A Phenomenological Investigation of Master’s-Level Counselor Research Identity Development Stages

This study explored counselor research identity, an aspect of professional identity, in master’s-level counseling students. Twelve students participated in individual interviews; six of the participants were involved in a focus group interview and visual representation process. The three data sources supported the emergence of five themes. The authors describe the themes in terms of what students contributed to the following three stages of research identity development: stage one, stagnation; stage two, negotiation; and stage three, stabilization. Implications for counselor education programs, counselor educators and counseling students are explored.

Keywords: phenomenological investigation, research identity, counseling students, focus group, counselor education

Counselor professional identity is complex and involves various developmental tasks that are dependent on both interpersonal and intrapersonal interactions (Auxier, Hughes, & Kline, 2003; Reisetter et al., 2004). According to Nugent and Jones (2009), “counselor professional identity is the integration of professional training and personal attributes within the context of the professional community” (p. 21). The context of a professional community may be understood as the behaviors, thoughts, actions and beliefs to which individuals within a professional community typically ascribe. All dimensions of counselor professional identity significantly impact how individuals behave, act and think within the context of their professional role (Gibson, Dollarhide, & Moss, 2010). The understanding of attitude, behavior and belief norms within the profession of counseling has been extremely important in assessing and stimulating the development of professional identity (Gibson et al., 2010).

Many variables influence the process of identity acquisition and maintenance. Erikson (1994) stated that “the process of identity formation emerges as an evolving configuration” (p. 125). While knowing that counselor professional identity formation never stops, one must consider how to intentionally and effectively guide the process. Kozina, Grabovari, De Stefano, and Drapeau (2010) demonstrated that practitioner identity evolves through deliberate tasks and actions aimed at helping counseling students develop particular attitudes, behaviors and beliefs. In addition to purposeful tasks, Gibson et al. (2010) asserted that the professional identity process occurs in stages and unique needs exist at different stages.

In recent years, research has become an important focus of the professional counseling community. The American Counseling Association Code of Ethics (2014) has emphasized the importance of counselors utilizing research to best inform their practices. Specifically, counselors who do not use techniques, procedures and modalities that are grounded in theory and have an empirical or scientific foundation must define the techniques as unproven or developing, explain the potential risks and ethical considerations of using such techniques, and take steps to protect clients from possible harm. This particular aspect of the ethical code introduces a unique aspect of counselors’ beliefs, behaviors and attitudes concerning empirically-based practice, which counselors need to consciously recognize as a part of counselor professional identity—research identity (RI).

The definition of professional identity in counseling has historically captured more of the practitioner role. The concept of a scientist–practitioner identity has been frequently used within the field of psychology. Researchers define the identity of a scientist–practitioner as “regularly consuming and applying research findings in their practice; following a scientific methodological way of clinical thinking and practice; regularly evaluating their practices; conducting research and communicating findings; collaborating with researchers to produce clinically meaningful research” (Lampropoulos, Spengler, Dixon, & Nicholas, 2002, p. 232). The scientist–practitioner identity may likely share common elements with the RI dimension of counselor professional identity.

As the concept of RI has surfaced, research has led to new ideas about counselors’ professional identity. Few researchers have attempted to define RI in the helping professions (Jorgensen & Duncan, 2015; Ponterotto & Grieger, 1999; Reisetter et al., 2004; Unrau & Grinnell, 2005). For doctoral counseling students, Reisetter et al. (2004) described the concept of RI as a mental and emotional connection with research, confidence in one’s ability to consume research, desire to conduct a magnitude of research in the future, and identification within the larger research community. In the field of psychology, Ponterotto and Grieger (1999) defined RI as “how one perceives oneself as a researcher, with strong implications for which topics and methods will be important to the researcher. Naturally, one’s RI both influences, and is influenced by, the paradigm from which one operates” (p. 52). Interestingly, Ponterotto and Grieger (1999) and Reisetter et al. (2004) both described the concept of RI without the use of references, highlighting the empirical attention still needed on the topic of RI.

In recent literature, Jorgensen and Duncan (2015) explored the meaning of RI in master’s-level counselors through a grounded theory approach. The authors suggested the following theory of RI:

(a) RI is considered an outcome that is initiated by the event of coming to understand what it means to be a counselor (professional identity); (b) RI is facilitated through the negotiation of internal facilitators, external facili­tators, faculty impacts, and beliefs about research; (c) RI is affected by the broader contexts of undergraduate major and area of specialization; (d) RI is enhanced by accepting fluid conceptualizations of research and professional identity; and (e) RI is manifested through research behaviors, attitudes toward research, and a level that symbolizes the various degrees of a student’s RI.

Based on their grounded theory, the authors offered a foundation for better understanding the concept of RI and suggested that future research explore the different levels of RI.

The purpose of this study was to focus on the dimension of research identity within the broader context of counselor professional identity, addressing gaps within the literature about the RI phenomenon. Counselors need a foundation for facilitating RI development. Also, counselors need a framework to fully understand the term and to apply previous findings more easily.

Method

The authors utilized a qualitative approach with a phenomenological framework to understand the phenomenon of master’s-level counselors’ RI. Researchers use a phenomenological approach to understand the subjective experiences of participants in relation to the topic under investigation (Creswell, 2013; Kopala & Suzuki, 1999). The authors examined the phenomenon and perspectives of 12 students who told stories about their RI and gave meaning to the different levels of experienced RI. The authors conducted individual interviews and a focus group to construct the meaning of levels of RI in multiple ways.

Researcher-as-Instrument and Potential Biases

Qualitative methodology requires researchers to be the instruments of investigation. Therefore, researchers must discuss their thoughts and feelings about the topic studied as a means of being transparent. The present authors conducted reflexive journaling throughout the study in order to minimize the impact of their biases on the data collection and data analysis processes (Hunt, 2011). The authors reflected in writing their thoughts and feelings about the topic, each interview, visual representations and the findings in scholarly articles during significant times in the research process.

