominant discursive regimes of truth therein. Holding to a close Foucauldian understanding of power and knowledge, Brown (2007) notes that a narrative therapeutic stances must move away from the binary notion that one may either have power and knowledge or not. Instead, the counselor must be clear in recognizing that both the counselor and client are “active embodied subjects in the therapeutic process of coauthoring identities” (Brown, 2007, p. 3). Far from the objective neutrality of the modernist stance and the oversimplified not-knowing abdication of power in some postmodern approaches, Brown (2007) sees the necessity of being positioned and taking a stance as a vital in narrative therapy. “In my view,” she states, “it is far more dangerous to deny the presence of our own knowledge and power through efforts at sidestepping it” (Brown, 2007, p. 12). Above all, and despite the politics and goals of any particular therapeutic alliance, Brown (2007) states the unequivocal positioning of the counselor as one of ethical responsibility for the well-being of the client.
Conclusion
It is clear from this examination that questions of power and knowledge in clients’ narratives, as well as within the therapeutic alliance, are subjects of lively debate and clarity is not easily gained. Partially, this difficulty seems to stem from the reality that knowledge and power often operate in implicit ways within the regimes of truth that are so often taken for granted as normative ways of understanding. What seems clear, however, is that narrative counselors hoping to be true to postmodern conceptualizations of power and knowledge—at least Foucauldian understandings—must continue to recognize the relations of power and knowledge at play in the therapeutic alliance. Rather than attempting to divest oneself of power, one must instead recognize that relations of power are unavoidable and that the counselor is always positioned in relations of power with the client. What might be stated with some degree of certainty is that relations of power and knowledge are unavoidable and inescapable, even for those practicing narrative, collaborative-based, postmodern therapies. The determining factor for how power and knowledge will be experienced as constraining, constitutive, oppressive, liberative, limiting or emancipatory is the degree to which the counselor is willing to recognize her or his own involvement in relations of power and position herself or himself within those dynamics of power and knowledge, recognizing all the while that the act of therapy is, indeed, a political act.
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Cody J. Sanders, NCC, is a doctoral student at Brite Divinity School in Ft. Worth, Texas. Correspondence can be addressed to Cody J. Sanders, Brite Divinity School, Pastoral Care and Training Center, 2855 S University Drive, Fort Worth, TX, 76129, cody.j.sanders@gmail.com.
Sep 3, 2014 | Article, Volume 2 - Issue 1
W. P. Anderson Jr., Sandra I. Lopez-Baez
Little is known about levels of personal growth attributed by students to typical college life experiences. This paper documents two studies of student self-reported and posttraumatic growth and compares growth levels across populations. Both studies measure student attributions of cause to academic and non-academic experiences, respectively. It is suggested that future research on the outcome of college life experiences can use a similar approach with a variety of variables.
Keywords: college life, personal growth, posttraumatic growth, life experiences, attributions of cause, outcome of college
The results of a rapidly growing number of studies document high levels of adversarial growth attributed by survivors in retrospect to coping with negative life experiences (Linley & Joseph, 2004) including war, bereavement, or loss of a child. The Posttraumatic Growth Inventory (PTGI) by Tedeschi & Calhoun (1996) has been the most popular measure of adversarial growth to date (Joseph, Linley, & Harris, 2005; Linley & Joseph, 2004; Tedeschi & Calhoun, 2004). In this context, adversarial refers to the negative nature of an experience of interest to researchers. Growth refers to personal growth defined as the positive psychological changes described by assessment-instrument items, changes such as the building of interpersonal relationships, a greater appreciation of the value of life, and a realization of new possibilities in life (examples after Tedeschi & Calhoun, 1996). Thus, adversarial growth is personal growth attributed by participants to a naturalistic negative event of particular interest to researchers. We stumbled on the PTGI and the relationship between adversarial and personal growth while searching for an instrument for measuring personal growth attributed in retrospect to the sum of naturalistic experiences (both positive and negative) occurring during a time period of interest to researchers.
The growth of immediate interest to us is not adversarial, but rather the total personal growth of college students because our long-term goal is to determine how educators can best facilitate growth, whether adversarial or otherwise. Developmental theorists (Chickering, 1969; Chickering & Reiser, 1973; Pascarella & Terenzine, 1991; Perry, 1970) have suggested that students grow in response to the combination of typical experiences of college life. Although researchers have used a variety of instruments to measure both academic and personal growth (for recent examples see Hassan, 2008; Higgins, Lauzon, Yew, Brasweth, & Morley, 2009), little is known about how college-student growth compares to adversarial growth because comparisons require measures obtained with the same instrument. Therefore, we want to measure college-student growth with an instrument that has been used to measure adversarial growth. Tedeschi and Calhoun (2004) implied that the PTGI might be suitable for our purposes by characterizing its items as capturing the core of personal growth (without distinguishing between adversarial and personal growth). We decided to use the PTGI after noting that the developers themselves conducted the first study of non-adversarial growth based on the PTGI by using it to measure levels of personal growth described by college students in a small (n = 32) non-trauma comparison group (see study 3 in Tedeschi & Calhoun, 1996).
We conducted the second and third studies of interest (N = 347, N = 117) by using the PTGI to measure levels of college student personal growth, whether adversarial or otherwise attributed to a single semester of college life (Anderson & Lopez-Baez, 2008, 2011). All three previous studies documented mean levels of student personal growth near the midpoint of the range reported for posttraumatic studies (range 46.00–83.47 for 14 studies summarized by Linley & Joseph, 2004). In our second study, we elicited brief explanations from students to learn how they accounted for their growth (see Anderson & Lopez-Baez, 2011). Explanations were in the form of percentage attributions of total personal growth to student-identified naturalistic experiences and to a researcher-identified 3-hour course designed to facilitate growth (student explanations like: college internship accounted for 30% of my total personal growth, finding a job for 40%, 3-hour course of interest to researchers for 15%, miscellaneous other experiences for the remaining 15%). We refer to these explanations as attributions of cause. Student attributions of cause to academic experiences supported the appealing idea (to counselors and educators) that personal growth as defined and measured by the PTGI can be intentionally facilitated by activities designed to do so.
The two studies described in the current paper are the fourth and fifth based on the PTGI to measure college-student personal growth that was not strictly adversarial. Both samples are generally similar to those of our previous studies (Anderson & Lopez-Baez, 2008, 2011). The current studies extend the results of our previous ones by (1) measuring the growth attributed by sample members to substantially longer periods of college life and (2) eliciting separate attributions of cause for academic and non-academic experiences, respectively. The primary purpose of Study 1 was to examine the internal validity of student data collected with the PTGI by comparing the descriptive statistics among the results of Study 1 with those of the three previous studies described above (Anderson & Lopez-Baez, 2008, 2011; Tedeschi & Calhoun, 1996). The primary purpose of Study 2 was to measure graduating college-senior attributions of annual personal growth, in retrospect to their freshman, sophomore, junior, and senior years, respectively. Study 2 was descriptive.
Study 1: Cumulative Growth
Research questions 1 and 2 are designed to collect descriptive data. Research question 3 is designed to collect information about the internal validity of our data by testing hypotheses on the basis of comparisons of the results of Study 1 with the results of previous studies (Anderson & Lopez-Baez, 2008, 2011).
Research question 1. What levels of cumulative personal growth do participants describe for their college undergraduate years (descriptive statistics for PTGI scores)?
Research question 2. What cumulative percentage attributions of cause do participants describe for academic experiences (college credit) and non-academic (all other) experiences, respectively, during their college undergraduate years (descriptive statistics for attributions of cause)?
Research question 3. To what extent do the results of comparable studies reflect internal validity (comparisons of statistical results across studies)?
Method
Participants
We recruited participants from among the 147 students in a 3-hour elective course, Problems of Personal Adjustment, taught by the first author at a southeastern university. The course covered topics of psychological adjustment and included activities to facilitate student personal growth. Data was collected at the end of the fall semester of 2007. Most students were third- and fourth-year undergraduates in the College of Arts and Sciences or the School of Commerce. A total of 137 students elected to participate (response rate 93.20%). After 15 questionnaires with missing data were eliminated, sample size was 122 (67 men, 55 women). Most participants were Caucasian (4 African Americans, 4 Hispanics, Asians, and Asian Americans) with a mean age of 21.05 (.79) years and a mean college career of 6.54 (1.34) completed semesters. Students were given extra credit for electing to participate.
Measures
Posttraumatic Growth Inventory. Each of the 21 PTGI items (Cronbach alpha = .90, test-retest r = .71) describes a single positive psychological change (Tedeschi & Calhoun, 1996). Examples (with corresponding subscale and number of subscale items) include: “A sense of closeness with others” (Relating to Others, 7 items), “I developed new interests” (New Possibilities, 5 items), “A feeling of self-reliance” (Personal Strength, 4 items), “A better understanding of spiritual matters” (Spiritual Change, 2 items), and “My priorities about what is important in life” (Appreciation of Life, 3 items). Participants of trauma studies are instructed to describe the degree of each change resulting from their trauma. Responses are positions on a 6-point Likert-type scale anchored by 0 (no change) and 5 (great change). Total score range is 0 to 105 (per-item basis 0 to 5.00). Tedeschi and Calhoun (1996) reported evidence for concurrent and discriminant validity (their second study 1996) and construct validity (their third study). We have previously reported mean levels of college-student growth of 59.07 (SD = 15.77, N = 347; Anderson & Lopez-Baez, 2008) and 60.42 (SD = 16.61, N = 117; Anderson & Lopez-Baez, 2011).
Blank table. We use a blank, 2-column table to elicit attributions of cause (see Appendix A). In column 1, participants list life experiences thought to have contributed most to their growth during their college years. Participants list estimates of corresponding percentage contributions to total growth in column 2. For purposes of this study, we tailored the table to provide subtotals of attributions of cause to academic experiences (lines 1–5) and non-academic experiences (lines 6–10). We pre-labeled Lines 4, 5, and 10. Line 4 refers to the course taught by the principal author from which participants were recruited.
Procedure
Like subjects in our two previous studies, participants were instructed to complete a preliminary exercise to stimulate thinking about personal growth by answering questions that distinguished between level and salience of growth. We do not report the results because the research questions of the current study do not involve salience.
After completing the preliminary exercise, participants were given the following instructions for completing the PTGI (after study 3, Tedeschi & Calhoun, 1996):
Consider the degree to which each change listed below [21 items] has occurred in your life during your years as an undergraduate, whether or not the change was directly related to university class work. For each change, select the best response from the following scale [6 Likert-type options of Tedeschi & Calhoun, 1996] and write the number in the space provided.
Participants provided attributions of cause by completing the form in Appendix A. Participants were instructed to complete lines 1–10 of column 1 (brief descriptions of experiences thought to have contributed most to total growth) and column 2 (corresponding percentage contributions). Participants also provided 1-line qualitative explanations of how each experience on lines 1–10 of column 1 contributed to their growth (entries not analyzed because not required by research questions).
Results
Cronbach alphas for each subscale calculated from the data of Study 1 are: .87 (all), .82 (Relating to Others), .65 (New Possibilities), .56 (Personal Strength), .82 (Spiritual Change), and .54 (Appreciation of Life). The mean female PTGI score of 71.71 (11.72) is greater than the mean male score of 66.94 (10.98), t (120) = 2.32, p = .022 < α = .05 (2-tail), d = .42. Male and female scores were combined because no significant gender differences were found by t-tests of corresponding subscale means (Bonferroni-corrected α = .01).
Research Question 1
Table 1 contains descriptive statistics for participant PTGI scores on a total and per-item basis. Magnitudes of Cronbach alphas reported above can be seen to reflect the variations in magnitude among the standard deviations reported for per-item subscale scores in Table 1 (larger Cronbach alphas are associated with larger standard deviations). This observation suggests that restricted ranges among our data for Personal Strength and Appreciation of Life account for the low values of alpha for each subscale.
The mean total of 69.09 (SD = 11.52) is greater than the midpoint of 52.50 for the maximum range of 0–105 and greater than the midpoint of 64.74 for the range of 46.00–83.47 reported for trauma studies (Linley & Joseph, 2004). The per-item mean of 3.29 (SD = .55) exceeds 3 or “moderate growth” on the 0 to 5 response scale (scale midpoint = 2.50). The per-item means for four of the five subscales are between 3.30 (SD = .77) and 3.65 (SD = .62), inclusive. The per-item mean for Spiritual Change is lower, 1.68 (SD = 1.17).
Research Question 2
Table 2 contains descriptive statistics for participant percentage attributions of cause to academic and non-academic experiences, respectively (from column 2 of Appendix A). Both subtotals are large although the non-academic subtotal is larger by a ratio of approximately 3:2. The three academic experiences thought by each participant to have contributed most to his or her growth account for 30.76% (14.79 + 8.03 + 7.94%) of the mean subtotal of 39.63% (SD = 11.94) of total personal growth. Illustrative examples include internships, terms abroad, and three-credit semester courses. Participants attribute the third largest contribution (7.94%) to a course designed to facilitate growth. Three non-academic experiences account for 50.31% (25.96 + 15.18 + 9.17%) of the mean subtotal 60.36% (SD = 11.94). Examples include illnesses, extracurricular activities, deaths of family members, and positive and negative changes in significant relationships.
Research Question 3
Numerical PTGI scores and percentage attributions of cause are subjective assessments by participants, as are most self-reports. Our research purposes require data with a degree of internal validity (veridicality or truthfulness; that is, correspondence between self-reports and actual subjective impressions). Internal validity can be assessed by comparing results of analyses across samples of multiple studies. For purposes of the following comparisons, we will use words to number our previous studies and a numeral to designate Study 1 of the current paper. Thus, our sample one (Anderson & Lopez-Baez, 2008) was a group of 347 students who described total growth in 2005 and 2006 for their preceding semester, M = 59.07 (SD = 15.77). Our sample two (Anderson & Lopez-Baez, 2011) was a group of 117 students who described total growth and attributions of cause in May of 2007 for their preceding semester, M= 60.42 (SD = 16.61). Our sample 1 is that of the current study. Samples are characterized by similar demographic characteristics.
Comparison one. Members of samples one and two described total growth for a single semester. Therefore, we expected samples one and two to have similar mean PTGI scores (null 1: unequal mean PTGI scores). A visual inspection finds similar means. Null 1 is rejected on the basis of that visual inspection, Cohen’s d of .08 (minimal effect size), and 95% C. I. of – 4.71 to 2.01 that includes 0.00 (SE difference = 1.71). As expected, mean total scores of samples one and two are similar.
Comparison two. Members of sample 1 described total growth over more semesters than did members of samples one or two. Therefore, we expected our sample 1 mean PTGI score to be higher than the mean of either sample one or sample two (nulls 2 and 3: sample 1 mean less than or equal to means of samples one and two, respectively). Nulls 2 and 3 are rejected on the basis of a 1-way ANOVA F (2, 593) = 20.02, p = .000, η2 = .064 with an independent variable of 3 groups and dependent variable of student PTGI scores; and Bonferroni post-hoc t-tests of student PTGI scores: sample one and sample 1, t (467) = 6.44, p = 0.000 < α = .0167 (2-tail), d = .73; sample two and sample 1, t (237) = 4.70, p = 0.000 < α = .0167 (2-tail), d = .61; sample one and sample two, t (462) = .79, p = .430 > α = .0167 (2-tail), d = .08. As expected, the sample 1 mean of 69.09 is greater than the means of both sample one and sample two.
Comparison three. Visual inspection of the subscale per-item mean scores of sample one (Anderson & Lopez-Baez, 2008) and sample two (Anderson & Lopez-Baez, 2011) found that four of the per-item means in each sample are approximately equal and that all four are greater than the corresponding mean Spiritual Change score by 1.50 to 2.00 scale divisions. Therefore, we expected the subscale scores of our sample 1 to exhibit the same pattern. Visual inspection (Table 1) finds that they do. Thus, as expected, the five subscale per-item mean scores of each of our three samples are characterized by four comparatively high and approximately equal subscale mean scores and a comparatively low mean Spiritual Change score.
Comparison four. Members of all three samples completed the same 3-credit course (during different semesters) designed to facilitate personal growth. Members of sample two and sample 1 were asked for attributions of cause for the course (Anderson & Lopez-Baez, 2011). The resulting attributions of cause to a course designed to facilitate growth are themselves evidence of internal validity. We expected members of sample two to attribute a larger percentage of total growth for one semester to the class than members of sample 1 attributed for the longer period of several semesters (directional null 4: sample two mean attributions of cause to course less than or equal to corresponding mean of sample 1). Null 4 is rejected on the basis of an independent t (237) = 12.18, p = .000 < α = .05 (1-tail), d = 1.56. As expected, the sample two mean attribution of cause to the course of 25.28 (14.28)% is greater than the corresponding sample 1 mean attribution of 7.94 (6.46)%.
Comparison five. Concurrent validity is the extent to which different measures of the same construct agree. We formulated Comparison five only after we saw an opportunity to investigate concurrent validity among the data of sample 1 by measuring the correspondence between high and low Spiritual Change scores in sample 1 and the presence or absence of corresponding written attributions of cause, respectively (null 5: no agreement). We defined high participant Spiritual Change scores as per-item mean scores of at least 3.50 scale divisions (at least 1 division above scale midpoint 2.50). Sample 1 contains 11 high scorers (2 students scored 5.00, 1 scored 4.50, 3 scored 4.00, 5 scored 3.50). We defined low Spiritual Change scores as per-item mean scores no larger than 1.50 scale divisions. Sample 1 contains 64 low scorers (14 students scored .00, 17 scored .50, 20 scored 1.00, and 13 scored 1.50). We added 19 questionnaires selected at random from those of the 64 low scorers to provide a sample of 30 questionnaires for analysis.
We examined each sample member’s written attributions of cause for evidence of spiritual growth. We defined evidence of spiritual growth narrowly by the wording of the Spiritual Change items (PTGI items 5 and 18). Thus, we defined evidence of spiritual growth as student descriptions of one or more attributions of cause with at least one explicit reference to religion or spirituality including references to worship, God (or other deity or deities), prayer, or religious writings; but not references to ethics, morality, or meaning of life in the absence of the required explicit references. (Example attributions interpreted as evidence include: my faith deepened, came to accept God’s will, learned more about Bible; but not: grew in commitment to boyfriend, learned importance of moral behavior, decided on career choice, or obtained new understanding of life). We easily reached consensus on all identifications because of the simple and specific definition of evidence adopted beforehand. Attributions by 6 of the 11 high scorers and 2 of the 19 low scores contained at least one reference to spiritual growth. Half of the 8 attributions with references to spiritual growth referred to religious studies classes.
High and low Spiritual Change scores corresponded to the presence or absence, respectively, of attributions of cause for 24 of 30 sample 1 members, percentage agreement = 80.00%. Spearman r is a measure of agreement between two series of nominal data that does not take into account the probability of chance matches. The probability of chance matches is high among data of interest because the sample is characterized by many low scores and many non-positive attributions of cause. Cohen’s κ (1960) is a measure of agreement between two series of nominal data that accounts for the probabilities of chance matches. Cohen’s κ was originally developed to assess inter-rater agreement and is widely used for that purpose. We used it instead to measure agreement after accounting for chance between two series of nominal data rated jointly by consensus. Kappa values have a range 0 to 1 and are interpreted like positive correlation coefficients. Null 5 is rejected on the basis of a Spearman r = .51, p = .005 (1-tail) and κ = .44, approximate SE = .19, approximate p = .013.
Discussion
Measures
This study is based on a mixed-methods design with two measures for data collection. The first is the PTGI, a self-report instrument developed from standard psychometric techniques. Posttraumatic researchers use the PTGI to measure personal growth attributed to a trauma of interest to researchers. We use the PTGI as described by the instrument developers (Tedeschi & Calhoun, 1996) to measure total personal growth attributed to the sum of experiences during a time period of interest. Our second measure is an open table for obtaining participant attributions of cause for total personal growth. Participants complete the table with written descriptions of personal experiences and estimates of corresponding percentage contributions. The table design is adapted from one introduced in a previous study (Anderson & Lopez-Baez, 2011).
Validity
The purposes of the current study require a degree of veridicality (truthfulness), a form of internal validity. Researchers have reported little evidence of any kind for the validity of subscale scores beyond the results of exploratory factor analysis (see review in Anderson & Lopez-Baez, 2008). This is perhaps the reason why researchers have drawn few conclusions about growth from subscale scores in their results. Our comparisons one to three in the current study demonstrate the existence of predicted relationships among total PTGI scores of three samples and therefore a degree of internal validity for the collection of individual items and total PTGI scores. Comparison four demonstrates the existence of a predicted relationship between the attributions of cause from two studies and therefore a degree of internal validity for data obtained with the table-based approach. Comparison five demonstrates a degree of concurrent validity for both the subscale of Spiritual Change and the table-based approach.
Posttraumatic Growth Inventory as a Measure of Personal Growth
The PTGI was developed as a measure of posttraumatic growth. Tedeschi and Calhoun (2004) described their instrument as reflecting the core of personal growth. The results of the current study offer strong support for Tedeschi and Calhoun’s description because the results report high levels of personal growth for students without regard to prior experiences of trauma. We believe that if the PTGI can be used to measure the personal growth of samples of populations as dissimilar as college students and trauma survivors, then the PTGI can probably be used as a measure of personal growth under other circumstances in future studies.
College-Student Growth and Posttraumatic Growth
Magnitude. Theorists have identified the college-student undergraduate years as a time of personal growth in response to both academic and non-academic experiences (Chickering, 1969; Chickering & Reisser, 1993; Perry, 1970) in terms that suggest the definition of personal growth embodied in the items of the PTGI. Researchers have generally confirmed theorist predictions of student growth (c.f., Hassan, 2008; Higgins et al., 2009), but not with measures that allow for comparison of personal growth in response to trauma. Therefore, we are not surprised that the results of the current study reflect student growth. We are surprised, however, by the magnitude of that growth, m = 69.09 (SD = 11.52) because it is so near the maximum of the range reported in previous studies of posttraumatic growth (Linley & Joseph, 2004). We believe that this comparison helps readers appreciate the magnitude of growth described by each population.
Factor structure and subscales. The developers of the PTGI reported a 5-factor structure for the items of their instrument on the basis of an exploratory factor analysis (Tedeschi & Calhoun, 1996) and developed five corresponding subscales of unequal length. Subsequent posttraumatic studies have reported 2- and 3-factor structures (see review in Anderson & Lopez-Baez, 2008). Taken together, we interpret these EFA results as evidence that the factor structure of posttraumatic growth is not highly differentiated or stable across different samples. The results of several EFA described in our first study (Anderson & Lopez-Baez, 2008) demonstrated that the factor structure of a sample of student scores also lacked differentiation and stability, and therefore resembled that of posttraumatic growth.
Posttraumatic researchers have typically reported descriptive statistics for the total PTGI score and for the original five subscales. We report our results this way in Table 1, but also include recalculations on a per-item average basis (total score and subscale scores divided by number of corresponding items). The per-item format allows for comparisons of scores for subscales of unequal length. The reader can verify by inspection of Table 1 that all of our subscale scores round to 3.50 (to nearest .5 scale units) except the score for Spiritual Change, which rounds to 1.50. We have observed a similar pattern among the results of our previous studies (Anderson & Lopez-Baez, 2008, 2011).
