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.