Participants

Participants in the individual interviews and focus group were from two CACREP-accredited counseling programs accredited by the Council for Accreditation of Counseling and Related Educational Programs, and located in the Midwestern United States. Researchers conducted 12 individual interviews during this study. Of the 12 participants (nine female, three male), five specialized in school counseling and seven specialized in clinical mental health. Five participants were at the midpoint of their counseling program (i.e., had completed 12–30 credits), and seven were at the end of their program (i.e., in the process of internship or had graduated within the last 6 months). The average age of participants was 29.25 (age range = 24–44).

Six participants (four female, two male) were involved in the focus group, with two being involved in both an individual interview and the focus group interview. All focus group participants were at the midpoint of their training program (i.e., had completed 12–30 credits). Three participants specialized in school counseling and three participants specialized in clinical mental health counseling. The authors avoided involving several of the participants in both data collection points in order to create potential for new meanings around RI to be constructed.

Procedure

The participants were initially contacted via e-mail, phone or in person to determine their suitability for participating in this study. The authors e-mailed potential participants a letter of invitation that featured the criteria for participation, asking them to contact the investigators if interested in being a participant. The following criteria were used to select participants for the individual interviews: identifying as master’s-level counseling students with a school counseling or clinical mental health counseling focus, and at the midpoint or end of their training. However, the focus group interview only included students at the midpoint (i.e., had completed 12–30 credits) in their program.

Once participants were determined for both individual and focus group interviews, the participants completed a demographic sheet and consent form that described the purpose of the study and their rights as participants (i.e., ceasing participation at any point). Individual interviews lasted 35–60 minutes and were recorded via a digital voice recorder. The focus group lasted 60 minutes and also was recorded. Digital files were immediately uploaded to a password-protected laptop once the interviews and focus group were completed. In order to ensure confidentiality, each participant received a pseudonym and all data (i.e., digital recordings, typed transcripts) were password protected.

Data Collection and Analysis

The authors utilized the following three data collection points in this study: individual interviews, a focus group and a visual representation. During the individual interview, participants answered questions from a semistructured protocol as well as questions about two articles that they were asked to read prior to their interview. During the focus group interview, participants answered questions from a semistructured protocol and drew a picture of what they imagined (i.e., visual representation) when they heard the word research. Importantly, visual representations facilitated a deeper co-construction of meaning relating to the levels of RI. According to Pain (2012), visual methods in research can build a trusting relationship with and between participants, encourage discussion, and facilitate the expression of abstract ideas. Visual representation also “allows for the creation of new insights using art either as the starting point for creative thought generation or as the means by which new meanings in the research can be expressed” (Poldma & Stewart, 2004, p. 146).

The researchers critiqued the data through a process suggested by Moustakas (1994) in conducting a phenomenological study. Bracketing of personal thoughts and feelings was done prior to and after each interview in order to ensure greater potential for objectivity and accurate representation of the data. The data were transcribed and critiqued through a primary coding process, which captured the essence of most sentences in the transcription. Horizontalization was carried out by viewing each transcript and finding ideas that seemed important to the interviewees. The researchers entered each idea into a spreadsheet in order to examine elements that occurred most frequently during the interviews, deriving meaning units to capture the overall common experiences of participants based on their most frequently described ideas. The data were merged into themes described through narrative definition and via direct quotes from each interview, leading to a contextual description that clarified each meaning unit.

In the focus group, participants were asked to draw a picture of what they imagined (i.e., visual representation) when they heard the word research. Participants shared their visual representation with the group and gave meaning to the picture by providing a narrative, which was transcribed and merged with the other data to provide more meaning to the phenomenon.

Trustworthiness Procedures

The researchers utilized researchers’ epoche, member checking, prolonged engagement with the data, cross-checking data, triangulation and reflexive journaling as trustworthiness procedures during the data analysis. The first author sought transparence and credibility throughout the research process by bracketing thoughts and feelings associated not only with the broad topic (researcher epoche), but also with each interview and data analysis procedure (reflexive journaling). The first and second author met on a regular basis to examine their journal entries and cross-check entries with the results of the coding processes to ensure that participants’ unique experiences were represented and to reflect on the overall research process (Creswell, 2013). Further, participants provided feedback in the process of member checking by examining their transcriptions, open codes and quotes supporting the themes. The researchers encouraged participants to review and edit, if necessary, their transcriptions, themes and quotes. Triangulation was used by comparing and integrating data offered through individual interviews, the focus group and visual representation. During the process of converging findings from all data sources, the first author cross-checked and resynthesized information to create themes that captured the essence of what was being communicated through various data sources.

Results

The researchers established three stages of RI (i.e., stagnation, negotiation, stabilization) and five primary themes collapsed under each corresponding stage, with meaning assigned based on how participants experienced the different levels. According to Jorgensen and Duncan (2015), RI is experienced on a continuum with each master’s-level counselor allocating different levels to the researcher dimension of professional identity. The stages of RI established in the current study further clarified different points on the broad RI continuum described by Jorgensen and Duncan (2015). Specifically, this research revealed more about the lower (stagnation), moderate (negotiation) and higher (stabilization) levels of RI by examining the participants’ reactions to external facilitators, internal processes related to research, research behaviors, and beliefs and attitudes toward research.