During preparation of this manuscript, we conducted an informal visual comparison (not based on comparative statistics) of intra-sample per-item subscale scores listed in the results of posttraumatic studies cited by Linley and Joseph (2004) and observed that most subscale scores in each sample were of almost equal magnitude. Most of the few exceptions were low subscale scores (greater than 1.00 per-item average scale value) for Spiritual Change. We do not interpret our observation of this pattern as empirical evidence of anything; however, the observation makes us wonder about the empirical relationship between Spiritual Change and the other four subscales and highlights the importance of assessing the internal validity of Spiritual Change to lay the groundwork for any future studies of the internal structure of growth.
Spiritual Change and spiritual growth. Tedeschi and Calhoun (2004) described the PTGI subscales as measures of five domains of personal growth. The results of our comparison three (see results section) reflect a pattern among mean subscale scores in which the per-item mean score for the Spiritual Change subscale is relatively low. This pattern is empirical evidence that growth does not occur uniformly across all domains at the same time, at least for spiritual growth as measured by Spiritual Change. The occurrence of the similar pattern we observed in the samples of many studies invites the following attempt to explain the pattern. An explanation might be especially important to educators and administrators of religious colleges and universities who actively seek to promote spiritual growth of students.
Developmental factors are probably at least partly responsible for the larger Spiritual Change scores in posttraumatic studies. Trauma study participants have generally been older than participants in samples of college students. Perhaps older people like those in many of the trauma-study samples are more likely than college undergraduates to describe spiritual growth. Perhaps spiritual growth is more characteristic of growth in response to trauma than of growth in response to college life. However, these two possibilities do not completely account for the pattern of interest (similar per-item subscale scores for four subscales and lower subscale score for Spiritual Change) because the pattern is not as pronounced among the five per-item subscale means reported by Tedeschi and Calhoun (1996) for their non-trauma comparison group of college undergraduates. The results of our comparison five (see results section) suggest another developmental factor. Comparison five is based on the narrow definition of spiritual growth embodied in the 2-item Spiritual Change scale. Participant interpretations of item content necessarily influence patterns among subscale scores. In particular, differences in religious background could contribute to different interpretations of Spiritual Change items. We recruited our sample members 12 years after Tedeschi and Calhoun recruited theirs, and we recruited ours from a different university in a different part of the United States. Perhaps our sample members have different religious backgrounds than the members of Tedeschi and Calhoun’s sample.
Intentional facilitation of growth. Personal growth can occur in response to very different kinds of experiences, from coping with horrible trauma to caring deeply for friends and significant others. Most of these experiences, certainly most of the traumatic ones, seem to arise spontaneously. This conclusion is important to philosophers and developmental theorists because it suggests that personal growth is central to the human condition. Developmental theorists have suggested that personal growth also can occur in response to planned academic activities. This prediction is important to educators and others concerned with how to facilitate growth.
Students in the current study sample attributed 40% of their personal growth to academic activities (see Table 1). We interpret these results as strong support for the prediction of personal growth in response to academic activities. Students in the current study and in the sample of our second study (Anderson & Lopez-Baez, 2011) also attributed substantial growth to a single 3-hour course designed to facilitate personal growth. We interpret these results as support for the prediction that substantial levels of personal growth can be facilitated by specific academic activities designed to do so.
Current study results include attributions of cause for a single academic course designed in part to facilitate growth. The course is generally similar to that described by Hassan (2008) in a study of growth attributed to a health education course. The percentage attributions of cause to the course described in the current study (Table 2) are consistent with percentages reported in our previous study (Anderson & Lopez-Baez, 2011). The results of that preceding study and the study of Hassan (2008) strongly suggest that personal growth can be intentionally facilitated if not taught explicitly. The results of the current study suggest that substantial growth is attributed by students to coursework in general.
Current study results include subtotals of attributions of cause for academic and non-academic experiences, respectively. Sample members report attributions of almost 40% of total personal growth to academic coursework and provide further evidence that personal growth can be intentionally facilitated.
Study 2: Annual Growth
Taken as a whole, the results of Study 1 and three previous studies (Tedeschi & Calhoun, 1996; Anderson & Lopez-Baez, 2008, 2011) suggest that college students like those in the samples attribute substantial personal growth to both academic and non-academic experiences of their college years. These conclusions lead us to wonder just how much growth graduating seniors attribute to each college year. We believe the answer is of interest to college educators and administrators charged with fostering student growth. Answering this question requires a descriptive study of a representative sample drawn from a population of interest. Study 2 uses a simple descriptive design to answer the question for the population of students similar to those in the sample of Study 1.
Research Question
What levels of personal growth do members of a sample of college seniors attribute to each year of their 4-year college careers and what percentages do they attribute to academic and non-academic experiences, respectively?
Method
Participants
Participants were recruited from graduating college seniors enrolled during the spring semesters of 2009 and 2010 in the course described in Study 1. Students earned extra course credit for participating. A total of 117 participants were recruited (70 of the 84 graduating seniors among the 142 students enrolled in the spring of 2009 and 47 of the 67 graduating seniors among the 144 students enrolled in the spring of 2010). A total of 108 participants (59 women and 49 men) remained after eliminating 9 questionnaires with missing data. Most participants were Caucasian (5 African American, 9 Hispanics, Asians, Asian Americans and other). Mean age was 21.64 (SD = .48) years. Academic concentrations (and number of students) were: college of arts and sciences (77), commerce school (18), college of engineering (12) and school of architecture (1). Participants completed the survey at the end of the last class meeting, approximately three weeks before graduation.
Measures
We used the PTGI to measure total personal growth. We used the table in Appendix B to elicit attributions of cause. Participants completed the table with annual estimates (in percent) of their personal growth, growth from academic experiences, and growth from non-academic experiences. We also asked participants to identify on a separate page (not shown in Appendix B) the experiences that they thought contributed most to their growth each year and to identify these experiences as either academic or non-academic.
Procedure
The first two pages of the Study 1 and Study 2 questionnaires (warm-up exercise and PTGI) were identical. Study 2 participants followed the instructions in Appendix B to provide attributions of cause.
Results
Subscale Cronbach alphas for our data are .85 (Relating to Others), .57 (New Possibilities), .59 (Personal Strength), .88 (Spiritual Change), and .77 (Appreciation of Life). The male mean PTGI score of 65.71 (SD = 12.27) is less than the female mean of 70.02 (SD = 11.50), but not significantly less, t (106) = 1.93, p = .056, α = .05 (2-tail). A single significant gender difference was found among subscale scores between the male mean RTO score, M = 21.27 (SD = 6.05) and the corresponding female mean, M = 24.46 (SD = 5.51), t (106) = 2.87, p = .005, α = .01 (Bonferroni-corrected 2-tail). Subsequent analyses of PTGI scores in the sample of the current study are based on the combined scores shown in Table 3.
No gender differences were suggested by the results of independent t-tests of corresponding male and female percentages of total, academic, or non-academic growth, respectively, for any college year, α = .0125 (Bonferroni-corrected 2-tail for each set of 4 analyses). Therefore, subsequent analyses of percentages of growth are based on the combined scores in Table 4.
Table 3 contains the descriptive statistics for the sample of Study 2. A visual comparison of the contents of Table 3 with those of Table 1 shows that all corresponding entries are approximately equal. Table 4 documents mean attributions of substantial levels of personal growth to both academic and non-academic experiences for each of four years. The mean attribution to non-academic experiences exceeds the mean attribution to academic experiences for every year. The greatest mean growth is attributed to the senior year; the least is attributed to the sophomore year.
Participants were asked to identify the year of occurrence of the single experience (as defined by participants) to which they attributed the most personal growth (question not shown in Appendix B). Year and corresponding frequencies of selection by participants are: freshman (22), sophomore (9), junior (35), and senior (42). Participants also were asked to identify whether the experience that contributed most to growth was academic or non-academic. Type of experience and frequencies of selection are: academic (27) and non-academic (81). Finally, participants were asked to describe (in words) the experience that contributed most to their growth. Illustrative descriptions include: “getting a job,” “finding a new girlfriend,” “death of a friend,” “learning to live with fraternity brothers,” and “finishing my undergraduate thesis.”
Discussion
Measures
The current study is the fourth we have conducted based on the PTGI for measuring student personal growth and the third based on a table for collecting attributions of cause in the form of quantitative percentages and qualitative descriptions. We believe the results of the four studies support the utility of both measures for measuring growth and the flexibility of both for measuring growth under different circumstances.
College-Student Personal Growth
The mean PTGI score and standard deviation described by the 108 college seniors with a college career of exactly 8 semesters in the sample of Study 2, M = 68.01 (SD = 12.01) are almost identical to the corresponding statistics described by the 122 college students with a mean college career of 6.54 (SD = 1.34) semesters in the sample of Study 1, M = 69.09 (SD = 11.52). We had expected the mean PTGI score of Sample 2 to exceed that of Sample 1 because the results of our previous studies reflected higher mean scores for more college semesters (Anderson & Lopez-Baez, 2008, 2011; Study 1 of this paper). The simplest way to explain the similarity between the total scores of Study 1 and Study 2 is to remind ourselves that we are comparing data from a cohort as opposed to longitudinal studies, and remind ourselves that college student growth varies widely among students in each sample as illustrated by the large standard deviations for total PTGI scores in each of our studies. The results reflected in Table 4 suggest that the college seniors in our sample attributed substantial growth to both academic and non-academic experiences during all four college years. We believe this pattern is evidence that college faculty and staff can influence the personal growth of many students during every year of a student cohort’s progression toward graduation.
General Discussion
We believe Study 1 provides substantial information about the validity of total PTGI scores and also of subscale scores for Spiritual Change. For this reason, we believe the results of Study 1 are of interest to researchers using the PTGI to measure adversarial or personal growth. The high levels of personal growth attributed by students to the sum of their college years and attributions of cause to academic activities will probably interest college administrators and educators.
The same results led us to wonder how student growth and attributions of cause might be distributed over each college year. For this reason, Study 2 seemed a natural extension of Study 1. The descriptive results of Study 2 are among the first to reflect levels of growth attributed by graduating seniors in retrospect to each year of their undergraduate careers. We believe these results will also interest college personnel concerned with facilitating student growth.
We developed the table-based approach used in Studies 1 and 2 to measure attributions of cause. Researchers can adapt the approach for use in future studies of personal growth. We believe that researchers with other research interests can use a similar approach to study a wide variety of other variables.
Limitations
The two studies described in the current paper are based on self-reports. Thus, the results of both are subject to the many potential validity threats associated with self-reports including additional threats to the historical validity of retrospective self-reports. Our research purposes require data with sufficient internal validity. Comparisons of the results of our three studies reflect a degree of internal validity as described in Study 1. However, PTGI scores might be associated with ceiling effects if higher levels of growth are attributed to longer time periods at diminishing rates, and percentage attributions might be associated with recency effects if more vivid recall of recent experiences leads to larger attributions of growth. Finally, because our samples are representative of populations of similar students, but not university students in general, our conclusions do not necessarily apply beyond the population represented by our samples.
Future Research
We plan more studies of college student growth with the long-term goal of learning how best to facilitate growth. We hope that readers can adapt our approach to measuring attributions of cause for use in future studies of personal growth and other variables.
References
Anderson, W. P., & Lopez-Baez, S. I. (2008). Measuring growth with the Posttraumatic Growth Inventory. Measurement and Evaluation in Counseling, 40, 215–227.
Anderson, W. P., & Lopez-Baez, S. I. (2011) Measuring personal growth attributed to a semester of college life using the Posttraumatic Growth Inventory. Journal of Counseling and Values, 56, 73–81.
Chickering, A. W. (1969). Education and identity. San Francisco, CA: Josey-Bass.
Chickering, A. W., & Reisser, L. (1993). Education and identity. San Francisco, CA: Josey-Bass.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
Hassan, K. L. (2008). Identifying indicators of student development in college. College Student Journal, 42, 517–530.
Higgins, J. W., Lauzon, L. L., Yew, A., Bratseth, C., & Worley, V. (2009). University students’ wellness—What difference can a course make? College Student Journal, 43, 766–777.
Joseph, S., Linley, P. A., & Harris, G. J. (2005). Understanding positive change following trauma and adversity: A structural clarification. Journal of Loss and Trauma, 10, 83–96.
Linley, P. A., & Joseph, S. (2004). Positive change following trauma and adversity: A review. Journal of Traumatic Stress, 17, 11–21.
Pascarella, E. T., & Terenzine, P. T. (1991). How college affects students: A third decade of research. San Francisco, CA: Jossey-Bass.
Perry, W. G. (1970). Forms of intellectual and ethical development in the college years: A scheme. New York, NY: Holt, Rinehart and Winston.
Tedeschi, R. G., & Calhoun, L. G. (1996). The Posttraumatic Growth Inventory: Measuring the positive legacy of trauma. Journal of Traumatic Stress, 9, 455–472.
Tedeschi, R. G., & Calhoun, L. (2004). Posttraumatic growth: Conceptual foundations and empirical evidence Psychological Inquiry, 15, 1–18.
W. P. Anderson, Jr., is an Adjunct Professor in the Counselor Education Department and Sandra I. Lopez-Baez, NCC, is an Associate Professor and Chair of Counselor Education Programs, both at the University of Virginia. Correspondence can be addressed to W. P. Anderson, Jr., University of Virginia, Counselor Education Department, Box 400270, Charlottesville, VA 22904, wpa@alumni.virginia.edu.
Appendix A
Specific Experiences that Contributed to your Personal Growth
Consider the experiences (both academic and personal) that contributed most to your Personal Growth during your undergraduate years, whether or not the experiences were directly related to work for which you earned academic credit. Name and briefly describe the 3 academic experiences (besides PPA) that contributed most to your Personal Growth on lines 1–3 of Column 1 in Table 1 below. For purposes of this study, academic experiences are defined as those for which you earned academic credit. Note that PPA (including your project) is listed on line 4 for comparison purposes. Next, on lines 6–10, name and briefly describe the 4 non-experiences that contributed most to your Personal Growth.
Column 1 Column 2
Specific experiences Contribution (%)
Academic credit experiences that contributed most to your Personal Growth during your undergraduate years (i.e., classes, internships, practicums, etc.)
1. ________________________________________________ ______ %
2. ________________________________________________ ______ %
3. ________________________________________________ ______ %
4. PPA (including project) %
5. Misc. other academic experiences %
Subtotal: Personal Growth from academic experiences ______ %
Non-academic experiences (if any) that contributed most to Personal Growth during your undergraduate years (i.e., friends, relationships, gains, losses, etc.)
6. ________________________________________________ ______ %
7. ________________________________________________ ______ %
8. ________________________________________________ ______ %
9. ________________________________________________ ______ %
10. Misc. other non-academic experiences %
Subtotal: Personal Growth from academic experiences _____ %
TOTAL GROWTH FROM ALL EXPERIENCES 1–10 100 %
Appendix B
History of Personal Growth during your College Undergraduate Years
Step I. Recall the beginning and ending dates of each of your undergraduate years including the current one. Fill in
dates in the blanks of the line below to describe each year.
Freshman year Sophomore Year Junior Year Senior Year
Dates __/__ – __/__ __/__ – __/__ __/__ – __/__ __/__ – __/__
Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr
Step II. Consider the total Personal Growth you have experienced during your undergraduate years. Estimate the
percent of that total experienced during each year including the current year and place your estimate in the
corresponding blank of the following line. Make sure that the total of your entries on the following line =
100%.
% of total ______ % + ______ % + ______ % + _____ % = 100 %
1a 2a 3a 4a
Step III. For each year above consider what percent of your Personal Growth that year resulted from academic
experiences (experiences for which you earned academic credit) and how much from non-academic (all other)
experiences. Fill in the corresponding percentages in the two lines below. Make sure that your academic and
non-academic growth for each year (column entries) equal the entry for the corresponding year in Step II
(above). That is, make sure: 1b + 1c = 1a, 2b + 2c = 2a, . . . .
Growth in response to academic experiences
______% ______% ______% ______%
1b 2b 3b 4b
Growth in response to non-academic experiences
______% ______% ______% ______%
1c 2c 3c 4c
Sep 3, 2014 | Article, Volume 1 - Issue 2
Rebecca A. Newgent, Kristin K. Higgins, Stephanie E. Belk, Bonni A. Nickens Behrend, Kelly A. Dunbar
Group counseling has been highlighted as one effective intervention for at-risk students, yet debate remains as to the comparable efficacy of traditional interventions versus thematic interventions. This study compared two psychosocial educational programs, the PEGS and ARK Programs, designed to help elementary school students with social skills development, problem behaviors, bullying, and self-esteem with 15 elementary-aged students. Results revealed no differences between the programs and improvement on many indicators. Implications for school counselors are presented.
Keywords: elementary students, psychosocial education, prevention programs, school counseling, traditional interventions, thematic interventions
Group work can be an effective means of counseling at-risk students. As such, the American School Counselors Association (ASCA) has endorsed group work as an important component of school counseling (ASCA, 2005). Groups can help students learn to solve problems in an efficient and effective manner and is an ideal method for meeting the needs of at-risk populations (Akos & Milsom, 2007). Group counseling allows students to develop connections while at the same time exploring factors that may affect their achievement (Kayler & Sherman, 2009). Groups are used to address such issues as social skills (Bostick & Anderson, 2009), bolstering students’ self-perceptions (Eppler, Olsen, & Hidano 2009), preventing problem behavior (Todd, Campbell, Meyer, & Horner, 2008), increasing academic success (Brigman, Webb, & Campbell, 2007), and reducing school-wide bullying (Allen, 2010). Further, Quinn, Kavale, Mathur, Rutherford, and Forness (1999) conducted a meta-analysis (35 studies) of social skills interventions used with children exhibiting problem or emotional behaviors. Results revealed several important issues. First, it appears that there is a wide range of presenting deficits in children’s social skills. Second, social skills training is widely used as a mechanism to address problem behavior in children; however, it may not be as effective at addressing problem behaviors when used in isolation. The purpose of this study is to compare the effectiveness of two psychosocial intervention programs, Psychosocial Educational Groups for Students (PEGS) and the At-Risk Kids Groups (ARK) and to assess the impact of each program. The PEGS and ARK Programs are designed to help elementary school students in the following areas: social skills development, problem behaviors, bullying, and self-esteem.
Issues Addressed in Groups
According to Berry and O’Conner (2010), social skills are a set of learned behaviors that allow for positive social interactions, such as sharing, helping, initiating and sustaining relationships. Children who enter the academic setting with problem behaviors are often the children who lack the social skills to create and maintain positive social interactions. In a recent study by Bostick and Anderson (2009), 49 third graders with social skill deficits participated in a 10-week social skills program aimed at reducing loneliness and anxiety in an academic setting. Findings revealed significant reductions in loneliness and anxiety as well as significant increases in reading scores.
Groups also can be an effective manner in addressing children with problem behavior. For example, Hudley, Graham, and Taylor (2007) studied the level of children’s aggression in relation to personal responsibility. After a series of 12 lessons related to detecting other’s intentions, externalization, and positive responses, students showed improvement in socially acceptable behavior along with a reduction in overall aggression. For additional examples also see Cotugno (2009), McCurdy, Lannie, and Barnabus (2009), and Todd, Campbell, Meyers, and Horner (2008).
Olweus (1997) defines bullying as purposeful behavior, repeated over time, including an imbalance of power. Several psychoeducational programs have been developed to address the issue of bullying in schools (Horne, Bartolomucci, & Newman-Carlson, 2003; Jenson & Dieterich, 2007; Olweus, 1991). At-risk students are at particular risk for bully-related behaviors, including both roles of victim and perpetrator (Allen, 2010). Therefore, group intervention in the schools may be a beneficial way of directly addressing bullying.
Self-esteem, also known as self-concept, is often defined as the way in which children think about themselves in relation to their attributes and abilities (Kenny & McEachern, 2009). “Part of preventing problem behaviors is increasing the self-esteem of those with problem behaviors” (Newgent, Behrend, Lounsbery, Higgins, & Lo, 2010, p. 82). There are relatively few empirical studies, however, on the effectiveness of groups and self-esteem (e.g., O’Moore & Kirkham, 2001; Whitney & Smith, 1993).
Prior Research
A paucity of research exists on addressing multiple presenting problems in children. A major implication of the Quinn et al. (1999) meta-analysis suggested that psychoeducational groups may need to address more than one problem. A study conducted by Newgent et al. (2010) examined the effectiveness of a 6-week psychosocial educational group for students that addressed social skills, problem behaviors, bullying, and self-esteem. Results showed that students with multiple presenting issues benefited from a group addressing multiple issues. Further, results showed that students with no clinically relevant presenting issues also benefited from the multi-issue group (Newgent et al., 2010). In addition, some research is suggesting that while traditional, process-oriented groups can be effective at addressing problems, thematic groups have become more prevalent, especially when there is a time constraint (Hartzler & Brownson, 2001). While the benefit of thematic groups is a more specific or more common goal it also can lack the breadth of more traditional groups.
This study aimed to compare the effectiveness of two selective intervention programs (traditional vs. thematic) on measures of social skills, problem behaviors, teacher- and self-reports of peer relationships (bullying behaviors and peer victimization), self-esteem (self-worth, ability, self-satisfaction, and self-respect) and perception of self. Further, this study aimed to assess the impact of each of the intervention programs. The following research questions were tested: Is there a differential impact when comparing the PEGS Program to the ARK Program? Do the PEGS and ARK Programs have a positive impact on social skills, problem behaviors, peer relationships, and self-esteem?
Method
Participants
Eleven (n = 11) students were enrolled in the PEGS Program and four (n = 4) students were enrolled in the ARK Program. No attrition of program participants occurred. While participation was open to all students, teachers only recommended male students for both programs. Students in the PEGS Program were in the 4th grade and students in the ARK Program were in the 5th grade. In the PEGS Program five students (45%) identified as Caucasian/White, four (36%) identified as African American, one (9%) identified as Hispanic, and one (9%) identified as other. In the ARK Program one student (25%) identified as Caucasian, one student (25%) identified as African American, and two students (50%) identified as Hispanic. No students were identified as having some type of diagnosed learning, behavioral, or emotional disability.
Selective Intervention Program
One of the underlying tenants of the PEGS and ARK Programs is that students should not be labeled or stigmatized for having problems. Therefore, both programs utilized referrals that were based on underlying characteristics that lead to specific problems, not labels. Thus, students who are impulsive, depressed, dominant, lonely, easily frustrated, anxious, lack empathy, have low self-esteem, have difficulty following rules, are socially withdrawn, view violence in a positive way, have few friends, make negative attributions, have mood swings, instigate conflict, have difficulty handling conflict, or are aggressive or stressed were identified by their teachers and recommended for one of the two programs.
While both the PEGS and the ARK Program cover the same underlying psychosocial educational content, the primary difference is that the PEGS Program consists of traditional psychosocial education units while the ARK Program units are targeted toward peer victimization (i.e., bullying). Both PEGS and ARK provide a series of 6 one-half hour group sessions over the course of 6 weeks for elementary school students in grades 3–5. The six psychosocial education components of each of the intervention programs include: (a) improving social skills, (b) building and increasing self-esteem, (c) developing problem-solving skills, (d) assertiveness training, (e) enhancing stress/coping skills, and (f) prevention of mental health problems/problem behaviors. Lessons for the PEGS Program were adapted from Lively Lessons for Classroom Sessions (Sartori, 2000), More Lively Lessons for Classroom Sessions (Sartori, 2004), and the Missouri Comprehensive Guidance Programs (Frankenbert, Grandelious, Keller, & Schaaf, n.d.). These lessons focused on cooperation, encouraging students to be proud of who they are, breaking the chain of violence, handling anxiety and stress, and tolerance regarding differences. Lessons for the ARK Program were adapted from Bully Busters: A Teacher’s Manual for Helping Bullies, Victims, and Bystanders (Horne, Bartolomucci, & Newman-Carlson, 2003). These lessons focused on increasing awareness of bullying, building personal power, recognizing the bully and the victim, recommendations and interventions for helping victims, and relaxation and coping skills. Puppets were utilized in both programs to help model the lessons being taught. Worksheets related to the lessons also were utilized to help crystallize the concepts.
All recommended students participated in the one of the programs. Given class schedules, the school counselor recommended that students be assigned to one of the programs by grades. Therefore, all recommended 4th grade students were assigned to the PEGS Program and all recommended 5th grade students were assigned to the ARK Program. These assignments were done on a random basis. No control groups were utilized in this study at the school’s request. The same facilitators were used for both programs.
Instruments
Social Skills Improvement System – Teacher Form (SSiS–T). The Social Skills Improvement System – Teacher Form was developed by Gresham and Elliott (SSiS; 2008) and published by NCS Pearson, Inc. The SSiS–T is an 83-item rating scale designed specifically for teachers to use to assess children’s school-related problem behaviors and competencies. Scores reported for each of the three measurement areas are percentiles. Clinical levels for each of the three areas are as follows: social skills (≤ 16), problem behaviors (≥ 84), and academic competence (≤ 16). That is, lower scores on social skills and academic competence and higher scores on problem behaviors are considered clinically problematic. For the purposes of this study, only social skills and problem behaviors were evaluated. Cronbach alphas for social skills were .95 and .92 at pre- and post-test assessment, respectively. Cronbach alphas for problem behaviors were .86 and .89 at pre- and post-test follow-up assessment, respectively.
Social Skills Improvement System – Student Form (SSiS–S). The Social Skills Improvement System – Student Form was developed by Gresham and Elliott (SSiS; 2008) and published by NCS Pearson, Inc. The SSiS-S is a 75-item rating scale designed specifically for students aged 8–12 to use to assess their own school-related problem behaviors and competencies. Scores reported for each of the two measurement areas are percentiles. Clinical levels for each of the two areas are as follows: social skills (≤ 16) and problem behaviors (≥ 84). That is, lower scores on social skills and academic competence and higher scores on problem behaviors are considered clinically problematic. Cronbach alphas for social skills were .80 and .87 at pre- and post-test assessment, respectively. Cronbach alphas for problem behaviors were .74 and .70 at pre- and post-test assessment, respectively.
Peer Relationship Measure – Teacher Report. The Peer Relationship Measure – Teacher Report (Newgent, 2009a) was developed specifically for the PEGS and ARK Programs. It measures teachers’ perceptions about peer victimization. Nine items measure bullying behaviors and 9 items measure victimization. Items are scored as 0 = never, 1 = sometimes, and 2 = a lot, with scores ranging from 0 to 18 for each area. Scores reported for both of the measurement areas are totals. A high score indicates a high level of bullying behaviors and/or victimization; a low score indicates a low level of bully behaviors and/or victimization. Cronbach alphas for bullying behaviors were .89 and .90 at pre- and post-test assessment, respectively. Cronbach alphas for victimization were .78 and .90 at pre- and post-test assessment, respectively.
Peer Relationship Measure – Self Report. The Peer Relationship Measure – Self Report (Newgent, 2009b) was developed specifically for the PEGS and ARK Programs. It measures students’ perceptions about peer victimization. Nine items measure bullying behaviors and nine items measure victimization. Items are scored as 0 = never, 1 = sometimes, and 2 = a lot, with scores ranging from 0 to 18 for each area. Scores reported for both of the measurement areas are totals. A high score indicates a high level of bullying behaviors and/or victimization; a low score indicates a low level of bully behaviors and/or victimization. This measure is a parallel measure to the Peer Relationship Measure – Teacher Report. Cronbach alphas for bullying behaviors were .07 and .87 at pre- and post-test assessment, respectively. Cronbach alphas for victimization were .81 and .82 at pre- and post-test assessment, respectively.
Modified Rosenberg’s Self-Esteem Inventory (a). The Modified Rosenberg’s Self-Esteem Inventory (a) (Zimprich, Perren, & Hornung, 2005) measures an individual’s level of self-esteem (i.e., perception of self-worth, ability, self-satisfaction, and self-respect) and was completed by the students. The 10-item inventory uses a 4-point likert scale (strongly agree, agree somewhat, disagree somewhat, and strongly disagree). Five of the items are reverse coded and the score reported is the total, which ranges from 0–30. A high score indicates a high level of self-esteem; a low score indicates a low level of self-esteem. Cronbach alphas were .60 and .54 at pre- and post-test assessment, respectively.
Modified Rosenberg’s Self-Esteem Inventory (b). The Modified Rosenberg’s Self-Esteem Inventory (b) (Zimprich et al., 2005) measures an individual’s self-esteem (i.e., perception of self) and was completed by the students. The 4-item inventory uses a 5-point likert scale (never, seldom, sometimes, often, always). One of the items is reverse coded and the score reported is the total, which ranges from 4–20. A high score indicates a high level of self-esteem; a low score indicates a low level of self-esteem. Cronbach alphas were .59 and -1.56 at pre- and post-test assessment, respectively.
Procedure
Fifteen students were recommended by their teachers for the PEGS and ARK Programs and 15 parental consents/child assents were received. Recommending teachers completed the SSiS–T (Gresham & Elliott, 2008) and the Peer Relationship Measure – Teacher Report (Newgent, 2009a) at the start of the PEGS and ARK Programs and at two months after the conclusion of the PEGS and ARK Programs. Ideally, we would have liked an additional follow-up assessment; however, there was difficulty in securing a longer period of involvement in the assessments. Students completed the SSiS–S (Gresham & Elliott, 2008), the Peer Relationship Measure – Self Report (Newgent, 2009b), the Modified Rosenberg’s Self-Esteem Inventory (a) (Zimprich et al., 2005), and the Modified Rosenberg’s Self-Esteem Inventory (b) (Zimprich et al., 2005) at the start of the PEGS and ARK Programs and at two months after the conclusion of the PEGS and ARK Programs. All participating students received a certificate of completion at the conclusion of the last session.
Sessions for both programs were co-facilitated by two graduate students in the counseling and educational research programs. Both facilitators passed criminal background checks and had prior professional experience working with children who exhibit problematic behaviors. Supervision was provided to the facilitators by two counseling faculty members overseeing the programs.
Data Analytic Plan
Each student who participated in the PEGS or ARK Programs was assessed at two time points utilizing a variety of measurement instruments (see Instruments section). Pre-test scores on each of the instruments were initially analyzed using independent-samples t-tests to test the underlying assumption that participant scores between the groups were not significantly different. Next, scores were analyzed using independent-samples t-tests to assess for significant differences on the post-test assessment scores between the PEGS Program and the ARK Program participants. Finally, paired-samples t-tests, comparing pre-test to post-test assessment scores within each program, were used to assess the impact of each of the programs. Effect sizes are reported as small (d = .20), medium (d = .50), and large (d = .80) (Cohen, 1992; O’Rourke, Hatcher, & Stepanski, 2005).
Results
Between-Group Analysis
Teacher-reported measures. Results were initially analyzed using independent-samples t-tests comparing the pre-test assessment scores of the PEGS Program to the ARK Program. Statistical comparisons are displayed in Table 1. Analysis of teacher-reported social skills failed to reveal a significant difference between the two groups, t(12) = 0.76; p < .46. The effect size was computed as d = .76, which represents a large effect. Analysis of teacher-reported problem behaviors also failed to reveal a significant difference between the groups, t(10.73) = -0.15; p < .88. The effect size was computed as d = .08, which represents a very small effect. Analysis of teacher-reported bullying behaviors failed to reveal a significant difference between the groups, t(12) = -0.97; p < .35. The effect size was computed as d = .97, which represents a large effect. Analysis of teacher-reported peer victimization also failed to reveal a significant difference between the groups, t(12) = -2.14; p < .054. The effect size was computed as d = 2.13, which represents a very large effect.
Results were then analyzed using independent-samples t-tests comparing the post-test assessment scores of the PEGS Program to the ARK Program. Statistical comparisons are displayed in Table 1. Analysis of teacher-reported social skills failed to reveal a significant difference between the two groups, t(12) = 0.08; p < .94. The effect size was computed as d = .08, which represents a very small effect. Analysis of teacher-reported problem behaviors also failed to reveal a significant difference between the groups, t(12) = -0.28; p < .78. The effect size was computed as d = .28, which represents a small effect. Analysis of teacher-reported bullying behaviors failed to reveal a significant difference between the groups, t(12) = -1.56; p < .14. The effect size was computed as d = 1.56, which represents a very large effect. Analysis of teacher-reported peer victimization revealed a significant difference between the groups, t(11.29) = -3.48; p < .005. The effect size was computed as d = 1.84, which represents a very large effect.
Self-reported measures. Results were initially analyzed using independent-samples t tests comparing the pre-test assessment scores of the PEGS Program to the ARK Program. Mean scores, significance, and effect size are displayed in Table 1. Analysis of self-reported social skills failed to reveal a significant difference between the two groups, t(3.25) = 1.16; p < .32. The effect size was computed as d = 5.88, which represents a very large effect. Analysis of self-reported problem behaviors also failed to reveal a significant difference between the groups, t(13) = 0.56; p < .59. The effect size was computed as d = .56, which represents a medium effect. Analysis of self-reported bullying behaviors failed to reveal a significant difference between the groups, t(13) = 1.25; p < .23. The effect size was computed as d = 1.25, which represents a very large effect. Analysis of self-reported peer victimization also failed to reveal a significant difference between the groups, t(13) = 1.09; p < .30. The effect size was computed as d = 1.09, which represents a very large effect. Analysis of self-reported self-esteem failed to reveal a significant difference between the groups, t(13) = 0.18; p < .86. The effect size was computed as d = .18, which represents a small effect. Analysis of self-reported perception of self also failed to reveal a significant difference between the groups, t(13) = -1.17; p < .26. The effect size was computed as d = 1.17, which represents a very large effect.
Results were then analyzed using independent-samples t-tests comparing the post-test assessment scores of the PEGS Program to the ARK Program. Mean scores, significance, and effect size are displayed in Table 1. Analysis of self-reported social skills failed to reveal a significant difference between the two groups, t(13) = 2.03; p < .06. The effect size was computed as d = 2.03, which represents a very large effect. Analysis of self-reported problem behaviors also failed to reveal a significant difference between the groups, t(13) = 0.56; p < .59. The effect size was computed as d = .56, which represents a medium effect. Analysis of self-reported bullying behaviors failed to reveal a significant difference between the groups, t(13) = -1.31; p < .21. The effect size was computed as d = 3.40, which represents a very large effect. Analysis of self-reported peer victimization also failed to reveal a significant difference between the groups, t(13) = 0.82; p < .43. The effect size was computed as d = .82, which represents a large effect. Analysis of self-reported self-esteem failed to reveal a significant difference between the groups, t(13) = 0.79; p < .44. The effect size was computed as d = .80, which represents a large effect. Analysis of self-reported perception of self also failed to reveal a significant difference between the groups, t(13) = 0.33; p < .75. The effect size was computed as d = .33, which represents a small to medium effect.
Within Group Analysis – PEGS
Teacher-reported measures. Results were analyzed using paired-samples t-tests comparing the pre-test assessment scores of the PEGS Program to the post-test assessment scores of the PEGS Program. Mean scores, significance, and effect size are displayed in Table 2. Analysis of teacher-reported social skills failed to reveal a significant difference between the pre- and post-test assessments, t(10) = 0.38; p < .71. The effect size was computed as d = .11, which represents a very small effect. Analysis of teacher-reported problem behaviors also failed to reveal a significant difference between the pre- and post-test assessments, t(10) = 1.95; p < .08. The effect size was computed as d = .59, which represents a medium effect. Analysis of teacher-reported bullying behaviors failed to reveal a significant difference between the pre- and post-test assessments, t(10) = 0.13; p < .90. The effect size was computed as d = .04, which represents a very small effect. Analysis of teacher-reported peer victimization also failed to reveal a significant difference between the pre- and post-test assessments, t(10) = -0.20; p < .84. The effect size was computed as d = .06, which represents a very small effect.
Self-reported measures. Results were analyzed using paired-samples t tests comparing the pre-test assessment scores of the PEGS Program to the post-test assessment scores of the PEGS Program. Mean scores, significance, and effect size are displayed in Table 2. Analysis of self-reported social skills failed to reveal a significant difference between the pre- and the post-test assessments, t(10) = -1.52; p < .16. The effect size was computed as d = .46, which represents a medium effect. Analysis of self-reported problem behaviors revealed a significant difference between the pre- and post-test assessments, t(10) = 2.81; p < .02. The effect size was computed as d = .85, which represents a large effect. Analysis of self-reported bullying behaviors failed to reveal a significant difference between the pre- and post-test assessments, t(10) = 0.52; p < .62. The effect size was computed as d = .15, which represents a small effect. Analysis of self-reported peer victimization revealed a significant difference between the pre- and post-test assessments, t(10) = 2.95; p < .01. The effect size was computed as d = .89, which represents a large effect. Analysis of self-reported self-esteem failed to reveal a significant difference between the pre- and post-test assessments, t(10) = -0.22; p < .83. The effect size was computed as d = .07, which represents a very small effect. Analysis of self-reported perception of self also failed to reveal a significant difference between the groups, t(10) = 0.76; p < .46. The effect size was computed as d = .23, which represents a small effect.
Within Group Analysis – ARK
Teacher-reported measures. Results were analyzed using paired-samples t-tests comparing the pre-test assessment scores of the ARK Program to the post-test assessment scores of the ARK Program. Mean scores, significance, and effect size are displayed in Table 3. Analysis of teacher-reported social skills revealed a significant difference between the pre- and post-test assessments, t(2) = 6.25; p < .02. The effect size was computed as d = 3.61, which represents a very large effect. Analysis of teacher-reported problem behaviors failed to reveal a significant difference between the pre-and post-test assessments, t(2) = 1.84; p < .21. The effect size was computed as d = 1.06, which represents a very large effect. Analysis of teacher-reported bullying behaviors revealed a significant difference between the pre- and post-test assessments, t(2) = 5.00; p < .04. The effect size was computed as d = 2.88, which represents a very large effect. Analysis of teacher-reported peer victimization failed to reveal a significant difference between the pre- and post-test assessments, t(2) = 0.00; p < 1.00. The effect size was computed as d = 0, which represents no effect.
Self-reported measures. Results were analyzed using paired-samples t-tests comparing the pre-test assessment scores of the ARK Program to the post-test assessment scores of the ARK Program. Mean scores, significance, and effect size are displayed in Table 3. Analysis of self-reported social skills revealed a significant difference between the pre- and post-test assessments, t(3) = -4.14; p < .03. The effect size was computed as d = 2.07, which represents a very large effect. Analysis of self-reported problem behaviors failed to reveal a significant difference between the pre- and post-test assessments, t(3) = 1.31; p < .28. The effect size was computed as d = .65, which represents a medium effect. Analysis of self-reported bullying behaviors revealed a significant difference between the pre- and post-test assessments, t(3) = 5.42; p < .01. The effect size was computed as d = 2.71, which represents a very large effect. Analysis of self-reported peer victimization failed to reveal a significant difference between the pre- and post-test assessments, t(3) = 1.14; p < .34. The effect size was computed as d = .57, which represents a medium effect. Analysis of self-reported self-esteem also failed to reveal a significant difference between the pre- and post-test assessments, t(3) = -0.80; p < .48. The effect size was computed as d = .40, which represents a small to medium effect. Analysis of self-reported perception of self failed to reveal a significant difference between the groups, t(3) = -0.69; p < .54. The effect size was computed as d = .34, which represents a small effect.
Discussion
The purpose of this study was to compare and assess the impact of two selective intervention psychosocial education programs (traditional vs. thematic) on measures of social skills, problem behaviors, teacher- and self-reports of peer relationships (bullying behaviors and peer victimization), self-esteem (self-worth, ability, self-satisfaction, and self-respect) and perception of self in a small number of elementary school students. The PEGS and ARK Programs were designed to be short-term, non-stigmatizing programs that can easily augment current school counselor or school-based counseling services. Two groups of students were assigned to one of two programs. A discussion of the comparison between the programs and impact of each program follows.
Comparison
Findings indicated that there were no significant differences between the pre-test assessment measures when comparing the PEGS and ARK Program participants. That is, the participants in each group were comparable prior to their participation in their respective programs. Therefore, should we find significant differences between the two programs at post-test assessment, we may attribute those differences to the impact of the program. Further, there were no significant differences between the post-test assessment measures when comparing the two programs’ participants, with the exception of teacher-reported peer victimization. In other words, participants in each group were comparable after their participation in the respective programs with the exception of participants in the ARK Program having significantly lower levels of teacher-reported victimization than those in the PEGS Program. Note however that there was no difference between the pre- and the post-test assessment for ARK participants on this measure and only a non-significant increase for PEGS participants.
While these results indicate that the implementation of thematic programming directed at peer harassment does not have a significantly greater positive impact on students than traditional programming directed at students who show more generalized problems, there were some large effects (d ≥ .80) between the groups. This may indicate that there may have been some differences between the participants of the two programs, although not statistically significant.
PEGS
Findings indicated that there was significant improvement on self-reported problem behaviors and peer victimization when comparing the pre- and post-test assessments for the PEGS Program participants. In other words, students reported fewer problem behaviors and a decrease in victimization by their peers. While not significant, improvement also was found for teacher-reported problem behaviors and bully behaviors and self-reported social skills, bully behaviors, and self-esteem.
ARK
Findings indicated that there was significant improvement on teacher-reported bully behavior and on self-reported social skills and bully behaviors. In other words, students reported increased social skills and both teachers and students reported a decrease in victimization. Additionally, meaningful improvements (d ≥ .80) were found on teacher-reported problem behaviors. That is, teachers noted fewer problem behaviors in students after their participation in the ARK Program.
Conclusions
Finding effective programming that can positively impact at-risk students can be difficult. Further complicating the issue is the onslaught of thematic programming targeting specific groups of at-risk students (Hartzler & Brownson, 2001). While targeted programming can be beneficial to a select group of students it may exclude other students who can benefit but may not have the same “label.” This study showed that the more traditional group (i.e., PEGS) was equally effective as the more thematic group (i.e, ARK).
Limitations and Future Directions
There were several limitations to this study. First, we were limited to working within the parameters the school established. That is, we were limited to 4th and 5th grade males only and we were limited to providing services during their respective lunch periods. It may have been beneficial to have both genders in each group as well as a mix of grades. Second, group sizes were small. While larger group sizes would result in a greater ability to detect a statistically significant difference if one exists, larger group sizes also can result in reduced effectiveness. Third, we were unable to have a no-treatment group. The use of a control group may have resulted in a more robust study. Finally, the unequal group sizes may have impacted the comparison of the two groups. We attempted to adjust for this by using the Satterthwaite method when the equality of variances was significantly different (O’Rourke et al., 2005).
Implications
There are several implications for school and school-based counselors. First, it would be important in program management if school and school-based counselors are made aware that traditional psychoeducational groups are similarly effective to thematic psychoeducational groups. With this information they can make more informed decisions about the type of groups they implement as well as the type of intervention programs they offer and purchase. If the results of this study hold true for other groups of students and other schools, school and school-based counselors who choose to utilize more traditional programming would be able to provide these services to a broader range of students, consistent with the ASCA Model (ASCA, 2005), and not limit it to a select group of students targeted for a specific issue. Additionally, the purchase of thematic intervention programs can be costly given that the use is limited to a smaller number of students and several different types of programs are needed to address students with differing issues.
Second, the PEGS Program is an empirically supported program (see Newgent et al., 2010). It is a short-term, inexpensive, and non-intrusive program that can positively impact students with a variety of underlying issues. School and school-based counselors can easily augment their services with the implementation of this program. School administrators may be more supportive of a program that is both cost effective and would not hinder counselors from fulfilling other duties. The ever changing demands on school and school-based counselors will most likely continue. Counselors need effective tools that they can use to help students address problems, increase self-esteem, improve social skills, and decrease peer victimization.
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Rebecca A. Newgent, NCC, is a Licensed Professional Clinical Counselor with Supervision Designation in Ohio, a Licensed Professional Counselor with Supervision Specialization in Arkansas, and Professor and Chairperson of the Department of Counselor Education at Western Illinois University – Quad Cities. Kristin K. Higgins is a Licensed Professional Counselor with Supervision Specialization in Arkansas and an Assistant Professor of School Counseling in the Counselor Education Program at the University of Arkansas. Stephanie E. Belk is a certified school counselor in the state of Arkansas and a doctoral student in the Counselor Education Program at the University of Arkansas. Kelly A. Dunbar, NCC, is a Licensed Professional Counselor in Oklahoma, a doctoral candidate in the Counselor Education Program at the University of Arkansas, and is an Assistant Professor of Counseling and Psychology at Northeastern State University. Bonni A. Nickens Behrend has a Master of Science degree in Education Statistics and Research Methods and is currently a master’s student in the Counselor Education Program and a Senior Graduate Research Assistant at the National Office for Research on Measurement and Evaluation Systems at the University of Arkansas. This research was supported in part by a grant from Mental Health America in Northwest Arkansas. Correspondence can be addressed to Rebecca A. Newgent, Department of Counselor Education, 3561 60th Street, Moline, IL, 61265, ra-newgent@wiu.edu.
Sep 3, 2014 | Article, Volume 1 - Issue 1
Chiharu Hensley
Raising a child with special needs exacerbates the inherent challenges of parenting. Although the needs of children with special needs are addressed frequently in the literature, the needs of the parents of children with special needs are often neglected. In order to offer effective and useful services for parents of children with special needs, this article examines the types and nature of support services used by the parents of children with special needs and the effectiveness of those support services in reducing the parents’ stress levels and/or increasing their coping skills. Seventy-four parents of special needs children were assessed and results revealed that low-cost services, particularly those that resulted in mutual support, were a significant priority among parents. The article concludes with a discussion of clinical implications and needed directions for future research.
Keywords: parenting, children, special needs, support services, counseling
Parenting involves much effort and countless responsibilities. Child rearing can be one of the most challenging tasks with which a person is confronted. Raising a child with special needs intensifies the challenge significantly. However, although the needs of children with special needs are addressed frequently in professional literature and in the media, the needs of parents of children with special needs are addressed far less often. In order to offer effective and useful services for parents of children with special needs, their experiences with common issues and concerns and how their needs can be met must be investigated and understood because such information is essential to enable parents to feel empowered in raising their children with special needs.