The five primary themes included (1) external facilitators of lower levels of RI (e.g., messages from others, program elements, undergraduate education, professional standards); (2) external facilitators of higher levels of RI (e.g., messages from others, program elements, undergraduate education, professional standards); (3) internal facilitators of higher levels of RI (e.g., professional identity conceptualization, conceptualization of research, attitude toward research, beliefs about research, research behaviors); (4) internal facilitators of lower levels of RI (e.g., professional identity conceptualization, conceptualization of research, attitude toward research, beliefs about research, research behaviors); and (5) faculty as salient to the RI process (e.g., mentoring, talking about research, infusing research into courses, modeling research behaviors). The authors discuss the results through the broader categories of stages, using select examples of how primary themes describe each stage. Participants were given fictitious names in order to protect their confidentiality.

Stage One: Stagnation

The first level of RI was named the stagnation stage because participants seemed to be stagnating in the process of forming their RI. All participants expressed the realization that research is a part of their identity; however, participants in stage one seemed to do little with that realization. The primary themes connected to this stage included the following: internal facilitators of lower levels of RI, external facilitators of lower levels of RI and faculty as salient to the RI process.

Participants at stage one often described an internal state of confusion, dislike, avoidance of research and loyalty to their practitioner identity, and they articulated narrow definitions of research (i.e., internal facilitators of lower levels of RI). Participant Shelly provided a visual representation of her narrow definition of research and explained, “That is probably why I don’t like research, because I think of . . . the science guy going cross-eyed.” For Shelly, the word research stimulated a visual representation of a scientist and someone dissimilar to her. She described her conceptualization of a researcher by saying, “Ohhh, not me at all.” Another participant, B.D., highlighted components of confusion, dislike and avoidance:

As a researcher, I was more reinforced that I was terrible at it and that I didn’t like it and, most of the research . . . taught to the class was such a joke and the appraisal class . . . was really confusing for me because I don’t like numbers and I didn’t want to work with numbers and that was difficult along with the data entry. . . . I was taught the importance of [research] and somewhat understand what’s going on, but that’s probably it.

Kelsi discussed the dislike of research among individuals with lower levels of RI. She stated, “I think a lot of people, I hate to say it, are . . . like myself, they aren’t the biggest fans of research.”

Other internal facilitators of lower levels of RI were captured through participants describing a loyalty to their practitioner side. Dan stated, “I think from terms as a practitioner, . . . you could get caught up in spending too much time on research and not enough time working with clients or implementing the knowledge base that you have with clients.”

Participants in the stagnation stage also discussed messages from others in the counseling profession, program elements and undergraduate major (i.e., external facilitators of lower levels of RI). Rocky shared that undergraduate major and program elements were components of lower levels of RI:

[As an] undergrad, I had no clue what . . . the actual process of research . . . was. . . . I had no clue. . . . I don’t know if it can be required, but I think in the counseling program research should be required.

Kelsi supported the idea of undergraduate education being a major external facilitator: “To tell the truth, I’m not the biggest fan on all of that, maybe because of my background. I don’t have a psych background.” Additionally, Bob indicated that messages from others were a part of lower levels of RI:

I think the messages that I received were . . . important, but I don’t think it was ever clearly defined or expected, without looking for further professional development or working for a doctoral program . . . you want to research . . . the areas that you are not familiar with, but I don’t feel like that was ever clearly expressed. I know we are taught the research and research writing, but I just don’t think it ever transpired into once you are a professional in the field, this is what’s expected of you.

Lastly, participants often described faculty members as major contributors to lower levels of RI. Participants with low RI consistently described faculty teaching styles, silence around research, lack of modeling research behaviors, and lack of invitations to co-research and mentor students in research. Jackie described how faculty influenced her RI: “We weren’t really ever invited to take part . . . we were never invited . . . and it was really never talked about.” Nicole further emphasized the impact of messages from others as either directly stated or implied through behaviors:

I got the impression that they didn’t do research. . . . We didn’t really talk about [research] a lot. In internship when I went out into my school district, I don’t think anybody had been involved in research. I had two of them [faculty] that had been in the school counseling profession for about 20 years and I’m not sure if they did [research]
at all.

Stage Two: Negotiation

The second and moderate level of RI was called the negotiation stage because participants described having to negotiate their love–hate relationship with research. This stage seemed to be a transition stage, as participants described moving out of their lower level of RI due to having more confidence, realizing a need to take initiative and being mentored by others. All five primary themes were apparent in this stage.

Nicole discussed how her internal state shifted as she took charge of her thinking and found internal and external motivation to conduct research: “Just thinking about the benefits that research has, not just to me, but to the profession as a whole, to my colleagues and even [to] the schools I’m working for [is important].” Another participant expressed that her interest and curiosity in research helped her persevere through her fear of research, which seemed to be an important element of the moderate level of RI. Sally stated, “I’m apprehensive to an extent, but very curious and interested to learn more . . . to understand more how [research] can be [an] integral part [to] my work.” In the focus group, Lisa constructed a visual representation and shared that her own curiosity has been the driving force for her level of RI:

Mine [visual conceptualization of research] just started off with curiosity, interest, desire, and then a picture of a woman wondering about something, because to me that is research. You just have this desire . . . to know why. So, it’s just that curiosity drives the interest.

In stage one of RI, participants clearly indicated loyalty to their practitioner side. In stage two, the transition of integrating research with practice became apparent through participants sharing more flexible views on how research can play a role in professional identity. Ellie gave the following example of this transition:

I think counselors like working with people and helping people . . . that’s why a lot of them go into the field. So it’s if they see research brings benefit, I think that a lot of them would say it’s worthwhile and beneficial, but it just depends on the person.

Nicole also validated that research has a place within professional identity conceptualization. She stated, “If you want to add some more credibility, or some more distinctions to your profession, I think that research does play an important role.”

External facilitators of RI were important in the transition to a higher level of RI. An example of an external facilitator came in the form of learning alternative methodologies (e.g., qualitative research). Nicole stated:

I think since I went through the program and . . . realized there were different types of research I could do [e.g., qualitative], I think my attitude now has become a lot better almost to the point where I’m not scared of it anymore. . . . I definitely think I’m more open to the possibility that I can do research and do well in my profession.

Another important part of the transition surfaced as participants described their conceptualization of research. In the stagnation stage, the participants’ definition of research seemed to be narrow and something with which they could not relate. As participants transitioned in their RI, they started to understand research in a broader way and to see research as something with which they could relate. Shelly stated:

I’m not a big person about research. I think it’s just the word research that makes me kind of cringe, but really when you think about it, I think we all do research all the time; we just don’t think about it that way.
Additionally, the behaviors that participants described at this stage were reflective of more than just consuming research, which was predominant at stage one. Sally shared the following:

I read pretty much every article I can get my hands on, go to trainings all the time, and I took the initiative . . . to research material and do presentations and . . . I’m considering
. . . [doing] more with research.

Stage Three: Stabilization

The third and highest level of RI for master’s-level counseling students was the stabilization stage, aptly named due to the stabilization in RI that occurred at this stage as compared with stages one and two. The themes connected to this stage of RI include the following: internal facilitators of higher levels of RI, external facilitators of higher levels of RI and faculty as salient to RI. One of the strongest components of this stage was participants’ internal state of RI. Participants’ conceptualization of research was influenced by the realization that research includes multiple components, ranging from surveying scholarly articles to conducting original research. Additionally, participants with a stronger internal RI were less vulnerable to negative messages about research.

Participants described internal components that facilitated higher levels of RI, including persistence, dedication, curiosity, integration of practitioner and research identities, and broad conceptualization of research. Another key element that seemed to represent a higher level of RI was the way that participants conceptualized research. At stages one and two, participants were more focused on research being about numbers and an activity that others do. The shift in participants’ conceptualization of research was demonstrated through the visual representation that focus group interviewees offered when hearing the word research. Participant Jessica constructed an image that manifested her conceptualization of research as being multidimensional.

Other important components of stage three were external facilitators of higher levels of RI described in the form of counselor education program elements, positive messages from others and undergraduate education that included research. Participant Henry gave an example of positive messages from others:

I would say that [a message from a supervisor] was [an] emphasis to do research just because I . . . work in a profession where you . . . constantly have questions in the area and there is no possible way you can have the answer to everything, and so the only way to do that is to do the research behind it.

Participant Dan discussed how exposure to research in his undergraduate program was critical in his RI process. He stated:

Until I took that undergraduate class, I had absolutely no interest in research and didn’t understand any of the value to it and now all of a sudden when you begin to see statistics, valid statistics, mind you, but statistics that . . . reinforce your thought process or your program . . . [it] was a positive.

Other ideas that came up frequently were program elements and flexibility around structuring research to include interest. Lindsey discussed how this impacted her RI process:

If you are interested in helping . . . clients, you should do [research] projects. You know, the program recognized that everybody has different interests and . . . they can’t teach us everything, they . . . let us adapt what we researched to what we are interested in.

Other program elements related to faculty playing a role in the RI process. Participants in this stage did not place as much emphasis on the faculty role as those in lower levels of RI; this shift seemed related to individuals at higher stages having more of an internal drive to know themselves as researchers. Participant Bob described how faculty can facilitate higher levels of RI:

[The] professor . . . was amazing. She is always continuing research and she likes to involve students . . . so she definitely pushed me and showed that continuing research is very important to professional development. So I would say that would be the number one factor for me.

Discussion

The findings of this research tell a story about the phenomenon of master’s-level counseling students’ RI. The story can be understood through viewing the process on a continuum that is fluid and comprised of interactions between the themes manifested in this study. The idea that research is a sub-identity of a counselor’s professional identity was validated at all levels of RI. Participants frequently identified what it would take to reach higher levels of RI. This information was used to further understand the facilitation of the RI development process across stages.
Some participants believed that research is important and has its place, but those in the stagnation stage believed that others should produce the research (i.e., diffusion of responsibility). There are multiple aspects that comprise stage one of RI (see Table 1). Factors that facilitate a higher level of RI in students at stage one include the following: more infusion of research across courses and continuing education training, open and frequent communication about research, teaching more critical thinking skills, supervisors providing directives such as having supervisees read research articles, knowledge of alternative methodologies, challenging views of research and working to help them establish a new conceptualization, and more research programming, such as assignments that require research activities.
Participants described the negotiation stage as a “necessary evil.” Although participants in this stage wanted to act on their belief that research is important to practice, they often described a struggle to make that happen. However, participants in the negotiation stage stated that they were more likely to engage in lower- to moderate-level research behaviors (e.g., reading articles, referencing research in papers and copresenting). Multiple aspects are comprised in this stage of RI (see Table 1). Counselors need to understand how to facilitate higher levels of RI. In addition to the factors mentioned in stage one, some factors that facilitate higher levels of RI include the following: establishing peer support for research activities, supervisors providing directives around and modeling research activities, mentoring students through research activities such as presenting and conducting research, involving students in faculty research projects, and continuing to foster an evolution of conceptualization of research and professional identity.