Parents of children with special needs often experience high levels of stress from both internal and external factors. For example, a study conducted by Heiman (2002) revealed that 84.4% of the participants who had children with various special needs experienced feelings including “depression, anger, shock, denial, fear, self-blame, guilt, sorrow, grief, confusion, despair, [and/or] hostility” at the time of their children’s first diagnoses. Barnett, Kaplan-Estrin, and Fialka (2003) reported a study of parents of children who were mildly or moderately impaired that showed about half of the parents were still experiencing negative responses to their children’s diagnoses two or more years after the initial diagnosis.
In addition, parents of children with special needs may suffer being stereotyped by others. For example, Goddard, Lehr, and Lapadat (2000) used focus groups to collect individual narratives from parents of children with special needs. They found that, more than the parents’ guilt or the condition of the child, being perceived as a victim of a tragedy and the sole advocate for the child as well as a lack of understanding from others, including professionals, contributes to parental stress. Financial concern is another external factor which contributes to high stress levels in parents raising children with special needs. Looman, O’Conner-Von, Ferski, and Hildenbrand (2009) found that the severity of a child’s special needs increased the odds of financial burden experienced by the family. Clearly, there are a variety of both internal and external stressors, and accompanying emotional reactions, with which parents of children with special needs are confronted. Therefore, providing services to reduce the stress and negative feelings to minimum levels would lead to better quality of life for the parents of children with special needs.
Given the relative lack of attention to the support service needs of parents raising children with special needs, the purpose of this study was to conduct an exploratory investigation of the types of services used by parents of children with special needs and the effectiveness of those services for reducing parents’ stress levels and increasing their coping skills.
Four primary research questions were addressed in this study:
1. What are the types of services used by parents of children with special needs?
2. How effective are services in reducing stress levels of such parents?
3. How effective are services in increasing the coping skills of parents?
4. What are some of the needs of parents which may be met by counseling services?
Method
There were two major parts to this research. The first involved distribution of a survey to parents of children with special needs and the second involved an extensive interview with a representative parent of a child with special needs. In the first part of the study a survey was used to collect data for approximately one year. Potential respondents included parents and/or primary caregivers of preschool or school-age children with special needs who resided in a Midwestern state. No restriction was placed on the potential respondents based on the type or number of special needs their child had. Participants were recruited through contact with organizations for families of children with special needs (e.g., local associations for learning disabilities, pervasive developmental disabilities, and physical disabilities) and snowball sampling with assistance of professionals at local public schools who work with children with special needs and their parents. An online survey, the primary means of data collection, was created using a commercial website (www.surveymonkey.com), and potential respondents were directed to the survey webpage from either the websites of the organizations or by typing in the website address found on a distributed survey invitation flyer. A paper version of the survey was prepared for participants from a university clinic for speech and hearing.
The second part of the study involved an individual follow-up interview. Initially, the intent was to garner enough participants for a focus group activity. Unfortunately, however, of all the survey respondents, only one expressed interest in participating in a focus group. Therefore, this respondent was selected and interviewed in order to explore the stressors, challenges, and supports available for the parents in greater depth. The interview was audio-taped and transcribed by the investigator.
Results
There were a total of 74 respondents. Among the respondents, 70 (94.6%) completed the survey online and 4 (5.4%) completed the paper form of the survey. Selected survey items and the resultant data are shown in Table 1.
Table 1
Selected Survey Items and the Resultant Data
Survey Items No. of Responses % Responses
Items for all the respondents (N = 74)
How would you rate your degree of stress on the following scale?
In the last month?
Very low 0 0.0
Low 8 10.8
Moderate 22 29.7
High 33 44.6
Very High 9 12.2
In the last year?
Very low 0 0.0
Low 5 6.8
Moderate 23 31.1
High 24 32.4
Very High 21 28.4
What would be the ratio of each factor that might be contributing to your stress level?
Raising a child(ren) with special needs
About 1–25% 9 12.2
About 26–40% 15 20.3
About 41–60% 15 20.3
About 61–80% 23 31.1
About 81–100% 11 14.9
Financial concerns
About 1–25% 15 20.3
About 26–40% 20 27.0
About 41–60% 12 16.2
About 61–80% 7 9.5
About 81–100% 15 20.3
Have you sought professional services (i.e., therapies) in dealing with your stress of raising a child(ren) with special needs?
Yes 30 40.5
No 43 58.1
If you answered No to the previous question, what was (were) your reason(s) for not seeking professional services (i.e., therapies)? (n = 43)
Unable to afford the service 5 11.6
Schedule conflict 7 16.3
Did not know about any service available 7 16.3
Unable to find a service that seemed
helpful for your needs 12 27.9
Counseling as a category of received service (n=30)
Type of service you have received:
Individual counseling 22 73.3
Couples counseling 3 10.0
Family counseling 7 23.3
How helpful was the service for dealing with your stress?
Very helpful 7 23.3
Somewhat helpful 12 40.0
Neutral 2 6.7
Somewhat unhelpful 2 6.7
Very unhelpful 3 10.0
Compared with your stress level before receiving service, how much has it changed after
receiving service?
Not changed at all 2 6.7
Greatly reduced 12 40.0
Somewhat reduced 7 23.3
Unsure 2 6.7
Somewhat increased 2 6.7
Greatly increased 2 6.7
Compared with your outlook on raising your child(ren) with special needs before receiving service, how much has it changed after receiving service?
Not changed at all 3 10.0
Greatly more optimistic 12 40.0
Somewhat more optimistic 4 13.3
Unsure 6 20.0
Somewhat more pessimistic 1 3.3
Greatly pessimistic 0 0.0
Group as a category of received service (n=16)
Group counseling 2 12.5
Support group 14 87.5
How helpful was the service for dealing with your stress?
Very helpful 6 37.5
Somewhat helpful 8 50.0
Neutral 1 6.3
Somewhat unhelpful 0 0.0
Very unhelpful 1 6.3
Compared with your stress level before receiving service, how much has it changed after receiving service?
Not changed at all 0 0.0
Greatly reduced 10 62.5
Somewhat reduced 3 18.8
Unsure 1 6.3
Somewhat increased 2 12.5
Greatly increased 0 0.0
Compared with your outlook on raising your child(ren) with special needs before receiving service, how much has it changed after receiving service?
Not changed at all 1 6.3
Greatly more optimistic 8 50.0
Somewhat more optimistic 2 12.5
Unsure 3 18.8
Somewhat more pessimistic 1 6.3
Greatly more pessimistic 0 0.0
Items for the respondents who sought a professional service(s) in the past for dealing
with their stress of raising their children with special needs (n=30)
What have you gained from receiving service(s)?
Peer support 8 26.7
Professional support 7 23.3
Network 10 33.3
Specific knowledge about the
child(ren)’s disability(ies) 14 46.7
Specific skills for dealing with
the child(ren)’s needs 13 43.3
What are some of the factors that you consider when choosing a service?
Cost (including transportation
and session fees) 20 66.7
Schedule/frequency 21 70.0
Format (e.g., individual vs. group vs.
psychoeducational vs. counseling) 16 53.3
How likely are you to seek an additional service(s) in the future?
Very likely 9 30.0
Likely 8 26.7
Unsure 6 20.0
Unlikely 1 3.3
Very unlikely 0 0.0
If you were to receive an additional service(s), what would be the most likely format/venue?
Individual counseling 15 50.0
Couples counseling 4 13.3
Family counseling 8 26.7
Group counseling 1 3.3
Support group 13 43.3
Parenting training individual sessions 4 13.3
Parenting training group sessions 6 20.0
Individual psychoeducational sessions 0 0.0
Psychoeducational group sessions 3 10.0
Coping skills—individual sessions 5 16.7
Coping skills—group sessions 3 10.0
Stress management—individual sessions 8 26.7
Stress management—group sessions 4 13.3
Note. Some of the items allowed multiple answers by a single respondent. Percentage of respondents for each item was measured based on the number of respondents corresponding to specific items.
Some of the 74 respondents did not provide responses for all items. The respondent group included 67 females (90.5%) and 63 (85.1%) participants who identified themselves as Caucasian/White. Thirty-five respondents (47.3%) were between ages 31 and 40, and 58 (78.4%) were married. Fifty-nine of the respondents (79.7%) had one child with special needs and 31 (41.9 %) reported the child’s disability as moderate.
In regard to stress levels, 33 respondents (44.6%) indicated that they had experienced a high degree of stress in the past month, and 45 (60.8%) indicated that they had experienced either a high or very high degree of stress in the past year. Twenty-three respondents (31.1%) indicated that raising their child with special needs contributed to about 61–80% of their total stress level, and 20 (27.0%) indicated that their financial concerns contributed to about 26–40% of their total stress level. In regard to help seeking, 45 (60.8%) indicated that they had never sought professional services (e.g., various possible therapies) to cope with the stress of raising a child with special needs. The most frequently cited (n = 12, 27.9%) reason for not seeking support services was that they were unable to find services that they perceived to be helpful for their needs.
Among the 30 respondents who had sought professional services, 22 (73.3%) indicated that they had sought individual counseling (which also was the most used type of service). The second most used type of service was support groups, in which 14 respondents (46.7%) indicated that they had joined or were current members of a support group. Among those who had received individual, couple, family, or any combination of counseling, 19 (73.1%) indicated that their stress levels were reduced to some or a great extent after receiving such service(s) and 16 (61.6%) responded that their outlook on raising their child with special needs became somewhat or greatly more optimistic.
Specifically, among the 16 (53.3%) who had received either group counseling, participated in support groups, or both, 13 (81.3%) indicated that their stress levels were somewhat or greatly reduced and 10 (62.5%) indicated that their outlook on raising their child) with special needs became somewhat or greatly more optimistic. Finally, 14 (46.7%) responded that they had gained specific knowledge about the child’s disability from receiving the services and 13 (43.3%) responded that they had gained specific skills for coping with the child’s needs.
Although the respondents in this latter subgroup had participated in a wide variety of support services, it appears that most were psychoeducational in nature. Seventeen respondents (56.7%) also reported that they were either likely or very likely to seek additional services in the future. The three most selected types of services that these respondents would most likely seek were individual counseling (n=15, 50.0%), support groups (n=13, 43.3%), and family counseling (n=8, 26.7%). Session schedule and frequency, cost (including transportation and session fees), and format of the service were all important factors considered in use of support services.
The second part of the study was an interview with the mother of a son with cerebral palsy in order to gather information about personal experiences, particularly those contributing to her level of stress. The interview was conducted at a house close to the hospital to which she periodically brought her son for treatment. At the time of the interview, Amy (a pseudonym), the mother, was 39 years old, and Michael (a pseudonym), her son, was two years old. Amy was Caucasian, between 31 and 40 years old, married, and had one child with special needs; therefore, she was “typical” of the majority of the respondents to the survey. Specific interview questions were not prepared in advance. Rather, Amy was asked to convey her most important and/or strongest experiences and emotions as a mother of a child with special needs.
A wide variety of issues were discussed during the interview, but the most pressing issue mentioned by Amy was the lack of available resources for parents of children with special needs. Amy related that large cities might have many resources available, “but especially not my little small town—the resources are so limited.” She talked about how in attempting to acquire information and resources to aid in Michael’s care, she had asked many different people. Importantly, she did considerable research on her own, primarily using the Internet. She felt that many, or perhaps most professionals did not know more than she did, regardless of their formal education and training. She gave the example of having told one of Michael’s doctors about Euro-Pēds, a facility specializing in physical therapy for children with cerebral palsy and other neuromuscular disorders. The doctor did not know about this resource. Amy also related how shocked she was when a receptionist at a local mental health facility was not aware of a “respite” fund provided by the facility. She expressed that it was “disheartening that these people are supposed to guide me, and they just couldn’t.” Then she went on to describe a situation in which parents of children with special needs could not obtain the service they wanted because they did not use the technical term:
I was told that there were even situations where people who aren’t articulate would call and say, ‘I need a
babysitter.’ And they say, ‘We don’t do babysitting services.’ Click. Because they didn’t say ‘respite,’ they were turned away…. It’s their job to be in tune with, maybe there’s something I’m not getting here. Let me figure out what’s wrong with this person that’s calling my mental health facility.
Amy was often disappointed in seeking resources and help, probably because of the lack of understanding and education among professionals.
Amy lamented that resources external to the family should not cause more stress because parents of children with special needs already are overwhelmed by feelings of guilt, helplessness and stress. She believed that Michael was not the cause of her issues, but rather that the actual problems were the by-products of his having a disability:
It’s not always directly related to the child, but all the side effects that how they affect you… A lot of it is just the overwhelming feeling that sometimes you wake up in the morning and say, ‘I can’t believe that he has so many problems.’ And you feel sorry for him, and you feel stressed out about it.
Amy also felt guilty about not being able to spend as much time as she would have liked with her other two children; the demands of Michael’s situation dominated all her plans. Amy had tried to be with her other children whenever she could, but still felt that she was not doing enough for them. Thus, she believed that Michael’s disability affected not only her, but also everyone else in the family. Amy also felt tremendous pressure when talking to Michael’s doctors:
Michael’s doctors say, ‘We don’t know if he can ever walk. But we don’t know if he won’t. It’s gonna be up to you, Mom. It’s gonna be, if he’s got the potential to do it. You’re the one that’s gonna push him…’It’s a lot of pressure and I don’t think that these doctors meant to give me that unneeded pressure… But I work very hard to push Michael, you know, everyday. But it scares me. It scares me that, ‘Am I pushing him enough? Am I pushing him too hard?’
Obviously Amy (and other parents of children with special needs like her) suffers from high levels of stress from both internal and external factors. To Amy, taking care of Michael was like “not knowing how to swim and you get thrown into a pool with another person who doesn’t know how to swim.” When Michael was born, Amy had to teach herself how to raise a child with special needs because “these children don’t come with an instruction manual…or a book of resources.” She believed that knowledge about Michael’s disability would be particularly important in order for her to take care of him properly and effectively. She also was aware that the process of accepting her son’s disability and learning how to take care of a child with special needs could be “a nightmare for some people,” because “even someone with formal medical training struggles with these children.” Amy related that she thought a support group to provide opportunities for the parents of children with special needs to discuss and share experiences and feelings would be beneficial. She also believed that inviting a professional such as a social worker to the group who could help the parents fill out paperwork for requesting funds and other assistance would be beneficial because many parents of children with special needs struggle with understanding and completing formal documents properly. At the end of the interview, Amy indicated that she felt like she was contributing at least in a small way to improving the lives of parents of children with special needs by participating in the research and that the interview was helpful in reducing her stress.
Discussion
This preliminary research was conducted to gather data, collect descriptive personal information, and, from the data, suggest future practices for gaining understanding of the unique needs of parents of children with special needs. Suggested in the results of this exploratory study, is that counseling services for parents of children with special needs are both warranted and needed. The format of such services likely should be group counseling because of lower cost and potential for mutual support among group members. Such group counseling sessions should be in part psychoeducational and in part intended to foster support to meet the goals of knowledge and skill acquisition for parenting children with special needs and sharing personal experiences with others. Individual and/or family counseling might be used as a follow-up service, especially for parents or families of children with special needs who appear to need intensive care. Finally, parents of children with special needs should be able to choose how they would like to interact, such as by phone, home visit, or face-to-face because they often struggle with finding child care for when they are away from home. Having support group meetings at each other’s homes also can be an option so that parents can take turns watching children during meetings.
Limitations of this study included a small number of male participants. Whether more responses from fathers would have changed the results is only a matter of speculation. Thus, future research that includes significantly more input from fathers of children with special needs is needed. Also, to be noted is that some participants reported confusion about terms such as psychoeducation, which may have influenced their responses. Therefore, future research should identify specific services rather than the categories of services. Any online survey is limited to those who have access to the Internet and are comfortable using computers. Future studies can overcome this limitation to a great extent by incorporating multiple methods involving several types of data collection. Finally, the case interview was perhaps the most valuable part of the study in terms of revealing the reality and challenges faced by parents of children with special needs. Thus, qualitative, phenomenological research also would be beneficial, especially for understanding the unique and complex concerns of parents of children with special needs.
References
Barnett, D., Clements, M., Kaplan-Estrin, M., & Fialka, J. (2003). Building new dreams: Supporting parents’ adaptation to their child with special needs. Infants and Young Children, 16, 184–200.
Ergüner-Tekinalp, B., & Akkök, F. (2004). The effects of a coping skills training program on the coping skills, hopelessness, and stress levels of mothers of children with autism. International Journal for the Advancement of Counselling, 26, 257–269.
Goddard, J. A., Lehr, R., & Lapadat, J. C. (2000). Parents of children with disabilities: Telling a different story. Canadian Journal of Counselling, 34, 273–289.
Heiman, T. (2002). Parents of children with disabilities: Resilience, coping, and future expectations. Journal of Developmental and Physical Disabilities, 14, 159–171.
Looman, W. S., O’Conner-Von, S. K., Ferski, G. J., & Hildenbrand, D. A. (2009). Financial and employment problems in families of children with special health care needs: Implications for research and practice. Journal of Pediatric Health Care, 23, 117–125.
Chiharu Hensley, NCC, is a professional counselor in Nagasaki, Japan. The author thanks Dr. Devika Dibya Choudhuri for generously and patiently guiding me through the entire process of the current study while I was a Master’s counseling student at Eastern Michigan University, and those who willingly participated in the study. Correspondence can be addressed to Chiharu Hensley, 9-1-403 Manabino 2-chome, Nagayo-cho, Nishisonogi County, Nagasaki, Japan, 851-2130, chiharu.hensley@gmail.com.
Sep 2, 2014 | Article, Volume 1 - Issue 3
Sachin Jain, Santiago Silva
One of the major flaws in current psychological tests is the belief that a prediction/diagnosis can be made that would tell an individual whether he is heterosexual, homosexual or bisexual. What is needed within the profession, however, is an assessment that has the sensitivity to help clients explore their sexual orientation. A pilot 100-item Sexual Orientation Scale was developed after interviewing 30 self-identified gay men who considered themselves happy/satisfied. The items summarized the thoughts and feelings of these 30 men during the discovery process and ultimate acceptance of their sexual orientation. The scale was then completed by 208 male participants. The Cronbach’s Alpha Coefficient was calculated for the initial 100-item version of the Sexual Orientation Scale along with item analysis and factor analysis. These statistical manipulations were computed to help eliminate items that did not discriminate well. The final version of the Sexual Orientation contains 43 items. Implications for the use of this scale and future directions in research are further explored.
Keywords: sexual orientation, scale development, males, assessment, exploration
As children grow up in our society, they are introduced to a wide range of knowledge about sexual behavior by their parents, siblings, and peers. Part of their education addresses the ideas of sexual orientation and/or preference. The inherent messages in this education are that a person is either heterosexual (sexually attracted to members of the opposite sex), homosexual (sexually attracted to members of the same sex), or bisexual (sexually attracted to members of both sexes).
Historical Overview of Sexual Orientation
A number of theories on the origin of homosexuality have attempted to define homosexuality. A number of these theories (c.f. Drescher, 2008; Ellis, 1936; Freud, 1922/2010; Krafft-Ebing, 1887/1965; Nuttbrock et al., 2009) place sexual orientation within the context of an individual’s overall sex role identity. These individuals link sexual attraction for men toward women with a masculine sex role orientation and sexual orientation toward men with a feminine sex role orientation (Axam & Zalesne, 1999; Mata, Ghavami & Wittig, 2010; Storms, 1980). Sexual orientation refers to a particular lifestyle (behavior) that an individual displays. Storms (1980) and Moradi, Mohr, Worthington, & Fassinger (2009) found most theories about the nature of sexual orientation emphasize either the person’s sex role orientation or erotic orientation. Although these assumptions have had a major impact on the development of theories, research, clinical practice, and even popular stereotypes, neither assumption has been adequately tested in past research. A homosexual person is one defined as having preferential erotic attraction to members of the same sex and usually (but not necessarily) engaging in overt sexual relations with them (Crooks & Baur, 2008; Marmor, 1980).
Cass (1984) and Harper & Harris (2010) identified four steps in the discovery process that people experience as they begin to identify their sexual orientation:
1. Individuals come to perceive themselves as a homosexual by adopting a self-image of what it means to be homosexual.
2. Individuals take this self-image a step further and allow it, through interaction with others, to become a homosexual identity.
3. Individuals assume the necessary affective, cognitive, and behavioral strategies in order to effectively manage this identity in everyday life.
4. Individuals find a way with which to incorporate the new identity into an overall sense of self.
Assessment of Sexual Orientation
Fergusson & Horwood (2005) wrote a review of the multitude of methods that have been used to assess sexual orientation. Conceptualization of sexual orientation as dichotomous (i.e., heterosexual and homosexual) was overturned over 60 years ago by Kinsey, Pomeroy, and Martin (1948) and by Kinsey, Pomeroy, Martin, and Gebhard (1953). These studies resulted in the development of a 7-point scale in which 0 represented exclusive heterosexuality and 6 represented exclusive homosexuality. Three on the scale indicated equal homosexual and heterosexual responsiveness. Individuals were rated on this continuum based upon their sexual behavior and physical reactions (i.e., physical attraction to desired partners) (Coleman, 1987; Fergusson & Horwood, 2005).
Although this notion that people fall in a continuum better represented the realities of the world (Bagley & Tremblay, 1997; Silenzio, Pena, Duberstein, Cerel, & Knox, 2007), the Kinsey Scale has many limitations for accurately describing an individual’s sexual orientation. The scale assumes that sexual behavior and erotic responsiveness are the same within individuals. In response to this criticism, Bell and Weinberg (1978) used two scales in their extensive study of homosexuality. They examined two scales: one for sexual behavior, and one for erotic fantasies. Bell & Weinberg (1978) found discrepancies between the two ratings. Paul (1984) and Garnets & Kimmel (2003) also reported discrepancies in approximately one-third of their homosexual samples. It was reported that most men saw their behavior as more exclusively homosexual than their erotic feelings (Coleman, 1987; Fergusson & Horwood, 2005; Schwartz, Kim, Kolundzija, Rieger & Sanders, 2008).
Coleman (1987) and Fergusson & Horwood (2005) suggested that while this dichotomous and continuous view of sexual orientation represented an improvement in assessment of sexual orientation, several clinicians and researchers have recommended additional dimensions (Fox, 2003). These dimensions are those based upon both the biological sex of the partner and the biological dichotomous sex of the individual.
As the literature on psychological testing and homosexuality unfolded, it became clear that tests were not very effective in creating special scales, signs or scoring patterns that could differentiate homosexuals from heterosexuals (Garnets & Kimmel, 2003; Paul, Weinrich, Gonsiorek, & Hotvedt, 1982). Homosexuality was no longer being studied as an illness. Contrastingly, literature has brought forth strong data that dismiss the notion that homosexuality is a disorder (Cass, 1984; Coleman, 1982; Harper & Harris, 2010; Henchen & O’Dowd, 1977; Morin & Miller, 1974; Tripp, 1975; Troiden, 1977; Weinberg, 1978).
One of the major flaws in current psychological tests is that there is a belief that a prediction/diagnosis can be made that would tell an individual whether he is heterosexual, homosexual, or bisexual. It is the authors’ belief that it is inappropriate to predict what kind of lifestyle an individual will/should follow. What is more feasible is to assist an individual as he or she explores the experience of uncertainty. Therefore, an instrument is needed that has the sensitivity to help clients explore their sexual orientation, not one that identifies levels of disturbance.