Table 1

 The Stages of RI Development in Master’s-Level Counseling Students

Lower Level of RI

Stagnation Stage

Moderate Level of RI

Negotiation Stage

Higher Level of RI

Stabilization Stage

Avoids research activities; mostly consumer-oriented (if anything); does not talk about research; skips the results section when reading articles Starts to become active with research; consumes research (reads articles) more regularly; copresents at conferences; shows willingness to take some risk around research Consumer and producer of research; conducts scholarly studies; pursues more rigorous research tasks such as scholarly publication; mentors others in their RI process; models research behaviors for others; demonstrates high levels of critical thinking, dedication, time management and persistence
Focuses more on using intuition to develop professionally; believes research is for researchers and practice is for counselors; believes research can take away from practice; has low research self-efficacy; does not believe research is a priority Believes research may be important for some counselors, but does not have to be for all; research can produce positive outcomes and can enhance practice; makes gains in research self-efficacy Believes research is core to the counseling practice; believes effective counseling practice does not come without research; believes research should be a priority; has high research self-efficacy
Mostly negative attitude toward research; says research is “stupid,” “waste of time” and “not fun;” irritated by others with moderate-to-high levels of RI; low motivation (both internal and external) to research Shows more internal motivation, but mainly motivated externally for research; ambivalent attitude toward research; says things like “it’s a necessary evil” Positive attitude toward research; says research is “exciting” and “crucial;” is frustrated by others’ negative attitudes toward research; is predominantly internally motivated to research
Definition of research is narrow and science/math-oriented; supports the idea of not seeing self as researcher Sees research in broader terms; starts to define research in a way they can connect with Views research as broad and all encompassing; sees self within conceptualization of research
Sees self solely as practitioner; does not see self as researcher RI is being negotiated; starts to consider seeing self as researcher; practitioner identity remains most salient Views self as both a researcher and counselor; has negotiated and integrated the two identities

Participants with the highest levels of RI were in the stabilization stage. These participants expressed knowing themselves as both a counseling student and a researcher. Internal and external factors contributed to participants’ ability to persist past elements in stages one and two to progress into stage three. In addition to all of the previously mentioned factors, some important elements that may help master’s-level counseling students stay at stage three include the following: involvement with faculty research projects, requiring a thesis, mentoring toward the overall goal of publication, creating student research groups, assigning projects that elicit knowledge of application of research, supervisors collaborating with supervisees on research projects, employment settings requiring data be gathered and research be conducted by counselors; and knowledge and skills in qualitative or quantitative research (or both), and presenting findings from research at conferences.

Implications

There are multiple implications from this research for counselor education programs, counselor educators and counseling students. The most profound and impactful aspects of the RI process were the external processes. The external components of program elements and faculty were foundational in how participants viewed themselves, others and the counseling profession. The outcome was manifested in levels of RI that were captured through three proposed stages.

Counselor education programs. Participants often stressed how important it was to RI development to be exposed to research early in their studies, exposed to alternative research methodologies in order to find common ground with research (e.g., qualitative research), and exposed to flexibility to infuse student interests in meeting research assignments. Additionally, participants often talked about the format of research courses and used words such as confusing, irrelevant and rushed to describe their feelings toward research courses. This information may indicate a need for counseling programs to reestablish how these courses are assigned and taught. Participants in this study shared that research courses were taught by faculty in other departments. Students in the counseling field may benefit from learning research from counselor educators so that research and practice are connected in more meaningful and practical ways.

Importantly, master’s-level counseling programs may want to consider offering a qualitative research course. Previous literature has demonstrated that exposure to qualitative methodology helps counseling students consider themselves researchers (Jorgensen & Duncan, 2015; Reisetter et al., 2004). Participants also discussed feeling connected to research that allowed them to interact with people. Often, barriers to higher levels of RI in participants related to the belief that research is only for scientists who know a lot about numbers and statistics.

Lastly, it may be important for master’s-level programs to create a programmatic structure that supports the integration of research into each course. According to Lambie and Vaccaro (2011), the research training environment is a crucial element in the process of students becoming confident with their research abilities. An integrative approach also may allow students more of a platform for building a relationship with research and finding something of interest that is not fixed within the parameters of research courses. This approach also supports a process for moving students along their RI development by assisting them in starting to identify research interests, then looking at the literature to examine gaps, and integrating those interests and gaps into ideas for original research.

Counselor educators. Consistent with previous research (Gelso, 2006; Jorgensen & Duncan, 2015), several participants talked about faculty playing a major role in how they came to know themselves as researchers. This theme surfaced at each stage of RI and was so frequently mentioned that it was considered an exclusive theme outside of other external facilitators. The findings from this study revealed concrete ways counselor educators can promote higher levels of RI in their students. Some simple tasks include faculty talking about their research processes in class or during meetings with students. Participants believed that the lack of conversation about research indicated that faculty members were not engaged in research or that they did not want students to know about or to be a part of their research. Other tasks may include taking students through the steps of critically analyzing research articles. Additional activities include having students copresent at conferences and co-research with faculty, and mentoring students’ research processes.

Ultimately, counselor educators may want to consider examining their own level of RI. This analysis may help break down barriers to effectively facilitating student RI development. Counselor educators’ transparency about their research may be enough to facilitate a higher level of RI in students and help them realize a need to build internal motivation to embrace research as a part of their professional identity as a counselor.

Counselors-in-training. Other implications are directed toward counselors-in-training. Counselors’ ownership of their RI is essential in the process of reaching higher levels of RI. Participants indicated that their internal processes were critical in how they processed and applied information that could support and facilitate their RI. They further indicated that a strong internal RI allowed them, or could allow them, to take better advantage of research, better apply research to practice and ultimately be a better practitioner.

Limitations

The limitations of this study relate to inherent issues with qualitative methodology. One, this research cannot be generalized due to the nature of its methodology, small sample size and the geographic location of the participants. Two, errors may have occurred during the research process due to researcher bias. Likewise, the researchers may have been biased in labeling the levels of research. Although the stages were based on information conveyed by the participants, the participants did not specifically categorize themselves in the levels proposed by the researchers.

Areas for Future Research

Future researchers may consider developing a scale that would objectively measure the stages of RI. An RI development scale would assist counselor educators with objectively measuring learning outcomes and in evaluating the counseling program’s effectiveness in executing accreditation research standards. Rowan and Wulff (2007) wrote that using qualitative methods to inform scale development is perceived as appropriate and sufficient within the research community. Particularly, they suggested that “analyzing data generated through interviews informs the survey designed for larger samples” (p. 450). The current study serves as a platform to move from subjective to more objective ways of assessing RI in master’s-level counseling students. Additionally, RI within the context of other professions could be examined after establishing a valid and reliable scale.

Conclusion

The current findings contribute to the goal of constructing a universal understanding of professional counselor identity development—particularly the RI dimension. Previous literature has primarily focused on behaviors, beliefs and attitudes that relate mostly to the practitioner side of counselor professional identity (Auxier et al., 2003; Brott & Myers, 1999; Hanna & Bemak, 1997; McAuliffe & Eriksen, 2002; Mellin, Hunt, & Nichols, 2011; Woodside, Oberman, Cole, & Carruth, 2007). The current research contributes to what is already known about how to develop practitioner identity. Further, as the counseling profession seeks greater recognition within the medical and human services communities, professional counselors must connect their work to activities that are considered more research-oriented. An understanding of RI stages and development may further assist in this process.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

References

American Counseling Association. (2014). 2014 ACA code of ethics. Alexandria, VA: Author.

Auxier, C. R., Hughes, F. R., & Kline, W. B. (2003). Identity development in counselors-in-training. Counselor Education and Supervision, 43, 25–38. doi:10.1002/j.1556-6978.2003.tb01827.x

Brott, P. E., & Myers, J. E. (1999). Development of professional school counselor identity: A grounded theory. Professional School Counseling, 2, 339–348.

Creswell, J. W. (2013). Qualitative inquiry & research design: Choosing among five approaches (3rd ed.). Thousand Oaks, CA: Sage.

Erikson, E. H. (1994). Identity and the life cycle. New York, NY: Norton.

Gelso, C. J. (2006). On the making of a scientist-practitioner: A theory of research training in professional psychology. Training and Education in Professional Psychology, S, 3–16.

doi:10.1037/1931-3918.S.1.3

Gibson, D. M., Dollarhide, C. T., & Moss, J. M. (2010). Professional identity development: A grounded theory of transformational tasks of new counselors. Counselor Education and Supervision, 50, 21–38. doi:10.1002/j.1556-6978.2010.tb00106.x

Hanna, F. J., & Bemak, F. (1997). The quest for identity in the counseling profession. Counselor Education and Supervision, 36, 194–206. doi:10.1002/j.1556-6978.1997.tb00386.x

Howard, G. S. (1985). Can research in the human sciences become more relevant to practice? Journal of Counseling & Development, 63, 539–544.

Hunt, B. (2011). Publishing qualitative research in counseling journals. Journal of Counseling & Development, 89, 296–300. doi:10.1002/j.1556-6678.2011.tb00092.x

Jorgensen, M. F., & Duncan, K. (2015). A grounded theory of master’s-level counselor research identity. Counselor Education and Supervision, 54, 17–31. doi:10.1002/j.1556-6978.2015.00067.

Kopala, M., & Suzuki, L. A. (Eds.). (1999). Using qualitative methods in psychology. Thousand Oaks, CA: Sage.

Kozina, K., Grabovari, N., De Stefano, J., & Drapeau, M. (2010). Measuring changes in counselor self-efficacy: Further validation and implications for training and supervision. The Clinical Supervisor, 29, 117–127.
doi:10.1080/07325223.2010.517483

Lambie, G. W., & Vaccaro, N. (2011). Doctoral counselor education students’ levels of research self-efficacy, perceptions of the research training environment, and interest in research. Counselor Education and Supervision, 50, 243–258. doi:10.1002/j.1556-6978.2011.tb00122.x

Lampropoulos, G. K., Spengler, P. M., Dixon, D. N., & Nicholas, D. R. (2002). How psychotherapy integration can complement the scientist-practitioner model. Journal of Clinical Psychology, 58, 1227–1240.

McAuliffe, G., & Eriksen, K. (Eds.). (2002). Teaching strategies for constructivist and developmental counselor education. Westport, CT: Bergin & Garvey.

Mellin, E. A., Hunt, B., & Nichols, L. M. (2011). Counselor professional identity: Findings and implications for counseling and interprofessional collaboration. Journal of Counseling & Development, 89, 140–147. doi:10.1002/j.1556-6678.2011.tb00071.x

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Nugent, F. A., & Jones, K. D. (2009). Introduction to the profession of counseling (5th ed.). Upper Saddle River, NJ: Pearson.

Pain, H. (2012). A literature review to evaluate the choice and use of visual methods. International Journal of Qualitative Methods, 11, 303–319.

Poldma, T., & Stewart, M. (2004). Understanding the value of artistic tools such as visual concept maps in design and education research. Art Design & Communication in Higher Education, 3, 141–148. doi:10.1386/adch.3.3.141/1

Ponterotto, J. G., & Grieger, I. (1999). Merging qualitative and quantitative perspectives in a research identity. In M. Kopala & L. A. Suzuki (Eds.), Using qualitative methods in psychology (pp. 49–62). Thousand Oaks, CA: Sage.

Reisetter, M., Korcuska, J. S., Yexley, M., Bonds, D., Nikels, H., & McHenry, W. (2004). Counselor educators and qualitative research: Affirming a research identity. Counselor Education and Supervision, 44, 2–16. doi:10.1002/j.1556-6978.2004.tb01856.x

Rowan, N., & Wulff, D. (2007). Using qualitative methods to inform scale development. The Qualitative Report, 12, 450–466.