Purpose of the Study
The purpose of the study was to construct an instrument that would help counselors in assisting clients who wish to explore sexual orientation. The instrument was to:
• Identify issues that need to be addressed by the client during the discovery of sexual orientation.
• Focus on issues such as self-definition, self-acceptance, fears, sexual fantasies, and understanding of lifestyle.
• Provide an information base for counselors as they help their clients unfold significant characteristics of their personality.
• Provide counselors a tool for helping clients meet the challenges they face now and will face in the future.
Method
Participants
The volunteer population of this study consisted of males who were either a) receiving personal counseling at a university counseling service, community mental health agency, and/or private practice; b) enrolled in introductory psychology classes at universities or community colleges; or c) participating in local men’s groups (i.e., Jaycees, Lions Clubs, support groups, etc.).
Two universities in California, eight universities in Texas, and one university in Wisconsin assisted in the collection of data. Three mental health agencies and four private counseling centers also were recruited for assistance in data collection. The private counseling centers served primarily gay and lesbian clients from the Dallas/Ft. Worth area.
Directors and/or counselors at the mental health sites mentioned above were visited. The purpose of the study was explained and they were asked if they would approach their clients (straight and gay) to determine their willingness to participate in the study. If the counselors were willing to speak to their clients, they were given instructions to share with clients who agreed to participate. They were instructed to give the client the research packet and return the completed information in the enclosed addressed/stamped envelope. Seventy-five agreed and completed packets from this group of mental health agencies were obtained.
Permission from psychology professors at the universities and/or colleges to address their introductory psychology classes was obtained for recruiting more subjects. The purpose of the study was shared with the class, willing participants were moved to another classroom, and they completed the research packet. One hundred and six packets were completed through this procedure.
Men’s groups were approached to obtain additional participants. Groups such as Jaycees, Lions Clubs, and Gay Men’s Support Groups were contacted and visited. A presentation was made that addressed the purpose of the study. Willing participants were provided with information packets, which they returned in enclosed envelopes. Thirty-three completed packets from representatives of the men’s groups were received. Twenty-eight of the 33 came from gay men’s support groups.
Demographic information from the personal data form was summarized and examined across the variable of sexual orientation on the following factors: educational level, socio-economic status, age, ethnicity, self-rating on the Kinsey Sexuality Scale and whether or not the participant was currently in counseling or psychotherapy. The males in the sample identified themselves as being either homosexual (gay) or heterosexual (straight). The males self-identified as gay or straight by rating themselves on the seven-point Kinsey Sexuality Scale (0=exclusively heterosexual to 6=exclusively homosexual). Straight responses were identified as those of which the men rated themselves as zero (0) or one (1) and gay responses were identified as those in which the men rated themselves as five (5) or six (6) on the Kinsey scale. Only six subjects rated themselves as 2, 3 and 4. The scales completed by these 6 subjects were not used in this study.
The sample consisted of a total of 208 men from cities in Texas, Wisconsin, and California: 132 were between the ages of 18–25 (63.5%); 52 were between the ages of 26–33 (25.0%); and 24 were between the ages of 34–40 (11.5%). According to the Kinsey Scale Rating, 104 were straight (50%) and 104 were gay (50%). Of the men who participated in the sample, 85 (40.9%) had received counseling and 123 (59.1%) had not.
Procedure
The first procedure consisted of the development of the items for the Sexual Orientation Scale. In order to achieve this task, thirty gay men who described themselves as being happy/satisfied with the gay lifestyle were interviewed. The men were identified via personal contacts and gay organizations. Their input was used to develop items for the Sexual Orientation Scale.
Three small group meetings of approximately two hours each with about ten men were scheduled. Each meeting began with a statement of the purpose of the groups and the study. It was explained that data was being collected to formulate a scale that would help people clarify questions about their sexual orientation. It was explained that the scale was not designed to label whether someone was gay or straight, but simply to identify issues surrounding sexual orientation. Time was allotted for questions and answers.
Participants were asked for permission to record the group session. When permission was obtained, participants were asked about their experience of the discovery process of their sexual orientation (e.g., “What struggles did you experience?” “What questions did you ask yourself during this discovery process?” “What were you feeling?” “Did you get in touch with any fears?” “What kind of sexual fantasies did you experience?”). These questions were asked in order to help the participant recall their discovery process. Participants were allowed to ask each other questions and/or identify with what was being shared in a casual and informal atmosphere. Recordings of the three small group meetings provided the source for the 100 items that represented thoughts and feelings the men experienced during their discovery process. These 100 items consisted of Phase 1 of the Sexual Orientation Scale development.
After the pilot scale was developed, packets were sent out to university counseling services, psychotherapists in private practice, and community mental health agencies. The packet consisted of: (a) a personal data form, (b) the 100-item Sexual Orientation Scale, (c) an informed consent form, and (d) an addressed and stamped envelope. Data on the 100-item Sexual Orientation Scale also was collected from different men’s groups and from the introductory psychology classes both at universities and community colleges.
Two hundred and eight packets were completed. Coincidently, 104 responses were from gay individuals, and 104 were straight responses. The responses were then transferred onto Scantrons and submitted for analysis.
Instrument Development
Item construction. Tests are composed of a number of items that are used to measure a particular subject. According to Wesman (1971), an item may be defined as a scoring unit. Creating an item should be taken seriously because each item in a test produces a unit of information regarding the person who takes the test.
Writing a test item is an involved process. Test items need to be subjected to constant evaluation in order to ensure, as much as possible, that they are measuring what they are intended to measure. The items developed for the Sexual Orientation Scale represent two variables: self-image and eroticism. These variables have been continuously identified in sexual-orientation literature (Cass, 1984; Coleman, 1982; Eliason & Schope, 2007; Grace, 1979) as variables that must be examined when attempting to answer questions regarding sexual orientation. Self-image is defined as involving self-definition, self-acceptance, fears and an understanding of lifestyle. Eroticism is defined as sexual fantasies.
Item analysis. According to Anastasi (1988), items on an instrument may be analyzed quantitatively, in regards to their statistical properties. When examining items qualitatively, content validity is considered as well as the evaluation of items in terms of effective item-writing procedures. Quantitative analysis primarily includes the measure of item difficulty and item discrimination (Anastasi, 1988).
Item difficulty answers the question: How hard or easy was a particular item for the group of participants? Item discrimination refers to the degree to which an item differentiates correctly among test takers in that behavior that the test is designed to measure (Anastasi, 1988, p. 210). Item discrimination was calculated as a correlation coefficient between the item score and the total score. Correlation coefficient indicates the strength and direction of a linear relationship between two random variables.
Results and Discussion
Item Design for the Sexual Orientation Scale
In designing the Sexual Orientation Scale, two areas of interests were salient. They were self-images and eroticism. The literature on sexual identity formation strongly supported the examination of these two interest areas during the discovery process of one’s sexual orientation. The importance of examining self-images and eroticism was further supported in the early stages of this study that resulted in the identification of the initial 100 items of the Sexual Orientation Scale.
Thirty self-identified gay men were interviewed regarding their discovery process. While reviewing interviews, items were generated that represented their thoughts and feelings. Examination of items clearly indicated that issues such as self-acceptance, understanding fears, and eroticism were being confronted during the discovery process.
Next, these 100 items were then subjected to an item analysis that resulted in identifying 45 items with item discrimination indices of 0.50 or higher. These 45 items were then further subjected to a factor analysis and an alpha coefficient.
An arbitrary decision was then made to use a 0.5 or higher factor loading in examining items. A strict convention of 0.5 or higher was used in order to identify the most discriminating items. The factor analysis identified the same items the item analysis identified. The alpha coefficients were as follows: overall= 0.924; straights= 0.723, gays= 0.653.
The factor analysis also identified four factors that were consistent with issues identified by both the literature and the initial 100 items. After reviewing the items in these factors, they were labeled as:
attraction to same sex
attraction to opposite sex
self-acceptance of gay behavior/attitudes
fears
Item Analysis
Anastasi (1988) pointed out that items on an instrument may be analyzed qualitatively, in terms of their content and form, and quantitatively, in terms of their statistical properties. The item analysis performed on the initial 100 items of the Sexual Orientation Scale focused on a quantitative analysis and more specifically on the measures of item difficulty and item discrimination.
Item difficulty refers to the percentage of subjects that endorse certain items on the scale. The closer the difficulty level approaches 0.50, the more differentiations the items can make (Anastasi, 1988).
Item discrimination refers to how effective the item discriminates between the two groups. Therefore, the higher the item discrimination score, the more effectively the item will differentiate between the two groups (gays/straights). Table 1 shows how the sample was grouped in order to establish an item-to-total score correlation, which is identified as a useful exercise to select items.
Based on the total score, the respondents were divided into quintiles (groups of approximately 40 subjects). A total score was established by assigning a value of 1 to true responses and a value of 2 to false responses. A true response indicated the way a gay man would respond. An item difficulty, identified in the item analysis as proportion of subjects that responded correctly to the item (PROP) and item discrimination, identified in the item analysis as a point biserial correlation coefficient (RPBI), were calculated for each item.
Table 2 outlines the item difficulty and item discrimination score and the scoring key of the initial 100 items. The asterisks identify the scores for the 43 items on the final version of the Sexual Orientation Scale. Of the final 43 items, approximately 76.7% of the items have a difficulty score that range from 0.40 to 0.60. Since Anastasi (1988) stated that the closer the difficulty level approaches 0.50, the more differentiations the item can make, it is safe to infer that the majority of the items on the Sexual Orientation Scale possessed good potential for differentiating between responses of the two groups. The remaining 23.3% of the items were not far behind. None of the item difficulty scores were less than 0.32 or higher than 0.76. This shows that the result of these items do not differentiate as well, but well enough to contribute to the overall reliability and validity of the Sexual Orientation Scale.
According to Anastasi (1988), the items that have low correlations with total score should be deleted and the items with the highest average inter-correlations will be retained. These items were retained because they are the ones that discriminate well and increase the validity of the test.
Analysis showed that 43 items on the initial 100-item Sexual Orientation Scale scored 0.50 or higher on the item discrimination index. Since the item discrimination refers to the degree to which an item differentiates correctly among test takers in the behavior that the test is designed to measure, one could assume the majority of the items on the Sexual Orientation Scale effectively differentiates between the two groups (gay/straight) that were tested.
Results of Item Analysis
The 45-item version of the Sexual Orientation Scale was a result of an item analysis done on the initial 100-item scale. The original data analysis identified 17 factors. An item discrimination index of 0.50 or higher was used to identify the items for the final version of the scale. Items that exhibited a higher level of commonality were selected. The 55 items that were deleted did not discriminate as well.
The 45 items were then submitted to the following statistical procedures: (a) Cronbach’s alpha coefficients were calculated for the overall sample, for the straight sample, and for the gay sample; and (b) a factor analysis was conducted via the running of five, four, three and two factor solutions on the overall sample (N=208). The factor analysis was done for the purpose of further validating the Sexual Orientation Scale.
Naming of the Factors
After creating and reviewing a SCREE Plot with the Eigen values of the 45 items, the researchers identified a bend that began to occur around the three, four and five factors. All factor solutions were investigated, and a decision was made to use the four-factors solution because (a) the items fit the four factors very well, and (b) the addition of a fifth factor accounted for negligent increase in the total variance. Every item in each factor carried a common theme.
The items in the four-factor solution were reviewed. Finally, two of the 45 items did not have a factor loading of 0.5 or higher. In keeping with the arbitrary decision of only using those items with a 0.5 factor loading or higher (for the purpose of implementing a stricter convention), items 36 and 15 were deleted. The final version of Sexual Orientation Scale resulted in having 43 items.
Table 3 summarizes the four factors solution by identifying the sorted rotated factor loadings of each item in each factor. Items 12, 22, 29, 39, 42, 45, 46, 49, 50, 59, 64, 65, 66, 67, 68, 72, 73, 80, 83, 88, 97, 98, and 99 loaded on Factor One with factor loadings ranging from 0.58 to 0.73. The common theme was sexual attraction to members of the same sex. The items in Factor One identified issues such as being attracted to nude males, erotic thoughts about men, masturbatory fantasies involving men only, relationships with males, etc. Therefore, Factor One was named “Attraction to Same Sex.”
Items 6, 14, 17, 41, 44, 48, 60, 63, 70, and 82 of Factor Two in Table 3 also had sexuality as their common theme. However, the sexual attraction addressed in the above items was towards members of the opposite sex. Their factor loadings ranged from 0.55 to 0.78. The items in Factor Two brought to surface issues dealing with erotic fantasies about women only, thoughts about women that led to sexual arousal, etc. Due to a common theme in these items, Factor Two was named “Attraction to Opposite Sex.”
Factor Three in Table 3 was comprised of items 16, 25, 33, 56, 89, and 91. These items had factor loadings ranging from 0.53 to 0.78. In examining these items, it was evident that the common theme surrounding the items was that of self-image and self-concept. The items in Factor Three addressed issues such as self-expression, expression of affection to another male, the acknowledgement of individual differences and the normalcy of being attracted to other men. Factor Three was named “Self-Acceptance of Gay Behaviors/Attitudes.”
Items 31, 32, 81, and 90 in Table 3 loaded onto Factor Four with loadings that ranged from 0.52 to 0.60. The common theme among these items was fear, which pertained to issues faced more often than not by gay men. The items addressed concerns in areas such as wanting to be sexually active with other men, jealousy, noticeable reactions to other men and fear of being gay. Because of the obvious common theme, Factor Four was named “Fears.”
It is vital to note that the four factors identified via the factor analysis represented those themes continually found in sexual orientation literature. Cass (1979 & 1984), Grace (1979) and Coleman (1982) consistently addressed the importance of examining the variables of self-images and eroticism during the discovery process of one’s sexual orientation. All these factors are clearly identified in the 43 items in the four-factor solution done on the final version of the Sexual Orientation Scale.
Reliability
A Cronbach’s alpha coefficient was performed in order to establish the reliability of the final 43-item version of the Sexual Orientation Scale. An alpha coefficient was done on the overall sample (N=205), the straight sample (N=103), and the gay sample (N=102). The overall sample has an N of 208. Three completed scales (1 straight respondent and 2 gay respondents) were eliminated because they did not complete the initial 100 items. The alphas for the 43-item version were 0.93 for the overall sample, 0.72 for the straight sample and 0.65 for the gay sample.
Construct-Related Validity
Internal consistency is a procedure used to establish construct validity. A statistical procedure used in this study to establish internal consistency was Cronbach’s Alpha Coefficient; this statistic also was used to establish instrument reliability (Miller, 1987). Table 4 shows the alphas which clearly exhibit the homogeneity for the items on the Sexual Orientation Scale.
A factor analysis was performed on the 45 items to identify the prevalent factors. After the factor analysis was done, four factors were identified as the most important factors that need to be examined when struggling with the uncovering discovery process of an individual’s sexual orientation. They are Attraction to Same Sex, Attraction to Opposite Sex, Self-Acceptance of Gay Behavior/Attitudes, and Fears. The items and other data on each factor are summarized in the following table. Normative data also was generated on the overall sample, the gay sample, and the straight sample. This was done for interpretation purposes. Table 4 summarizes the established normative data.
Limitations
This study contained methodological features which resulted in limitations. The major areas of limitations were (a) the sampling procedures and (b) generalizability.
Sampling Procedures
The initial 100-item Sexual Orientation Scale resulted from interviews with self-identified gay men who stated they were happy/satisfied as gay men. Only 30 men were interviewed. Although this number was an acceptable number, a larger number of men interviewed may have provided additional insights.
Of this group of 30 men (participating in the development of the items for the Sexual Orientation Scale) from cities in the Rio Grande Valley (RGV) in South Texas, 90% were Hispanic college graduates. Being a Hispanic gay man in the RGV in South Texas is difficult. The machismo attitude is somewhat prevalent in this area. This, coupled with a strong Catholic belief about homosexuality makes life as a gay man very secretive in this area. Thus, it is important to note that the initial 30 subjects were men who are openly gay, educated, motivated, and obvious risk takers. The sample group, therefore, may not have represented the “typical” gay man in the United States. Moreover, a different or more thorough perspective about what is involved in the discovery process with respect to sexual orientation might have risen if there had been a more diverse group of gay men in terms of ethnicity and geographical area.
The sample size (N = 208) utilized for the statistical item analysis was small. Although acceptable for this study, a much larger sample would likely improve the scale’s reliability and validity. The sample in this study did not include women. Women were excluded because it was suspected that gay men and lesbian women experience a different discovery process and that a parallel, yet different study is necessary for females.
Lastly, the two samples (gay/straight) are not actually directly comparable because, in essence, they were not selected in the same way. For example, a large percentage (65.4%) of the gay sample compared to 16.3% of the straight sample was enrolled in counseling. One can ascertain that most of the gay samples were selected from university counseling centers, mental health agencies, and the private sector. Contrastingly, the straight sample was selected from introductory psychology classes and from the membership of men’s groups (civic and/or support).
Generalizability
The generalizability of the results of this study is limited to men who are between the ages of 18 and 40 and who are either receiving counseling services from university counseling centers, mental health agencies, or the private sector, or who are in introductory psychology classes or members of men’s groups. The generalizability of this study is further limited to Hispanic and Caucasian men who met the research criteria.
Recommendations
The following are recommendations to either improve the present study’s design or identify areas for future research:
• Obtain a more culturally diverse sample by including representatives of other ethnic groups along with representatives from the Hispanic and Caucasian groups. This would increase the potential of gathering different perspectives and insights as well as increase the generalizability of the results.
• Utilize a larger more diverse sample in order to compare the reliability and validity data obtained in this study with other studies. A test re-test might be considered so as to verify the reliability of the Sexual Orientation Scale.
• In order to minimize a client’s tendency to answer the way they think their therapist or counselor wants them to, a lie/consistency scale may need to be established for the Sexual Orientation Scale. This may be done by including items that emphasize the same information, but written in a different manner.
Once the Sexual Orientation Scale has undergone further empirical investigation and eventual modifications, the use of the scale in counselor training programs should be considered. This would be done in hopes of (a) educating future counselors in how to assist clients who are confused about their sexual orientation, (b) increasing one’s sensitivity to and knowledge about gay/lesbian issues, and (c) requiring to some extent that future counselors accept and understand their own biases in regards to individual differences and more specifically to gays and lesbian.
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Sachin Jain, NCC, is an Assistant Professor, Department of Counseling, Oakland University, Rochester, MI. Santiago Silva, is a Clinical Professor & Director of the Counseling Assessment Preparation Clinic (CAP) at the University of Texas-Pan American. Correspondence can be addressed to Sachin Jain, Oakland University, Department of Counseling, 2200 N Squirrel Road, Rochester, MI, 48309, sacedu@yahoo.com.
Sep 2, 2014 | Article, Volume 2 - Issue 1
Robert C. Reardon, Stephen J. Leierer, Donghyuck Lee
A six-year retrospective study of a university career course evaluated the effect of four different class schedule formats on students’ earned grades, expected grades and evaluations of teaching. Some formats exhibited significant differences in earned and expected grades, but significant differences were not observed in student evaluations of instruction. Career services providers, including curriculum designers, administrators and instructors, will find the results of this study helpful in the delivery of services, especially with high-risk freshman students.
Keywords: career, teaching, course, instruction, evaluation, grades
While individual counseling has been shown to be effective in helping students develop career decision-making skills (Brown & Ryan Krane, 2000; Reese & Miller, 2006; Whiston & Oliver, 2005; Whiston, Sexton, & Lasoff, 1998), undergraduate career courses also can be effective interventions (Folsom & Reardon, 2003; Reardon, Folsom, Lee, & Clark, 2011; Whiston et al., 1998).
Although college career courses have been shown to offer substantial benefits (Brown & Ryan Krane, 2000; Osborn, Howard, & Leierer, 2007; Reed, Reardon, Lenz, & Leierer, 2001; Reese & Miller, 2006; Whiston & Oliver, 2005; Whiston et al., 1998), the content and format of such courses vary greatly (Folsom & Reardon, 2003). The present study sought to focus on one aspect of such career course variability: alternative class schedule formats.
Effective career classes can be characterized by these features: (a) structured course approaches appear to be more effective than unstructured approaches (Smith, 1981); (b) individual career exploration should be a cornerstone of the course (Blustein, 1989); and (c) five components (written exercises, individualized interpretations and feedback, in-session occupational exploration, modeling, and building support for choices within one’s social network) (Brown & Ryan Krane, 2000; Brown et al., 2003).
What is the effect that class schedule might have on course effectiveness? Only one study (Vernick, Reardon, & Sampson, 2004) has examined this issue, and the results showed that such courses should be designed to meet more than once a week and avoid over-exposure to materials and activities so as not to overwhelm the student. Extending this concept, we hypothesized that certain course schedule formats (weekly meeting frequency and term length) could make a difference in student learning and evaluation of teaching.
Alternative Career Class Schedules
This study focused on a course based on cognitive information processing theory incorporated into the course textbook, Career Planning and Development: A Comprehensive Approach (Reardon, Lenz, Sampson, & Peterson, 2000). All sections of the course followed a prescribed curriculum comprising a mixture of lectures, panel presentations, small and large group instructional activities, personal research, and field work; however, the classes differed in terms of the class meeting schedule (class duration, number of weekly meetings, and number of weeks a class met during an academic term).
We examined 57 course sections that met over a six-year period and were team-taught by lead instructors and co-instructors with an instructor/student ratio of about 1:8. Lead instructors included both professional staff and faculty who supervised the co-instructors. During the time of this study, four class schedule formats were used. In the case of a 16-week semester, the class met once per week for 3 hours; twice per week for 1.5 hours; or three times weekly for 1 hour. A fourth schedule option was for a 6-week term with the class meeting four times weekly for about 8 hours per week. In the 16-week semester, the class met once per week for 3 hours on Wednesdays (W); twice per week for 1.5 hours on either Monday/Wednesday or Tuesday/Thursday (MW/TuTh); or three times weekly for 1 hour on Monday, Wednesday, and Friday (MWF). A fourth schedule option was for a 6-week term where the class met four times weekly for about 8 hours per week on Monday, Tuesday, Wednesday, and Thursday (MTuWTh). In summary, we sought to evaluate the influence of these four class schedule formats upon the educational experience of the students as measured by expected grades, earned grades, and student evaluations of teaching.
Course Measures
The following section gives details about the three measures of student learning and perceptions of teaching used in this study.
Earned Grade (EG)
Although a student’s grade point average has limitations as a measure of academic achievement, class grades are nevertheless a widely accepted method of quantifying students’ level of educational achievement and future success in graduate school or employment (Plant, Ericsson, Hill, & Asberg, 2005). Specific to career development, Reardon, Leierer, and Lee (2007) showed that grades might be useful measures of career course interventions, “especially if the treatment variables are carefully described and the grading procedures are fully explained and replicable by other researchers” (p. 495). For this study, we assumed that a student’s final EG would accurately reflect learning in the course.