Unrau, Y. A., & Grinnell, R. M., Jr. (2005). The impact of social work research courses on research self-efficacy for social work students. Social Work Education, 24, 639–651. doi:10.1080/02615470500185069

Woodside, M., Oberman, A. H., Cole, K. G., & Carruth, E. K. (2007). Learning to be a counselor: A prepracticum point of view. Counselor Education and Supervision, 47, 14–28. doi:10.1002/j.1556-6978.2007.tb00035.x

Maribeth F. Jorgensen, NCC, is an Assistant Professor at the University of South Dakota. Kelly Duncan, NCC, is an Associate Professor at the University of South Dakota. Correspondence may be addressed to Maribeth F. Jorgensen, 414 East Clark Street, Vermillion, SD 57069, Maribeth.Jorgensen@usd.edu.

 

Effect of Participation in Student Success Skills on Prosocial and Bullying Behavior

Melissa Mariani, Linda Webb, Elizabeth Villares, Greg Brigman

This study involved fifth-grade students (N = 336) from one Florida school district and examined prosocial behaviors, bullying behaviors, engagement in school success skills and perceptions of classroom climate between the treatment group who received the school counselor-led Student Success Skills classroom guidance program, and their peer counterparts (comparison group). Statistically significant differences were found (p values ranged from .000–.019), along with partial eta-squared effect sizes ranging from .01 (small) to .26 (quite large) between groups. Evidence supported the Student Success Skills classroom program as a positive intervention for affecting student engagement, perceptions and behavior. 

 

Keywords: bullying, prosocial behaviors, Student Success Skills, classroom climate, school counselor

 

While some forms of youth victimization have steadily declined over the years, bullying occurrences have remained relatively stable (DeVoe et al., 2004; Wang, Iannotti, & Nansel, 2009). Reports have indicated that 30–40% of students admit to regular involvement in bullying behaviors (Bradshaw, O’Brennan, & Sawyer, 2008; Nansel et al., 2001; Spriggs, Iannotti, Nansel, & Haynie, 2007). Additionally, statistics reveal that bullying is much more common among early adolescents than elementary age children (Bradshaw et al., 2008; Olweus, 1993; Ortega & Lera, 2000). In fact, notable increases in the rates of peer aggression occur during the transition years, in both grade 6 (beginning of middle school) and grade 9 (beginning of high school; Olweus, 1993; Ortega & Lera, 2000); therefore, targeting students prior to these peaks would be considered more proactive.

 

Recent approaches to combat the bullying problem have highlighted the importance of increasing students’ social competencies and coping and social interaction skills (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011). Greenberg et al. (2003) offered that alternative approaches to managing problem behavior are most beneficial when they simultaneously foster students’ personal and social skills while improving the quality of the school environment. The philosophy behind incorporating these types of programs in schools suggests that in order for students to fully reach their potential, educators must address the whole child (Payton et al., 2008; Saleebey, 2008). Ultimately, building key skills in all children contributes to creating a positive, safe and caring learning environment, one that discourages aggression and violence.

 

The Consequences of Bullying Behaviors

 

Bullying can negatively impact victims and bullies, as well as bystanders. Emotionally, victims of bullying report higher levels of fear and anxiety (Gini & Pozzoli, 2009; Reijntjes, Kamphuis, Prinzie, & Telch, 2010), are more socially withdrawn (Roth, Coles, & Heimberg, 2002), and are more likely to experience depression (Ttofi, Farrington, Lösel, & Loeber, 2011) than their peers. In terms of social consequences, victims suffer from increased levels of peer rejection (Gini & Pozzoli, 2009; Reijntjes et al., 2010). Victimization also has been linked to academic consequences, including increased tardiness, absentee and dropout rates (Beale & Scott, 2001; Nansel et al., 2001); poorer grades; and more academic struggles than their peer counterparts (Boulton, Trueman, & Murray, 2008). Similarly, bullies and bystanders experience distinct consequences that contribute to the struggles they experience in school. For example, bullies also may earn poorer grades and have higher absentee and dropout rates than non-aggressive peers (Bernstein & Watson, 1997), and bystanders have reported increased levels of fear about school safety (Olweus, 1993).

 

The literature further indicates that the actions of those involved in bullying situations, including bystanders, can either enhance or damage a school’s climate (Catalano, Haggerty, Oesterle, Fleming, & Hawkins, 2004; Swearer, Espelage, Vaillancourt, & Hymel, 2010). Carney (2008) concluded that experiencing bullying firsthand, as well as witnessing bullying incidents, can be traumatic for students. It is evident that schools should be concerned about proactively addressing bullying behaviors. If not, significant consequences related to student behavior and academic achievement can abound.

 

Empirical Support for Student Success Skills

 

The Student Success Skills (SSS) classroom program (Brigman & Webb, 2010) is based on extensive research reviews (Daly, Duhon, & Witt, 2002; Greenberg et al., 2003; Hattie, Biggs, & Purdie, 1996; Masten & Coatsworth, 1998; Payton et al., 2008; Wang, Haertel, & Walberg, 1994; Zins, Weissberg, Wang, & Walberg, 2004) that identified three key categories of skills needed in order to grow, perform and achieve: (a) cognitive and meta-cognitive skills such as goal setting, progress monitoring and memory skills; (b) social skills such as interpersonal, social problem solving, listening and teamwork skills; and (c) self-management skills such as managing attention, motivation and anger. Recent evidence supporting the use of these skills, valuing the teaching of both academic and social skills in order to promote student growth and success, also can be found in the literature (Winne & Nesbit, 2010; Yeager &Walton, 2011).

 

SSS is a comprehensive, evidence-based, school counselor-led program that supports development of these key skills in students. This manualized intervention consists of five 45-minute classroom lessons spaced one week apart, beginning in the fall, usually in late August or early September. Three monthly booster sessions are then implemented beginning in January. A total of 20 strategies are introduced and reinforced using a highly engaging “tell-show-do” format known to increase levels of student engagement and motivation. Each SSS lesson follows a structured beginning, middle and end sequence clearly detailed in the SSS manual. (Due to space limitations, readers are encouraged to review the Webb and Brigman [2006] descriptive article on the SSS classroom program).