Expected Grade (XG)
Grade expectations are a complex phenomenon that combines realistic data-driven grade expectations with unjustified optimism or wishful thinking (Svanum & Bigatti, 2006). The XG reflects the student’s assessment of course demands and optimism about successfully meeting those demands. This grade prediction may be informed or uninformed; however, after completing multiple assignments over the course of the semester, Svanum and Bigatti (2006) noted that students lower the value of their XG such that it will be only moderately inflated and will reliably predict their final EG. Because students in our course had the course grading scale in the syllabus, a signed performance contract, and predicted their grades during the last week of the semester when 85% of their grade had already been accounted for, we hypothesized that in aggregate their predictions would be only moderately inflated and thus a reliable predictor of their earned grades and success in the course. We felt this grade variable was important as a measure of students’ confidence in their mastery of the career development subject matter and the problem-solving skills taught in the course, and therefore a valid measure of the relative effectiveness of different class schedule formats.
In addition, comparing EG and XG informs us about students’ self-evaluation of learning and their actual performance in the course. When there is not a significant difference between the two scores, we might suppose that students have a fairly accurate understanding of their performance on completed assignments and those still to be graded. By contrast, a significant difference between XG and EG indicates a discrepancy between students’ self-evaluations of graded and as-yet-ungraded assignments and the official final grades. If XG is significantly higher than EG in a section, one may conclude that the academic work has been undervalued by the instructor or overvalued by the students. Conversely, if XG is significantly lower than EG, one might conclude that students’ estimates were conservative or instructors recognized a level of performance not seen by the students.
Student Evaluation of Teaching (SET)
Student evaluation of classes and teaching effectiveness is standard practice at most postsecondary institutions. There is substantial anecdotal and experimental evidence supporting the usefulness of SETs (Centra, 1993; Marsh & Dunkin, 1992; Marsh & Roche, 1997). Certain student ratings forms provide important feedback that can be used to improve teaching performance (Greenwald & Gillmore, 1997; Marsh & Roche, 1997; McKeachie, 1997), and when asked most faculty members support the use of SETs as a tool for teaching improvement (Baxter, 1991; Griffin, 1999; Schmelkin, Spencer, & Gellman, 1997). Although SET is not without its critics, it appears to be a pragmatic way to access and compare student perceptions of teachers’ effectiveness and therefore a potential measure of the relative efficacy of different class schedules.
In an effort to better evaluate students’ course experiences, the influence of EG (Goldman, 1985) and XG (Greenwald & Gillmore, 1997) on SET is receiving considerable attention in the literature. The present study provided an opportunity to examine the relationship of SET to both EG and XG relative to four different class schedule formats.
Research Questions
In seeking to discover if particular class schedules were more effective in a team-taught career course, we evaluated grades and participant feedback from undergraduate students. The goal was to determine if any of the four differing class schedules produced significant differences in the course evaluation measures EG, XG, and SET. Although we were examining these measures from the students’ perspective and such measures are typically scored at the individual student level, we chose to examine class section level scores because XG and SET data were only available to us in this way.
The first group of research questions examined differences between mean evaluative measures, aggregated by class format and averaged for classes that met one (W), two (MW/TuTh), or three times per week (MWF) for 16 weeks, or four times per week (MTuWTh) for 6 weeks.
Research Question 1: Were there any significant differences in the career course evaluation measures among the four class formats?
RQ 1.1: Are there differences in mean EG between formats?
RQ 1.2: Are there differences in mean XG between formats?
RQ 1.3: Are there differences in mean SET between formats?
The second group of research questions explored the differences between the evaluation measures (EG, XG, and SET) within the sections.
Research Question 2: Within any given format, are there significant differences between the mean of the aggregated class evaluation measures?
RQ 2.1: Is the mean XG significantly different than the mean EG?
RQ 2.2: Is the mean XG significantly different than the mean SET?
RQ 2.3: Is the mean EG significantly different than the mean SET?
Method
Participants
Over a 6-year period, 1,479 students were enrolled in 57 sections of a career course to fulfill elective requirements for the baccalaureate degree. The class met in a standard classroom in academic buildings on the campus. Although the class was offered for variable credit, over 95% of the students took it for 3 credit hours. The number of students per section ranged from 19–34 with a mean of 26.5.
Ethnic diversity was generally proportional to the general student population of the university: Caucasian, 74%; African American, 12%; Hispanic American, 7%; Other, 4%; Asian, 3%; and American Indian, .4%. The course typically enrolled about 60% females and 40% males, including freshmen (15%), sophomores (45%), juniors (20%), and seniors (20%). Depending on the semester, between 15% and 25% of the course was composed of students with officially undeclared majors, and the large percentage of sophomores was the result of academic advisors referring these undeclared students to the class. While almost 40% of the members in a typical class reported satisfaction with their present career situation, about 60% were unsure, dissatisfied, or undecided.
Course Grading Procedures
Student grades were computed using scores earned on assignments contained in the performance contract. This contract was comprised of 28 different graded activities spread across the three units of the course. Given the use of the performance contract, students in this course should have had a very good idea of what their final grade would be when they filled out the SET and estimated their grade, because only two of the 28 activities accounting for 125 of 653 total points were still ungraded at that point.
Student Evaluation of Teaching Ratings
We used a standardized instrument for SETs, the Student Instructional Rating System (SIRS; Arreola, 1973), a student course on form developed at Michigan State University (Davis, 1969) and adapted for use at our university. SIRS provided an opportunity for instructors to obtain reactions to their instructional effectiveness and course organization and to compare these results to those of similar courses offered within the university.
The SIRS consisted of 32 items and 25 of these items enabled students to express their degree of satisfaction with the quality of instruction provided in the course by using a 5-point Likert scale. For example, the course was well organized could be marked strongly agree, agree, neutral, disagree, or strongly disagree. One item on the SIRS was of special interest in this study: What grade do you expect to receive in this course? A, B, C, D, or F.
We also employed a second instructional rating instrument, the State University System Student Assessment of Instruction (SUSSAI) which had been used at the university for five years prior to this study. This instrument consisted of eight items focused on class and instructor evaluation. One item was of special interest in this study: Overall assessment of instructor: Excellent=4, Very Good=3, Good=2, Fair=1, Poor=0.
Data Collection
After obtaining permission from the university institutional review board, we received the archived career course grade data for a six-year period. We aggregated the grades of these 1,479 students by class schedule and averaged the results to achieve a mean EG for each class schedule format.
The data relating to students’ perceptions of what they had achieved and the quality of instruction they had received was collected as follows: On the last week of class, while filling out their teacher evaluations, all students in a section were asked to indicate the grade they expected to receive and the results were tallied and averaged to determine a class mean XG. These class averages of 57 sections were forwarded to the researchers, and the results were tallied and averaged to find the mean XG for each class schedule format. In addition, we retrieved overall class ratings of instructors for an ad hoc sample of career classes over the 6-year period. These data enabled us to examine the relationships between mean EG and XG, EG and SET, and XG and SET.
Procedures
In this team-taught course where all instructors were involved in making large- and small-group presentations, each co-instructor had primary responsibility for evaluating the progress of students in his or her small group and assigning a grade, while the lead instructor of the team had overall responsibility for course presentations and management. In completing the SIRS and SUSSAI items for the SET, students were asked to provide a composite rating of the instructional team for their section. SETs were completed anonymously during the final two class meetings while instructors were out of the room and then returned by a student proctor to the university’s office of evaluation services.
Data Analysis
We examined how different class formats influenced mean EG, XG, and SET. The independent variable of class schedule format had four levels. The first three levels met over the course of a 16-week fall or spring semester for either 3 hours once a week (W), 1.5 hours twice a week (MW/TuTh), or 1 hour three times a week (MWF). The final level met for 2 hours four times a week over the course of a 6-week semester (MTuWTh). Because the assumptions related to independence for the three evaluative measures could not be met (i.e., the evaluations for each class section were correlated), we analyzed the data using a split-plot design.
Results
As is the case for other ANOVA and MANOVA tests, the dependent variables were assumed to be normally distributed. We tested the dependent variables to determine if they were normally distributed by computing skewness and kurtosis of each of the dependent variables to see if they fell between −1.0 and +1.0. Both the SET and EG scores did not violate the assumptions of normality as measured by skewness and kurtosis. However, while the skewness of XG did fall within the appropriate range, the kurtosis score was 1.04. Although this score is above 1.00, we believe this minor violation does not seriously affect the results and their interpretation.
Research Question 1
Using the split-plot MANOVA, we found a significant interaction of the three evaluative measures across the four class formats F (6, 106) = 4.47, p < .0005, η2 = .20. Specifically, there was a significant difference in EG between the four course formats, F (3, 53) = 19.15, p < .0005, partial η2 = .52. The EG for schedule MTuWTh (M = 3.50) was significantly higher (p < .005) than that of formats W, MW/TuTh, and MWF (M = 3.25, 3.32, and 3.31, respectively). Next, there was a significant difference in XG between the four course formats, F (3, 53) = 3.62, p = .019, η2 = .02. The means for XG for the W, MW/TuTh, MWF, and MTuWTh were 3.71, 3.57, 3.34, and 3.64, respectively. There was not a significant difference for XG between formats W, MW/TuTh, and MTuWTh. However, there was a significant difference between format MWF and format MTuWTh (p = .036), and format MWF was trending lower when compared with format W (p = .097) and format MW/TuTh (p = .051). Finally, there was not a significant difference on SET scores across the four formats, F(3, 53) = 1.36, p = ns. The mean SET scores for formats W, MW/TuTh, MWF, and MTuWTh were 2.88, 3.15, 3.31, and 3.11, respectively.
Research Question 2
When we compared evaluation measures within each format, we found significant differences with each one, F (2, 52) = 23.61, p < .0005, η2 = .47. We found XG significantly greater than EG within schedule format W (.46, p = .002) and format MW/TuTh (.35, p < .0005). By contrast, the difference between XG and EG was smaller and not statistically significant within format MWF (.13, p = ns) and format MTuWTh (.13, p = ns). This lack of a significant difference between EG and XG indicates that these students earned grades very similar to the grades they expected to receive. It is apparent that the students and instructors used similar evaluation and grading methods. Stated another way, this finding suggests that students in classes meeting more frequently per week have a slightly more accurate perception of how they are doing in the class.
We also found that mean XG was significantly greater than mean SET for format W (.83, p =.003), format MW/TuTh (.42, p < .0005), and format MTuWTh (.53, p < .0005). However, there was not a significant difference between XG and SET for format MWF (.13, p = ns). Finally, in comparing the difference between mean EG and mean SET within each of the four formats, we found a significantly higher EG only for format MTuWTh (.40, p < .0005). No significant differences were observed for formats W, MW/TuTh, and MWF, which had differences of .37, .07, and .13, respectively.
In summary, we found significant differences in the evaluation measures of XG, EG, and SET across the four different career course formats. Class sections which met four times a week for 6 weeks had a significantly higher EG than classes meeting one, two, or three times a week for a 16-week semester. Interestingly, formats W, MW/TuTh, and MTuWTh all had mean XG scores over 3.55, while format MWF’s XG was not only lower than the other formats, but significantly lower than that of format MTuWTh. Finally, mean SET scores were not significantly different from one another. Notably, they were all well above the rating of “good” (good = 2.0), with a mean of 3.15 on a 4-point scale. Means for the sections ranged between 2.88 and 3.31; thus we concluded that students found the instruction to be very good or excellent.
Discussion
Career course interventions have been developed to help students improve their academic and career decision-making skills. Comprehensive career courses offered for academic credit represent a cost-effective intervention that could be described as a “mega-dose” of career services (Reardon et al., 2011). While the benefits of college career courses are clear, it is unclear what contributions specific class formats (differing by length of class period, number of classes per week, length of course in weeks) might make to their effectiveness. Thus, the purpose of our study was to analyze the influence of different schedule formats on earned and expected grades and students’ evaluation of their instructors.
Previous studies on career development classes have described various limitations (see Gold, Kivlighan, Kerr, & Kramer, 1993; Reese & Miller, 2010), and we attempted to address these in the following ways. First, although we did not directly address random selection and random assignment issues, we aggregated class section scores instead of individual student scores, thus reducing the effect of individual outliers. By using the aggregate mean for each career planning section, individual students’ evaluation of the teacher remained anonymous yet the evaluation of the course section remained intact. The second limitation described by other researchers is the small number of participants in the career class analyzed. Over a six-year period we were able to collect data from almost 1,500 students from 57 sections of the course. The third limitation we attempted to address was the lack of equal representation of different ethnic groups. While we did not have equal percentages of students from different ethnicities, the demographic composition of our sample closely matched the composition of our university.
Perhaps the greatest strength of this study’s design was the replication of the intervention. That is, because the course structure and specific assignments were very similar for all sections, in effect the replication of the career course occurred across all 57 of the course sections analyzed. In each section, the course content and procedures were clearly specified and grades were based on the successful execution of a performance contract by the student.
Earned and Expected Grades
We examined how schedule influenced mean earned grade (EG) and expected grade (XG) scores. Like Vernick et al. (2004), we found that sections meeting only once per week over 16 weeks (format W) had the lowest EG, though not significantly lower than formats MW/TuTh and MWF. By contrast, schedule MTuWTh had a significantly higher EG than all the other formats, suggesting that a 6-week semester of 2-hour class meetings four times a week was more conducive to learning than a 16-week semester of classes meeting one, two, or three times per week for 3 hours, 1.5 hours, or 1 hour, respectively; that is, the “mega-dose” of career development interventions given in the course were intensified with MTuWTh.
Further analysis of the difference between mean section EG and XG scores enables us to compare the students’ view of their performance in the course with their actual performance. Ideally, we would prefer that there not be a significant difference between XG and EG in order to increase students’ confidence about the fairness of the grading and their sense of having mastered the material in the course. Expanding on these points, when the section mean XG was significantly higher than the mean EG, students could have left the course with a sense of failure and disappointment. Interestingly, in this study schedules W and MW/TuTh had significantly higher mean XG than mean EG, indicating an incongruity between the expected and earned grades. By contrast, for both schedules MWF and MTWF, the difference between mean XG and mean EG was not significant. One might conclude that fewer course meetings per week increased the difference between XG and EG scores.
Student Evaluation of Teaching
With regard to SET, there were no significant differences between the four class schedule formats, although we had suspected this might be the case. Perhaps a significant difference between section means for SET and XG would describe an incongruity between the students’ estimate of instruction quality and their evaluation of their own performance in the course. If XG were significantly higher than SET, this finding might indicate that students in these sections believed their performance was more related to their abilities and efforts rather than course instruction. By contrast, sections with significantly lower XG than SET scores may have rated instructors’ presentation of material higher than their own performance in the course. Interestingly, for schedules W, MW/TuTh, and MTuWTh, XG was significantly higher than SET, suggesting that students evaluated themselves more favorably than they did their instructors. We found it curious that for schedule MWF alone, XG was not significantly higher than SET.
Finally, EG is assigned to the student by the instructor, while SET is assigned to the instructor by the student. By comparing mean EG with SET, we can examine the relationship between an instructor’s evaluation of his or her students with students’ evaluation of the instructor. When EG is greater than SET, this means that instructors evaluated their students more favorably than they themselves were evaluated; conversely, when SET is greater than EG, students evaluated instructors more favorably than they themselves were evaluated. For schedules W, MW/TuTh, and MWF, there were no significant differences between mean EG and SET scores. However, for MTuWTh, in which students achieved a significantly higher mean EG than the other formats, the EG also was significantly higher than the SET, suggesting that this high-performing group had higher expectations for their instructors than they felt the instructors met.
Limitations
Because this study is field research, there are a few limitations to discuss. First, participants were undergraduates taking a career planning course from one university. The advantage to using this approach was consistency of teaching content, training and quality control of teaching personnel, administration of tests, and assignments, thus reducing the possibility that course differences were responsible for random error variance. But, because these results come from only one university’s career course, caution should be exercised when generalizing them to other courses.
Second, participants were not randomly selected. In fact, random assignment was impossible given the students’ autonomy in selecting this course. Random selection is seldom an option in field research at an educational institution, but this fact does restrict the robustness and generalizability of results to other populations (Babbie, 2001).
Third, participants in the study may have been experiencing more career-related difficulties than other students who did not elect to take the course. It is to be expected that participants perceived a career course as more important to their progress than nonparticipants, which limits generalizability of these findings (Smith & Glass, 1987).
Fourth, because the data were collected over a six-year period, it is difficult to determine the effect of historical events on the behavior and attitudes of participants (Smith & Glass, 1987; Van Dalen, 1979). For example, students from the initial semester of the study took the class at the height of the tech bubble, while others took the class in the shadows of the 9-11 tragedy. Although we were not able to control for these events, we acknowledge that researchers and practitioners must be aware of the influence of external events upon any college course.
Implications
There are several implications regarding the findings of this study. The significant differences found between schedule formats in the outcomes of EG and XG serve to remind instructors, those who supervise them, and those managing career courses about the potential impact of this variable. For example, these findings indicate that classes meeting one time per week for three hours are not characterized by higher earned grades, and by implication this means student learning. Additional studies should isolate and evaluate format variables such as length of the entire course, number of classes per week, and length of individual classes so that those evaluating teachers might consider this in their evaluations. At the same time, the absence of any differences in student evaluations of teaching across the four schedule formats is reassuring for those teaching and supervising instructors, at least in a course that was as highly structured and standardized as the one in this study.
Career services providers, curriculum designers, administrators, and instructors may wish to consider these findings when making decisions about the design and delivery of career courses, especially for high-risk freshmen (Osborn et al., 2007). Students meeting for four classes a week over a 6-week semester earned and expected significantly higher grades overall than students meeting over a 16-week semester. Taking the 6-week intensive course during the summer term before beginning the freshman year could both increase students’ chances of academic success and their confidence in navigating the college experience.
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Robert C. Reardon, NCC, NCCC, is Professor Emeritus at the Career Center at Florida State University. Stephen J. Leierer is an Associate Professor at East Carolina University. Donghyuck Lee is an Assistant Professor at Konkuk University in Seoul, Korea. Correspondence can be addressed to Robert C. Reardon, Career Center, Florida State University, 100 S. Woodward St., Tallahassee, FL 32306-4162, rreardon@admin.fsu.edu.
Sep 2, 2014 | Article, Volume 1 - Issue 2
John McCarthy, Michelle Bruno, Teresa E. Fernandes
The prevalence of depression increases markedly during adolescence, yet many youth are not receiving the support that they need. One factor that has been speculated as contributing to low rates of care is a lack of mental health literacy about depression and viable sources of support. This pilot study focused on mental health literacy as it relates to adolescent depression and suicidality and represented a pseudo-replication of Burns and Rapee (2006). Overall, participants (N=36) in this study were able to differentiate depressed vignettes from non-depressed vignettes and identify common symptoms of depression in their assessments. Also, sources of optimal help identified by participants varied upon the perceived degree of seriousness of the difficulties. Such results offer implications regarding the potential benefit of including adolescents in a more direct way when providing outreach or offering services.
Keywords: adolescents, mental health literacy, depression, suicidality, support
Depression in adolescence is of particular relevance, as it can continue into adulthood yet often goes undiagnosed and untreated (Wagner, Emslie, Kowatch, & Weller, 2008). According to the Diagnostic and Statistical Manual of Mental Disorders-Text Revision (DSM-IV-TR) (APA, 2000), the diagnostic criteria and duration mirror adult depression in many respects. As in adult depression, adolescent depression can include a variety of symptoms, at least one of which must be either depressed mood or loss of pleasure/interest. Furthermore, the DSM-IV-TR stipulates that, if depressed mood is chosen, it may be substituted by irritable mood in adolescents.
The rate of depression increases six-fold between the ages of 15–18 (Hankin, 2006). Approximately eight percent of teenagers—an estimated two million youth from 12–17 years of age—suffered at least one major depressive episode in 2007. Only 39% received some form of treatment for depression in the preceding 12 months. The rate of receiving professional help was much lower among those youth without health insurance (17%). Among all teenagers who obtained treatment, over half (59%) saw a counselor for assistance with their depression. Nearly 37% and 27% of youths saw a psychologist or general practitioner/family doctor, respectively (Substance Abuse and Mental Health Services Administration, 2009).
Given the prevalence of mental illness and its impact on society, it is no surprise that there is a growing interest in mental health literacy, a term first used by Jorm et al. (1997). Defined as the “knowledge and beliefs about mental disorders which aid their recognition, management or prevention” (p. 182), mental health literacy also includes knowledge about treatment and from whom to seek help. It has been found, for instance, that family and friends can be vital in the recognition of depressive symptoms (Langlands, Jorm, Kelly, & Kitchener, 2008a). However, Highet, Thompson, and McNair (2005) saw that family members usually recognized symptoms of the individual in hindsight. The general public often does not possess the knowledge base to help someone who is developing a psychotic illness (Langlands, Jorm, Kelly, & Kitchener, 2008b). Kitchener and Jorm (2002) found that individuals who took part in their Mental Health First Aid course showed improvement in recognizing disorders, and their views about treatment of disorders became more in line with those of professionals in the mental health field. In addition, the course reduced their stigma attached to mental disorders, increased their feelings of confidence in providing help, and increased the help provided to others.
Few studies have been conducted on younger populations and mental health literacy. Burns and Rapee (2006) noted, “While there is growing literature on the mental health literacy of adults, to date there has not been a parallel interest in the mental health literacy of young people” (p. 227). Wright et al. (2005) looked at young adults’ (ages 12 to 25) ability to pinpoint depression and psychosis and their recommendations for help to be sought. Nearly half of the participants were able to label the depressed vignette as depressed, but only a quarter of participants were able to label psychosis. People who were given the depressed vignette were less likely to choose a correct form of treatment than those given the psychosis vignette. Psychologists and psychiatrists were recommended more frequently for the psychosis vignette than for the depressed vignette, and a family doctor or general practitioner was chosen more often for the depressed vignette than for the psychosis vignette.
Adolescents have been found more likely to consider themselves “very confident” (Jorm, Wright, & Morgan, 2007a, p. 67) to help a peer in need with girls rating themselves as more confident than boys. In addition, across vignettes, confidence in providing help to a peer with a problem was higher for depression (without alcohol misuse) and social phobia than for psychosis and depression with alcohol misuse (Jorm et al, 2007a).
Jorm, Wright, & Morgan (2007b) found differences among Australian youth in the type of help sought for mental disorders. Participants were read vignettes describing youth of similar ages who were experiencing various disorders, then were asked a series of questions that included where they would turn with similar problems. For the vignette describing a teen suffering from depression, adolescents aged 12–17 chose family (54%) most often as a source of help and opted for mental health professional or service most infrequently (2%). Nearly one-third of young adults ages 18–25 selected family (31%) or a general practitioner/medical doctor (31%) on a similar vignette regarding depression. Overall the perceived barriers to help-seeking were personal in nature and did not relate to systemic characteristics, as they noted, “For young people, it is embarrassment or concern about what others think…” (p. 559).
Burns and Rapee (2006) used a vignette-based approach to measure mental health literacy among high school students in Australia. In their study, they utilized the Friend In Need questionnaire, created by the authors for that specific study. This instrument offers five short vignettes of teenage students, two of whom (“Tony” and “Emily”) represented youth meant to be clinically depressed. One of the two vignettes (“Emily”) offered a reference to suicidal ideation. The remaining three vignettes were of students facing difficulties, though were not intended to reflect depression.
They found that over two-thirds of participants (68%) accurately labeled “Emily” as depressed, while about one-third (34%) recognized “Tony” as depressed. Female participants were more likely to make a depressed diagnosis in both the “Tony” and “Emily” vignettes than the male participants. Female participants also showed more worry for the depressed vignettes than male participants. Among help-seeking sources, counselors were chosen most often for the helpers of the depressed teens, and this category was followed by friends and family/relatives.
To our knowledge, no study has been conducted on the mental health literacy of U.S. teens as it pertains to adolescent depression. With this point in mind, the current study represents a replication of Burns and Rapee (2006) and offers an initial sample involving older adolescents’ perspectives in the assessment, recovery time, and help-seeking recommendations regarding depression. Our central study questions were consistent with Burns and Rapee and the questions posed by the Friend in Need Questionnaire.
Procedures
Both prior to and after receiving approval by the university’s institutional review board, two of the authors met with the principal of the school where the data was collected. It was determined that eight sections of the school’s psychology and anthropology classes would be appropriate to the topic of study and ages of interest, and the primary author contacted the teachers and shared the following information with them: the parental/guardian consent form, the student consent form, details concerning the data collection process, and pertinent dates of the consent form deadlines and actual administration of the instrument used in this study. Teachers distributed the consent forms to students, who, if interested in possibly participating in the study, took them to their parents/guardians. Signed parental/guardian consent must have been completed and returned to the teachers in a four-day time period, which occurred prior to the date of the administration of the instrument. In both the parental/guardian and participant consent forms, it was made clear that the questionnaire was not a formal test and would take an estimated 25–40 minutes to complete.
On the day of the data collection, one of the two primary authors (JM and MB) went to the classroom, collected the completed parental/guardian consent form, read an abbreviated student consent form to the potential participants after giving a hard copy to them, and asked for questions at the conclusion. Students with unsigned parental/guardian consent forms were given an alternate class assignment, while those students who consented to be in the study completed the Friend in Need Questionnaire. No extra credit was granted for participation in the study. Participants completed the questionnaire in their classrooms. In a few instances, participants and the author/administrator were asked to move to a nearby vacant room for the data collection.
Approximately five classes were visited for data collection, and a total of 36 students, 21 of whom were young men, participated in the study. Most participants completed the questionnaire in approximately 20 minutes. The questionnaires were completed in an anonymous manner. In the coding process, a number was given to each questionnaire for tracking purposes only. Finally, the two authors also offered to return to the class after the data administration to further discuss the study; however, no teachers chose this option.
Instrument
Adolescents’ mental health literacy was assessed using the Friend in Need Questionnaire (Burns & Rapee, 2006). As previously described, the questionnaire presents five vignettes of young people experiencing various difficulties and solicits both close-ended and open-ended responses from participants. Specifically, participants are instructed to read each vignette and respond to the following general questions: (a) How worried would you be about the person’s overall emotional well-being? (b) What do you think is the problem of the person? (c) What aspects of the vignette provided the strongest hints that the person was having difficulties? (d) How long will it take this adolescent to feel better? and (e) Does this person need help from others to cope with his/her problems? The final question also has a supplemental, open-ended question regarding who the helper would be. The respondents are posed with all of these questions for each of the five vignettes. The complete Friend in Need Questionnaire can be found in Burns and Rapee (2006).
A coding system was devised for the open-ended responses, specifically on the responses asking about the youth’s problem, aspects of the vignette that provided hints, and the appropriate helper. For the question concerning the youth’s problem, the responses were filtered into two categories: “depressed” or “not depressed.” To qualify as “depressed,” the respondents needed to write the words “depressed/depression” or “suicide/suicidal.” Any other problems listed were considered to be “not depressed.” On the question regarding hints of the problem in the vignette, the coder was looking for responses that fit into diagnostic criteria for depression. The two depressed vignettes each had five diagnostic criteria imbedded in them, and this question tried to tease out whether respondents could identify these key criteria. Hence, the responses were categorized into the five diagnostic criteria of each vignette, with other responses not qualifying. The question that asked about the appropriate helper was split into nine possible categories of helpers. A few respondents, whose answers occurred rarely, were not included in the analyses.
Results
The findings are described in order of the items presented in the Friend in Need Questionnaire. The first question assessed whether adolescents could label a cluster of depressive symptoms in a case vignette as depressed. Respondents were asked, “What do you think is the matter with [name]?” This open-ended question elicited a variety of responses from respondents. Only responses that included “depressed,” “depression,” “suicide,” or “suicidal” were coded as a label of depression. In reviewing the responses to the two vignettes concerning students (Tony and Emily) depicted as depressed, it was evident that the majority of participants accurately labeled the vignettes, as 75% accurately identified Emily as depressed and 58% accurately labeled Tony as depressed.
The majority of respondents also accurately identified the non-depressed vignettes as such. Specifically, over 94% of respondents accurately identified Mandy as not being depressed. All participants (100%) accurately identified Jade as non-depressed, and over 97% accurately identified Nick as not being depressed. Frequencies of depressive codes for all vignettes are included in Table 1. Separate chi-square analyses were conducted to examine any differences in ratings of each vignette between male and female participants. Results indicated that no such differences exist on any of the five vignettes.
Second, in regard to respondents expressing greater worry for youth in the depressed vignettes versus the non-depressed vignettes, the Friend in Need Questionnaire instructed participants to rate their concern on a five-point scale with higher scores indicating more worry. The scores for the depressed vignettes (Emily and Tony) and non-depressed vignettes (Mandy, Jade, and Nick) were collapsed to produce mean scores of level of worry. A general linear model was used to compare sex differences (participant) in the intensity of worry scores for depressed and non-depressed vignettes. Results indicate that no significant differences existed between male (M = 3.40, SD = .38) and female participants (M = 3.45, SD = .33) regarding ratings of worry for the depressed (p < .58). No significant differences were found regarding male (M = 1.80, SD = .41) and female participants’ (M = 1.81, SD = .39) ratings of worry of the non-depressed vignettes either (p < .82).
The third question pertained to the length of recovery in the depressed and non-depressed vignettes. The respondents rated each vignette on the perceived length of time it would take the character to feel better on a four-point Likert scale from 1 (one or two days) to 4 (longer than a few months). Higher scores indicate a perception that more time is needed to feel better. Despite the use of a Likert scale, some respondents chose two answers or marked in between two options. When this occurred, the score was adjusted to reflect an average. For example, if someone circled, both “3” and “4,” a score of “3.5” was entered. This decision was made to maintain as many respondents as possible, given the small number of the sample. Overall, the respondents rated the depressed vignettes with a mean score of 3.67 (SD = .37), which indicates a recovery period of between “one or two months” and “longer than a few months.” This finding compared to a lower mean score of 1.97 (one or two days, SD = .45) for the non-depressed vignettes. Scores on the two depressed vignettes and scores on the three non-depressed vignettes were collapsed to create a composite mean score of recovery time for depressed (dependent variable) versus non-depressed vignettes (dependent variable).
A two-way MANOVA was conducted to determine if sex differences (of respondents) made a difference in the length of the recovery for both scenarios (depressed versus non-depressed). The overall model was statistically significant for the recovery time between the depressed and non-depressed vignettes F (1, 34) = 651.31; p = .01. The MANOVA did not reveal a significant interaction between participant gender and recovery time of vignettes (p < .27). Female respondents rated both the depressed vignettes (M = 3.82, SD = 24) and non-depressed vignettes (M = 2.03, SD = .43) higher than male respondents who rated the vignettes as 3.57 (SD = .53) and 1.93 (SD = .47) respectively, but this difference was not statistically significant.
Fourth, participants were asked to identify the elements of the vignette that demonstrated whether the fictitious teens were having emotional troubles. The two depressed vignettes (Emily and Tony) contained criteria of a Major Depressive Episode as described in the DSM-IV-TR (APA, 2000). In the case of Emily, respondents readily identified indicators of suicide (91%) and self-worth (72%). Respondents were less likely to identify symptoms of loss of interest (19%), fatigue (22%), and mood (19%) in this case. (See Table 2 for more complete results.) In the case of Tony, a majority of respondents identified loss of interest (75%) and weight loss (58%). Respondents were less likely to identify Tony’s fatigue (44%), insomnia (39%), and diminished ability to think or concentrate (39%).
Finally, after noting which symptoms were strong indicators of problems, respondents answered an open-ended question about sources of help to aid the person in the vignette. For all five vignettes, participants answered whether they thought the person in the vignette needed help from another person. The options included “no,” “yes,” or “don’t know.” If the respondents endorsed that the person did need help, they were asked to answer a follow-up question indicating who they think should help the person. For the depressed vignettes, 58% of respondents indicated that Tony needed help, and 75% indicated the same for Emily.
In regard to the type of helpers, participants’ responses were broken down into nine categories of helpers, including counselor; friends; family; professional; psychologist; psychiatrist; doctor; teacher; and someone who has had the same difficulty. Some coding decisions included how to categorize responses not explicitly in the list. Some of these included counseling, school counselor, and guidance counselor, which were included in the category of counselor. For the friend category, other responses included “peers” and “someone who knows him/her well.” For family, “parents,” “relatives,” “siblings/brother/sister” also were included. Non-specific terms were included in the professional category, including specialist, shrink, therapist, psychotherapist, and family therapist. Other responses included in the psychiatrist category were “doctor for depression/depressed kids” and “doctor who prescribes antidepressants.” Some responses that were not coded included third party, new people, anyone, role model, someone he/she doesn’t know, and everyone.
Nearly half of the participants (47%) identified the family as the suggested primary helper for Tony, while over one-third (36%) of participants suggested a counselor. The same percentage (36%) identified the family and a psychiatrist, respectively, for Emily, as the best sources of help (see Tables 3-4 for more complete results).
Discussion
The primary purpose of this study was to examine the level of teenagers’ mental health literacy specific to adolescent depression. Because it was a pilot study that involved a relatively small sample size, the findings are admittedly limited in generalizability. However, even with the small sample size, the results offer initial points of comparison to Burns and Rapee’s (2006) larger scale study. First and perhaps foremost, the level of detection of adolescent depression was relatively high in the present study, yet no significant differences were found as they related to gender. Over half of the participants correctly labeled both depressed-based vignettes (Emily and Tony) as being depressed, and three in four participants indicated that Emily was depressed. To their credit, participants rated both depressed vignettes as highest in terms of depression.
This finding is noteworthy. In Burns and Rapee (2006), the corresponding findings of correctly identifying depression in Emily and Tony were 68% and 34%, respectively. The higher rating of Emily as depressed was similar in both settings, yet the rating of Tony as depressed was sizably different with American participants being more inclined to have viewed the fictitious student as depressed.
A closer investigation of this finding points to critical symptoms chosen in the participants’ assessment. The vignette of Emily featured pointed comments of suicidality, and, to no surprise, it was this characteristic that was almost uniformly (92%) expressed by participants when asked about the “strongest hints that something was wrong.” The element of suicidality also was foremost in Burns and Rapee (2006) in reference to Emily, but its expression was lower (77%) among the Australian sample. At least two possibilities are present. First, it is conceivable that the Australian teenagers were not as concerned about the suicidal ideation as the U.S. participants in the present study. A second possibility is that the awareness of suicidality among adolescents has increased in more recent years in the U.S., prompting a higher rate among the U.S. teenagers.
Suicidality was absent in the vignette of Tony. However, other signs of depression were present, and these symptoms included anhedonia, fatigue, weight loss, insomnia, and diminished ability to think/concentrate. Both U.S. participants in the present study and Australian participants in Burns and Rapee (2006) placed “diminished loss of interest” as the primary symptom of an emotional difficulty at nearly identical rates (73% and 75%, respectively). The same held true for the second-rated symptom (weight loss) in both samples, again expressed by nearly the same percentage (58% in the present study and 61% in Burns and Rapee). The consistency in the ranking and percentages of both samples reflects the teenagers’ recognition of lowered interest levels and appetite difficulties leading to weight loss when an adolescent is experiencing depression. In actuality, both behaviors do indeed tend to be two of the six most frequent symptoms among teenagers who are depressed (Roberts, Lewinsohn, & Seeley, 1995).
To their credit, participants in the present study also were able to differentiate the depressed vignettes from the non-depressed vignettes. Mandy was feeling upset over a relationship termination initiated by her former boyfriend that occurred three days prior. Jade expressed family disruption and had become intoxicated at a recent party. Meanwhile, Nick was coping with the loss of a grandparent. None of these vignettes offered significant amounts in the way of genuine depression, and by and large, the majority of participants detected that their respective problems were not severe. A mere 6% of participants indicated that Mandy was depressed. Similarly, none of the participants indicated that Jade was depressed, and only 3% of them assessed Nick to be depressed. This finding offers support for the overall level of mental health literacy of the sample as it pertains to adolescent depression. Moreover, in comparison to the Australian participants in Burns and Rapee (2006), the American sample fared somewhat better: They found that, though none of their participants found Jade to be depressed, 11% and 9% of teenagers in their study did relate Nick and Mandy, respectively, to be depressed.
The participants in the present study demonstrated significantly more concern and anticipated a longer recovery period for the students in the depressive vignettes than in the non-depressed vignettes. In our study, a significant difference was accurately found in estimated recovery time.
The average duration of an initial depressive episode is eight months when no treatment is received (Brent & Birmaher, 2002). These findings add support to the conclusion that the sample possessed a considerable level of literacy. Given the fact that, to our knowledge, this pilot study is the first to assess mental health literacy for adolescent depression among American teenagers, no point of comparison exists. With this point in mind, the finding was relatively surprising. The adolescents in the present study were astute in their detection, concern, and estimated time of recovery, which could be related to a knowledge set based on their classroom education or acquired in other ways (i.e., having a friend who was depressed). Regardless of the mode of acquisition, the adolescents in this study offered greater concern for the fictitious students in the midst of a depressive episode and estimated their recovery more accurately than those students in the non-depressed vignettes.
It was mildly surprising that, unlike Burns and Rapee (2006) and Gifford-May (2002), no significant difference was found in regard to gender and mental health literacy. Burns and Rapee found that girls “clearly demonstrated” higher literacy in their abilities to not only correctly label the depressed vignettes, but also in their expression of greater concern over the students in those same vignettes (p. 232). One point of speculation on their part dealt with the higher levels of depression experienced by young women in later adolescence (Lewinsohn, Rohde, & Seeley, 1998). However, given the absence of significant differences in gender within the sample in the present study, it raises the possibility that young men in the U.S. are more insightful regarding adolescent depression than anticipated.
Burns and Rapee (2006) indicated that the primary reason for raising the mental health literacy of adolescents “is to increase the likelihood that young people can access the most appropriate help when needed” (p. 233). Taken from combined data from 2005 and 2006, an estimated 12% of American youth aged 12–17 obtained professional help for emotional or behavioral problems, and females were more likely than males to receive professional help (Office of Applied Statistics, 2008). However, the literature points to the fact that many other teenagers in need of mental health assistance for various disorders do not receive it. In fact, a mere 39% of those adolescents suffering a depressive episode receive treatment (Office of Applied Statistics, 2009).
The recommended sources for help in our sample were family and counselor, respectively, for Tony, and family and psychiatrist, both at equal percentages, for Emily. For the vignette of Emily, counselor ranked sixth of the nine helping sources. This finding is in contrast to the real-world conditions where nearly 60% of those teenagers with depression in 2007 saw or talked to a counselor in their treatment (Office of Applied Statistics, 2009).
Though the reasoning behind the choices of the helping sources was not sought, the selections lead to intriguing possibilities. First, in the case of Tony, the primary helping source was family, despite information in the vignette that the family system was deteriorating over a parental separation. Even if that played no role in the participants’ responses, the choice of family in soliciting help is striking in that parent-adolescent conflicts increase during early adolescent years (Laursen, Coy, & Collins, 1998). Suicidal adolescents reported difficulty in communicating with parents, tremendous stress in their home life, and a distressed relationship with one or both parents (Bostik & Everall, 2006). However, this finding is consistent with a qualitative study (McCarthy, Downes, & Sherman, 2008) pointing to beneficial parental partnerships that developed during depressive episodes and were instrumental in the teenager getting professional help. Counselor, the second recommended helper choice in the vignette of Tony, may not be as surprising. The school from which the data were collected does have a staff of professional school counselors, and this finding may speak to the participants’ level of comfort with counselors.
The topic of recommended helper was much different in the vignette of Emily, as the choices were much more equal in terms of the percentages. The selection of psychiatrist as the second recommended helper may point to the participants’ perception of the potential for harm and their connotation that a physician with mental health expertise and prescription privileges was needed. In a similar vein, the designations of psychologist and professional were closely behind psychiatrist in recommended helpers, again suggesting the participants’ notion that highly trained professionals who likely have a doctoral degree were needed to aid Emily. This finding mirrors recent research, as 27% of those adolescents having a depressive episode saw their family physician or a general practitioner. Roughly the same number sought help from a psychiatrist or psychologist (Office of Applied Statistics, 2009).
Surprisingly, friends were the third most common choice of helper in a case of a student marked by suicidal ideation. With the potential for harm in this student, friends may not be the best source for initial help. However, participants in the present study may have thought that friends would be supportive during an emotionally difficult period. Finally, the lower ranking of the counselor designation may be connected with a perception that a counselor is sought for less complex difficulties.
Burns and Rapee (2006) found that counselor and friend were the two primary overall recommended sources of help. In regard to counselors, they noted that this finding may be reflective of the “access and familiarity” that adolescents in many Australian schools possess with this type of professional (p. 233). Overall, however, the participants in their study offered far lower rankings of a psychologist, professional, or psychiatrist as a source of help in the depressed vignettes. This finding could point to a familiarity by American teenagers with medical professionals, particularly with the prevalence rate of medication prescribed to this population in the U.S. compared to European countries (Levin, 2008).
Limitations
Limitations are clearly evident in this study. As previously noted, the small sample size that is consistent with pilot studies restricts generalizability. The sample size also may have been composed of more sophisticated students in mental health, as many students in the sample were enrolled in a psychology class. Burns and Rapee (2006) pointed out that the vignette-based instrument of the Friend in Need Questionnaire is consistent with the manner in which other mental health literacy studies have been conducted. However, they added, “The extent to which such data can be translated into what actually is likely to happen in the real world is unclear” (p. 234). They also noted that a subsequent challenge for research in this area includes the development of research modalities that examine literacy in a naturalistic setting, such as interviews with adolescents. This suggestion connects to Dundon’s (2006) call to bring forth the “voice of the adolescent” that has been lacking in the research on adolescent depression (p. 384).
Implications
This pilot study represents a point of entry in studying American teenagers’ mental health literacy in regard to teen depression. Participants in this study showed the ability to correctly differentiate depressed vignettes from non-depressed vignettes and, in their assessment, indicated relevant symptoms of depressive symptoms faced by adolescents. Overall they also expressed sources of help that varied upon the perceived degree of seriousness of the difficulties. The outcomes offer implications regarding the potential benefit of including adolescents in a more direct way when providing outreach or offering services. They demonstrated an accurate understanding of when more intense levels of care could be beneficial.
The study produced results that also warrant further exploration of the relationships between youth and parents during adolescence. Although this developmental period can be marked by tumultuous relationships between them, there may be wisdom in providing communication skills to strengthen such relationships. Such efforts could result in more disclosure of depressive symptoms to parents, which may expedite the process of getting help as opposed to sharing such struggles only with peers. In addition to implications for teens and parents, this research can help shape additional studies in expanding the understanding of literacy.
Future research calls for additional mental health literacy investigations marked not only by larger sample sizes, but also by an in-depth investigation of adolescents of various racial/ethnic differences within the sample. Higher rates of adolescent depression have been found in youth of Latino descent (Guiao & Thompson, 2004; Twenge & Nolen-Hoeksema, 2002), for instance, and it would be important to evaluate the mental health literacy levels among respective backgrounds. With teenage depression being a pressing matter in adolescent mental health, the domain of mental health literacy in regard to this disorder is a vital one that merits additional research.
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John McCarthy, NCC, is a Professor in the Department of Counseling at the Indiana University of Pennsylvania (IUP). Michelle Bruno is an Assistant Professor in the same department at IUP. Teresa E. Fernandes, NCC, is a counselor at the Meadows Psychiatric Center, Centre Hall, PA. Correspondence can be addressed to John McCarthy, Indiana University of Pennsylvania, Department of Counseling, 206 Stouffer Hall, Indiana, PA, 15705, john.mccarthy@iup.edu.
Sep 2, 2014 | Article, Volume 1 - Issue 1
Richard A. Wantz, Michael Firmin
Numerous sources of information influence how individuals perceive professional counselors. The stressors associated with entering college, developmental differences, and factors associated with service fees may further impact how college students view mental health professionals and may ultimately influence when, for what issues, and with whom they seek support. Individual perceptions of professional counselors furthermore impress upon the overall identity of the counseling profession. Two hundred and sixty-one undergraduate students were surveyed regarding their perceptions of professional counselors’ effectiveness and sources of information from which information was learned about counselors. Overall, counselors were viewed positively on the dimensions measured. The sources that most influenced perceptions were word of mouth, common knowledge, movies, school and education, friends, books, and television.
Keywords: professional counselors, perceptions, counselor effectiveness, professional identity, undergraduates
Perception is not reality, but perception is nonetheless a very cogent relative to how humans come to understand reality. Moreover, perception tends to drive behavior and decisions made by consumers. In the present context, we are interested in how college students come to perceive human service providers across a number of variables. The constructs explored are not novel, as this genre of research has been assessed in decades past (e.g., Murray, 1962; Strong, Hendel, & Bratton, 1971; Tallent & Reiss, 1959; West & Walsh, 1975). However, we believe the topic warrants refreshed attention, particularly with the professional licensure acquired among all human service professions: psychiatrists, psychologists, counselors, marriage and family therapists, social workers, and psychiatric nurses.
The media tends to exert a cogent effect on students’ perceptions across multiple life domains, including human service professionals (Von Sydow, Weber, & Christian, 1998). Students also are affected by other information sources such as previous experiences with their high school (guidance) counselors, personal therapy, clergy, family doctors, parental influence, and input from peers (Tinsley, de St. Aubin, & Brown, 1982). Students’ perceptions of human service providers also may be affected by various campaigns, typically receiving information-influence from multiple sources that actively attempt to shape their perceptions of mental health services’ value and efficacy (Hanson, 1998).
Some human service professions have been more aggressive in how they advocate their service value to the public. Fall, Levitov, Jennings, and Eberts (2000) note that psychiatrists and psychologists generally have dwarfed counselors’ efforts at advocacy. Counselors, as a profession, have struggled significantly with their own identity (Garrett & Eriksen, 1999; Eriksen & McAulife, 1999), which likely affects this phenomenon. That is, if one’s identity is unclear to the respective professionals, then probably it will negatively affect its status among the laity (Gale & Austin, 2003). Psychology generally has lagged behind psychiatry in terms of the public’s professional perceptions (Webb & Speer, 1985), although Zytowski et al. (1988) reported that people frequently confused the terms psychiatrist and psychologist relative to function. Counseling psychologists also often seem to be confused with professional counselors in the public’s understanding (Hanna & Bemak, 1997; Lent, 1990).
Social work has existed as a vocation for over a hundred years. Kaufman & Raymond (1995) reported that the public’s awareness of the profession’s perception was somewhat negative in their survey sample. LeCroy and Stinson (2004) and Winston and Stinson (2004) likewise found individuals in their particular sample to be relatively knowledgeable regarding social workers’ responsibilities, although reported attitudes were more positive than those reported by Kaufman and Raymond. This partly may be due to the fact that respondents reported more favorable perceptions of social workers as helping those needing avocation than they did for social workers as therapists. Sharpley, Rogers, and Evans (1984) suggest that marriage and family therapy, as a profession, is relatively cryptic to the general public. That is, people generally deduce what such human service personnel do, as indicated by the title, but do not have as much first-hand knowledge or experience with such professionals as they do with counselors, social workers, psychologists, and other professionals.
Ingham (1985) notes that a helping profession’s overall image affects clinicians in that profession relative to their abilities in helping clients to utilize their services. This conclusion makes logical sense in that consumers’ confidence in the care provided is subjective and highly influenced by psychological variables, such as idiographic perceptions. Attempts at educating the public regarding an apt understanding of what a human service profession has to offer has shown various levels of effectiveness (Pistole & Roberts, 2002). Nonetheless, Pistole (2001) also notes that the general public finds the distinctions among the various human service providers to be bewildering. In short, without periodic reminders, the public’s image of various human service personnel may reconverge in a fog of misperception.
Since many individuals have never experienced the services of mental health clinicians, often their perceptions are based on reports or intuitively acquired opinions. For example, Trautt and Bloom (1982) report that fee structures affect perceptions of status and effectiveness provided by clinicians. The basic understanding, of course, is that the more expensive the treatment, the higher its perceived value and professional status. That, of course, can result in self-fulfilling prophesies—with people paying more money expecting more from therapy—and experiencing better success rates. We are unaware of any studies where clients were randomly assigned to professional therapists and (systematically) charged varying pay rates. Such a study, controlling for fee structures, might yield some valuable data to the present discussion regarding how the public perceives the value of respective human service professionals.
Beyond the public’s general perceptions on this topic, however, we are particularly focused on students’ perceptions. Hundreds of thousands of students annually utilize the services of university counseling centers, as well as private practice therapists and other human service agencies. With the added stress of academics, social pressures, being away from home for the first time, transitioning from teenage to adult responsibilities, dating, drinking alcohol, and other similar stressors, having apt utilization of psychotherapeutic services is paramount for college students. Turner and Quinn (1999) suggest that college students’ perceptions differ from the population-in-general, and research data from one group may not accurately generalize to the other.
Notwithstanding obvious developmental differences between college students and more mature adults from the general population, counseling students may not pay (directly, out of pocket) for the services available to them. Campus counseling centers, for example, typically receive funding from tuition or generic student fees, rather than students paying direct dollars for the services. Additionally, most full-time students remain on their parents’ medical insurance which also offsets financial costs involved in private practice expenses. In short, cost of services seems to be a significant variable for the general population (Farberman, 1997) that may not load with the same degree of importance vis-a-vis college students. Additionally, titles (such as “doctor”) may not have as much bearing with the general public (Myers & Sweeney, 2004) as they do with college students who routinely use such nomenclature with professors and others on a daily basis. In short, while we accommodate research findings that compare the various mental health professionals as perceived by the general public (e.g., Murstein & Fontaine, 1993), we also treat the results with some degree of prudence and believe college students represent a distinct population worthy of particular focus and exploration.
Gelso, Brooks, and Karl (1975) conducted a study that was similar in some respects to our present one. They surveyed 187 students from a large eastern university with a sample of 103 females and 84 males. Subjects were asked to rate perceived characteristics of various human service professionals, including high school counselors, college counselors, advisers, counseling psychologists, clinical psychologists, and psychiatrists. They found that overall college students did not report significant differences relative to professionals’ personal characteristics. However, they did report differences among the human service providers relative to their perceived competencies in treating various hypothetical presenting problems.
In the 30 years subsequent to this study, we are interested in how student perceptions have changed over time. Additionally, the Gelso, Brooks, and Karl (1975) study did not account for students’ perceptions of social workers, marriage and family therapists, or psychiatric nurses. Given the present milieu, we are more interested in these professionals than the categories of school counselors or advisors. Additionally, we also chose to combine the categories of counseling and clinical psychologists into the generic grouping, “psychologist.” The specific questions asked of students also differed in our present study. However, the general tenor of the two studies is similar—and we believe the updating of knowledge in this area has significant importance for those working with college students in various capacities and milieus.
Warner and Bradley (1991) also conducted a study similar to the present one. Their participants included 60 men and 60 women who were undergraduate college students enrolled in a University of Montana introductory psychology course. They assessed student perceptions of master’s-level counselors, clinical psychologists, and psychiatrists on multiple variables. Findings included students reporting their perceptions of counselors as possessing more caring-type qualities. Psychiatrists were seen as most able to address severe psychopathology and psychologists were viewed as more academics and researchers than as therapists.
Method
Participants
We surveyed 261 students from three sections of a general psychology course for this study. The course was selected, in part, because it is included in the university’s general studies core curriculum. Consequently, it represented a relatively wide range of majors from the student body and included students from freshman through senior status. The sample was taken at a selective, private, comprehensive university located in the Midwest with a study body of approximately 3,000 students. It included 167 women and 92 men with ages ranging from 17 to 55. The students were mostly Caucasian with 9% identifying themselves as ethnic minorities representing 34 states.
Procedure
The instrument was first pilot tested (Goodwin, 2005) to a group of undergraduate students at a regional state university prior to utilizing it in the present research project. Modifications were made in clarifying ambiguous terminology, instructions, and time to complete. Due to practical considerations, the instrument was designed to be completed in about one-half of a normal class period. The survey was administered during a normal class period with students having the option to participate at will without reward or penalty for doing so. Two students chose not to complete the surveys for undisclosed reasons.
The survey queried students regarding their perceptions of human service professionals (HSP), taking about 20–25 minutes to complete. Anonymity was provided to all students regarding answers to all items. Questions were asked about the overall perceived effectiveness of various HSPs, for which types of problems they might recommend various HSPs, and overall perceptions about the various HSPs. Although obviously many types of HSPs exist, this particular survey focused on psychiatrists, psychologists, professional counselors, marriage & family therapists, social workers and psychiatric nurses. In order to control for order effects as potential threats to internal validity (Sarafino, 2005), the various HSPs were presented in random order each time they appeared throughout the survey. The amount of data collected from the survey was relatively substantial. However, given the practical number of journal pages that can be reasonably devoted to presenting the information, along with our desire to comprehensively address perceptions of counselors, the present article addresses only this particular segment of the data collection.
Results
We organized the survey’s results in terms of the counseling services utilized, how effective students perceived counseling to be, for what types of problems or issues counselors are thought to be apt, how students came to view their perceptions of professional counselors, and qualities thought to characterize professional counselors. All percentages are rounded for clarity of reading and presentation, except where percentages fall below 1%.
Types of Services Utilized
At the end of the questionnaire, students were asked to confidentially self-disclose whether or not they had received services from a HSP. The question was placed at the end in order to have students already somewhat acclimated to HSPs and to have them somewhat more comfortable with the world of different types of HSPs. Of those answering the question, 28% of the participants indicated having received assistance from a HSP prior to completing the survey. The specific question asked whether or not students received prior professional assistance regarding personal, social, occupational or mental health concerns. About 3% of all the participants chose not to answer this particular question. However, of the 28% only 1% indicated that they did not know the profession of their HSP, indicating that most of the respondents who previously had utilized HSP services were aware whether the professional they saw was a counselor, psychologist, social worker, etc. Relatively few (
States possess a variety of titles by which professional counselors can or should be called (Freeman 2006). Consequently, rather than asking students simply to identify whether or not they had previously utilized the services of a “counselor,” we specified some types of counselors they may have seen. These included professional counselor, pastoral counselor, addictions or chemical dependency counselor, rehabilitation counselor, clinical mental health counselor, professional clinical counselor, and school guidance counselor.
Of the 28% of students who indicated they had previously utilized HSP services, three particular types of counselors were more prominent than the others. Namely, 16% indicated having seen a school counselor, 11% saw a professional counselor, and 9% saw a pastoral counselor. Relatively few students indicated having seen a rehabilitation counselor (0.4%), an addictions counselor (0.8%), or a mental health/clinical counselor (3%).
Perceived Overall Effectiveness
Students were asked to indicate how effective they believed professional counselors are overall. The particular question was worded as follows: In general, what is your opinion about how overall effective professional counselors would be with helping a mental health consumer? The options provided, with descriptors in parenthesis, were 1 (Positive), 2 (Neutral), 3 (Negative), and 4 (Unsure or don’t know). The intent of the question was to capture the gestalt of students’ thinking regarding professional counselors, prior to probing more deeply vis-a-vis types of counselors and for which kinds of issues they might find effective interventions.
Only 3% of the participants indicated having no opinion regarding this question. Another 3% indicated viewing professional counselors negatively. A total of 28% of the participants indicated having neutral views regarding counselors’ overall effectiveness. Sixty-six percent of the participants indicated having a positive view of professional counselors.
Types of Issues for Which Counselors Are Adept
Students were asked to identify for what types of issues they believed professional counselors would be particularly adept. They were provided with 12 different issues and asked to rate them as Yes (I would recommend a professional counselor for this situation), No (I would not recommend a professional counselor for this situation), or NS (Not sure, not familiar). Relatively few students skipped these questions or chose not to respond (range=0.8% to 3.4%). In other words, response rates were consistently high for these questions, obviously adding to the interpretation process. The same is true with students indicating that they were unsure or unfamiliar. Namely, on average 4% or so of students indicated being unsure for the situations presented (range=1.9 to 6.9). Results showed three clusters of participants’ responses.
The first cluster had four prominent responses, exhibited by 80% or more of the respondents—they involved college issues, academic problems, depression, and career counseling. A total of 91% of the participants indicated believing a professional counselor would be effective for helping college students who report homesickness, roommate problems, and falling behind with class assignments. A similar number (88%) believed that a professional counselor would be effective with a depressed individual who reports feeling sad and empty most days, finds little pleasure in daily activities, has insomnia, and is unable to concentrate. Comparable responses (83%) were seen for professional counselors addressing a young person with adequate intellectual capacity, but a pattern of academic problems (e.g., failing grades and significant underachievement). Finally, 80% of participants indicated that a professional counselor would be effective for a person reporting job dissatisfaction and uncertainty about career choices.
The next cluster of responses involved issues of family dysfunction, substance abuse, and attention-deficit hyperactivity disorder (ADHD). Seventy-six percent of participants indicated feeling that professional counselors were effective for a family unit reporting communication problems, negative interactions, criticism, and withdrawal among family members. For cases when a person self-administers and abuses drugs that results in impairment of daily academic, occupational and social functioning, 73% of the respondents in our survey believed a professional counselor would be effective. Sixty-seven percent of participants indicated that a professional counselor would be effective when a person with persistent patterns of inattention and hyperactivity-impulsivity that interferes with academic, occupational, and social function.
The final cluster of participants’ responses involved issues of personality assessment, intelligence testing, psychotic symptoms, physical disabilities, and mental health evaluations. Just over half (53%) of the participants indicated that professional counselors were apt for working with a person who needs personality assessment. Forty-four percent said that a professional counselor would be effective for a person with psychiatric symptoms who experiences delusions, hallucinations, disorganized speech, and is frequently incapable of meeting ordinary demands of life. When asked if an unemployed individual with a physical disability seeking employment would be a target source for a professional counselor, 43% answered affirmatively. Only 40% of participants indicated that a professional counselor would be effective in helping a client who needs a comprehensive mental health evaluation. Fewer (37%) indicated that intelligence testing was germane for a professional counselor.
Table 1
Sources of Perceptions about Counselors
Another line of inquiry addressed the identified sources by which students indicated they developed their perceptions about counselors. In other words, they told us about the factors that influenced them the most regarding how they came to think about professional counselors. The options from which to choose included books, common knowledge, friends or associates, HSPs, insurance company or carrier, Internet, magazines, physician or nurse, movies, newspapers, personal experience, school and education, and television. Only 2% of the participants declined to participate in this section of the survey or marked “none.”
Instructions asked students to complete this section in two steps. First, they were to indicate (by checking a corresponding box) whether or not they learned about a professional counselor from the identified source. Students were told they could select multiple sources. In the second step, they were asked to rate whether the information about the HSP was 1 (positive), 2 (neutral) or 3 (negative). Only 2% of the students marked a box described as “other,” indicating that the categories provided were relatively comprehensive. Results from this portion of the survey showed the data falling into three clusters. The two clusters representing extreme scores were of relatively equal size, while the third or middle was small (only two sources in the category).
The first cluster showed the following items as being relatively influential in how students came to understand the roles of professional counselors: common knowledge (84%), movies (63%), school and education (60%), friends (55%), books (49%) and television (44%). The middle cluster included personal experience (27%) and Internet (24%). The finding that 27% indicated personal experience to be influential is consistent with the demographic portion of the questionnaire where 28% of students said they had personal contact with a HSP prior to completing the survey. The third cluster comprised those sources that participants said were relatively non-influential in generating their perceptions of professional counselors. They included magazines (20%), physician or nurse (18%), newspaper (13%), HSPs (10%) and insurance companies (5%).
Results from the second step in the survey are more difficult to summarize. The data was more dispersed than the first step, although three clusters inductively emerged. Some items received few responses, as they were not selected very frequently in step one. The percentages listed do not add up to 100% for each item because the remaining percentage for each item is accounted by students who did not provide answers for that item. For example, if an item had 1% positive, 1% neutral, and 1% negative, then 97% of the participants simply left the question blank.
The first were items where students indicated that professional counselors were as viewed mostly positive. These included school and education (43% positive, 13% neutral and 3% negative), friends (38% positive, 10% neutral and 6% negative), books (30% positive, 17% neutral and 2% negative), personal experience (17% positive, 7% neutral and 3% negative), physicians (10% positive, 6% neutral and 2% negative), and HSPs (8% positive, 0.8% neutral and 0.8% negative).The second cluster comprised items that were rated as being mostly neutral and with relatively few positive indicators. These included: movies (14% positive, 28% neutral and 19% negative) and television (13% positive, 25% neutral and 6% negative). The third cluster showed a relative spread of responses, although there were few negatives in each category. They included: common knowledge (38% positive, 42% neutral and 3% negative), magazines (10% positive, 8% neutral 3% negative), Internet (10% positive, 12% neutral and 1% negative), newspapers (5% positive, 6% neutral and 3% negative), and insurance companies (0.8% positive, 2% neutral and 2% negative).
Perceived Counselor Qualities
The final portion of the questionnaire addressed how participants viewed various professional counselors’ characteristics. Students were asked to identify statements that they believed to be true about professional counselors, based on their overall knowledge of them. Options included competent, can be in independent private practice, diagnose and treat mental and emotional disorders, doctoral degree required to practice, intelligent/smart, overpaid, prescribe medication and trustworthy. Consistently, only 1% of the participants chose not to respond to this portion of the survey, making interpretation for this section relatively straightforward. The findings fell neatly into two categories: characteristics counselors presumably possess and those they do not.
Characteristics that students believed professional counselors possess include being competent (81%), independent private practice (81%), trustworthy, (79%), and intelligent/smart (77%). Contrariwise, participants identified the following as not characterizing professional counselors, as indicated by the relatively low percentages of marked responses: doctorate required (30%), diagnose and treat mental disorders (22%), overpaid (16%) and prescribe medications (5%).
Discussion
Given the formation and advancement of the American Mental Health Counseling Association (AMCHA), the introduction of state licensure laws that specifically use mental health counselors as formal nomenclature (Freeman, 2006), and particular certifications that have been offered in clinical mental health counseling, we were somewhat surprised that only 3% of the students who had previously used HSP services identified doing so with clinical mental health counselors. Of course, they may have been confused with names, but to the degree that accurate reporting occurred, the numbers were relatively low compared to other types of counselors.
Obviously, school counselors are very important relative to how students perceive professional counselors. They accounted for the largest portion of users (16%). First impressions are not always necessarily lasting impressions. However, they are cogent and school counselors may set the tone for how these students, for the rest of their lives, perceive others using the word “counselor” in their professional titles. This sentiment was illustrated in qualitative research findings by Wantz, Firmin, Johnson, and Firmin (2006).
Three times as many students indicated having seen a pastoral counselor than a mental health counselor (9% and 3%, respectively). Obviously, we do not know if some students actually meant that they saw an ordained clergy person for personal issues, considering this person to be a pastoral counselor, since they received counseling from him/her and the person was clergy. However, assuming accurate reporting, it suggests that graduate training programs should consider giving additional attention to this domain of counseling. Although courses in pastoral counseling sometimes are seen in religiously-oriented universities (e.g., seminaries, Catholic or Christian colleges), the apparent popularity of their use by students, suggested by the present research, provides evidence that more widespread attention to pastoral counseling is warranted.
Students’ overall perception of professional counselors as being effective is heartening. Particularly welcoming is that only 3% viewed counselors negatively. Social psychology research (Myers, 1994) has shown that a few negative, public incidences can have overshadowing effects on a group’s overall positive characteristics. Fortunately for professional counselors, whatever data might feed negative overall impressions seems to be relatively dormant for students in the present sample.
A general continuum emerged vis-a-vis students’ perceptions of what types of issues are most germane for professional counselors to address. Namely, high responses were provided for general, developmental life issues such as academic problems, depression and career counseling. Moderate responses were provided for problems where direct brain-behavior connections are involved such as ADHD or drug counseling. The lowest responses were provided for types of situations where assessment is warranted, such as personality or intellectual assessment and mental health evaluations. These findings are consistent with overall perceptions that students do not think of counselors in terms of being clinical mental health professionals, but rather as more generic, trained counselors. If the field wishes to advance itself toward the direction of diagnosis, assessment, and treating psychopathology, then data from the present survey would suggest that efforts should be redoubled.
Not all media sources appear to be equal in influencing students’ perceptions of professional counselors. For example, newspapers (13%), magazines (20%), and the Internet (24%) were relatively inconsequential when compared to movies (63%), books (49%) and television (44%). Unfortunately for professional counseling organizations, the most potentially influential sources also happen to be the most expensive ones to target. Nonetheless, if organizations such as the American Counseling Association (ACA), American Mental Health Counselors Association (AMCHA), and the National Board for Certified Counselors (NBCC) are going to impact students’ thinking, then they should target the most efficacious sources. It could be, of course, that the reason newspapers, magazines and the Internet were so relatively non-influential is that few inroads have been attempted in these domains. Advertising in university newspapers, posting and promoting user-friendly web sites, and generating informative articles in popular magazines simply may be an important need for professional counseling advocacy at this time.
In a separate study under development, using qualitative methodology, we are attempting to better flesh-out some of the details relating to these sources of impact on students’ perceptions of professional counselors, particularly the concept of “common knowledge.” Although not surveyed in this study, an influential source proved to be word-of-mouth in perception formations regarding counseling. That is, influences of school, friends, personal experience, physicians, and HSPs most likely have some type of personal connections tied to the medium. Evidently, there is some truth to the adage that word-of-mouth is the best means of advertising—assuming, of course, that the messages being relayed are positive.
In the perceived counselor qualities portion of the survey, it was somewhat disheartening that comparatively few (22%) students indicated they saw professional counselors as competent to diagnose and treat mental disorders. This finding was consistent with other data throughout the survey. Namely, students generally view counselors as professionals who address relatively normal, human development issues rather than psychopathology or more severe disorders requiring assessment, diagnosis and treatment. Again, if the counseling profession wishes to move in the latter direction, then findings from the present research suggest that there is some distance to go. Early acquired school counselor perceptions tended to initiate students’ mindsets regarding what counselors do and they seem not to have moved far from those early perceptions.
In summary, we believe that the present study is a strong first step in a line of needed research regarding just how people come to understand counselors. The findings here do not dictate any action on behalf of professional counseling organizations. However, we believe that the findings indicate in which directions the winds of student perceptions are blowing—and that is data which should be considered when making policy decisions. If counselors are going to move to new, future levels of excellence in terms of public perception, then paying attention to this type of data and giving it due consideration is an important initial component.
Limitations and Future Research
All good research studies report limitations (Murnan & Price, 2004) and we indicate four of them here. First, while our sample had several strengths, including adequate size (Patten, 1998), high response rate (Stoop, 2004), and lack of incentives/bribes for participation (Storms & Loosveldt, 2004), it was taken from a single locale. Some compensation exists, such as students coming from 34 states and the relatively broad cross-section of college majors represented. However, future research in this domain should assess students from a wider variety of institutions such as research universities, state universities, and liberal arts colleges—as well as from diverse locales in the country in order to enhance the study’s external validity (Cohen & Wenner, 2006).
Second, our study had relatively low representation from minority students. This simply was an artifact of the university where the data was collected. Specifically, minorities comprised only 6% of the student body population. Further research should contain samples with larger representations of minority individuals. Additionally, replicating this present study with all minority students would provide an interesting comparison among many points of investigation.
Third, some of the items queried were selected a priori. While we believe them to be of interest and germane to our purposes, future research should broaden questionnaires to include questions that are derived empirically from the research literature. Also, organizations such as the Council for Accreditation of Counseling and Related Educational Programs should provide input vis-a-vis questions that directly would enhance their efforts in counselor education preparation. The same is true with potential input from NBCC and ACA as they market professional counselors to the general population as well as college students.
Fourth, in retrospect there are two particular changes we would have made to the survey instrument. One is that we would have added a Likert-scale to the first question, querying the perceived overall effectiveness of counselors. While we believe that rating professional counselors with three choices was useful—and we would keep the question—we also would recommend future researchers add a Likert-scale question that is anchored with descriptions, but to which numeric interval-scale values could be assessed. Second, looking back on our questionnaire, we would have asked how many students saw more than one HSP. That is, did they use more than one type of human service professional’s services (e.g., they saw both a rehabilitation counselor and a school counselor). Accounting for multiple uses within the same clientele could provide potentially useful data.
Future research should take the present study and apply it to the population in general. That is, we produced what we believe to be fairly apt representations of perceptions among students—but they do not represent the population at large. Obviously, college students have unique features of adult development that are not necessarily shared by older adults (Foos & Clark, 2003). The very low reported influence that health insurance companies have on college students’ perceptions is one of many examples of where student ideations and those of more middle-aged adults might differ.
And finally, qualitative research is needed in this area. A prime value of questionnaires, such as the present one, is that more voluminous amounts of data can be collected—providing breadth of understanding (Gall & Borg, 2003). Such research also tends to answer “how many” or “what” types of questions (Hittleman & Simon, 2003). Thicker descriptions are needed to help flesh-out some of the details on which survey research was only able to skim. Answers to some of the “why” and “how” questions that the present findings raise can best be answered with follow-up qualitative research methodology (Flick, 2002).
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Richard A. Wantz, NCC, is a Professor at Wright State University, and Michael Firmin, NCC, teaches at Cedarville University, both in Ohio. Correspondence can be addressed to Richard A. Wantz, Wright State University, Department of Human Services, 3640 Colonel Glenn Highway, Dayton, OH, 45435, richard.wantz@wright.edu.