 

Five outcome studies testing the effectiveness of SSS classroom and small group programs have resulted in positive effects on both student achievement and behavior, as well as perceived improvement in classroom behaviors (Brigman & Campbell, 2003; Brigman, Webb, & Campbell, 2007; Campbell & Brigman, 2005; León, Villares, Brigman, Webb, & Peluso, 2011; Webb, Brigman, & Campbell, 2005). A recent meta-analysis of these five SSS studies revealed an overall effect size of .29 (large), a medium effect size of .17 (equivalent to an additional half of a year of learning in reading) and a large effect size of .41 (equivalent to an additional full year of learning in math; Villares, Frain, Brigman, Webb, & Peluso, 2012).

 

While the SSS program has been shown to positively affect student achievement and behavior in general, comparison studies have not examined the impact of SSS on reducing bullying behavior. Consequently, the current study sought to measure the effects of the SSS classroom program administered by school counselors (Brigman & Webb, 2010) on student prosocial behavior, bullying behavior, engagement in school success skills and perceptions of classroom climate. The SSS intervention was selected because the cognitive, social and self-management skills taught in the program are associated with promoting academic and prosocial behaviors in youth, behaviors that enhance a positive school climate and discourage negative behaviors like bullying.

 

Purpose of the Study

 

The purpose of this study was to determine the effectiveness of the SSS classroom program (Brigman & Webb, 2010) on fifth-grade students’ prosocial behavior, bullying behavior, engagement in school success skills and perceptions of classroom climate. The problem addressed is significant for two reasons. First, a wide range of negative consequences can result from ineffectively dealing with bullying (Bernstein & Watson, 1997; Carney, 2008; Catalano et al., 2004; Deluty, 1985; Gini & Pozzoli, 2009; Olweus, 1993; Reijntjes et al., 2010; Swearer et al., 2010). Second, further research is needed to demonstrate the positive impact that school counselors have in schools. It has been stated that the school counselor’s role in addressing bullying in schools is crucial (Crothers & Levinson, 2004; Hanish & Guerra, 2000; Hazler & Carney, 2000; Hermann & Finn, 2002).

 

Research Questions

The following research questions were addressed: (a) Is there an increase in the prosocial behaviors of fifth-grade students after participating in the SSS classroom program? (b) Is there a decrease in the bullying behaviors of fifth-grade students after participating in the SSS classroom program? (c) Is there an increase in levels of engagement in student success skills (cognitive and learning, social, and self-management) of fifth-grade students after participating in the SSS classroom program? (d) Is there an improvement in classroom climate after fifth-grade students participate in the SSS classroom program?

 

Method

 

Participants and Sampling Procedures

Fifth-grade students (N = 336, 181 females and 155 males) from five public elementary schools in central Florida volunteered to participate in this study. The eligibility criteria included the following: (a) participating schools had to employ a certified school counselor, (b) school counselors had to agree to implement the manualized SSS classroom program (Brigman & Webb, 2010), and (c) in an attempt to create a whole-school culture, the school had to have more than one fifth-grade classroom participating. On average, each school contained 4–6 general education fifth-grade classrooms; 21 of these 22 classrooms in the five participating elementary schools were included in the study. All students in general education fifth-grade classrooms were invited to participate. Blended classrooms (e.g., multiple grade levels in one classroom) were not included so that generalizations among age levels could be made between schools. The volunteer sample (N = 336) mean age was 10 years old. Racial identifications included 7 (2%) Asian, 52 (15%) African American, 221 (66%) Caucasian, 43 (13%) Latino/a, 12 (3.6%) Multiracial and 1 (.4%) American Indian. Thirty-one percent of the sample (n = 104) received free lunch and 7.1% (n = 24) were on reduced-lunch status.

 

The study followed a pre-post quasi-experimental cohort group design (Cook & Campbell, 1979). Random assignment of individual students was not conducive to preserving the nature of a whole-school culture, so schools were assigned to either the treatment or comparison group based on the order in which they volunteered to participate. The first three schools to volunteer were assigned to the treatment group (schools A, B and C) while the last two schools (schools D and E) were assigned to the comparison group.

Procedures

Following approval from the university’s Institutional Review Board, consent for research was obtained from the participating school district, school administrators, parents, teachers and students. In September, five certified school counselors from the participating schools received a 1-day training in the manualized use of the SSS classroom guidance program as well as other study-related procedures including instrument administration and electronic summary report instructions. The SSS program, consisting of five consecutive 45-minute lessons spaced a week apart, was then implemented in all fifth-grade classrooms in the treatment schools beginning in October. Monthly booster lessons followed beginning in January. Only students with parent permission completed the required instruments: the Peer Relations Questionnaire (PRQ), the Student Engagement in School Success Skills (SESSS) survey and the My Class Inventory-Short Form Revised (MCI-SFR). Students were ensured of the anonymity of their reporting by using generic school, classroom and student numbers. For a classroom to remain eligible to participate, a minimum of 80% of the students in the classroom had to return a signed parent consent form.

 

     Treatment group. Schools A, B and C served as the treatment group (n = 209) and participating fifth-grade students in this group received the SSS classroom intervention. These students completed the following pretests in September 2010: the PRQ, MCI-SFR and SESSS. Implementation of the SSS classroom program began in October. Following the completion of the first five SSS lessons, treatment students completed the SESSS instrument (posttest). Booster lessons were delivered in January, February and March, and treatment students were then asked to complete the PRQ, MCI-SFR and SESSS following the final booster lesson (post-posttest).

 

     Comparison group. Schools D and E served as the comparison group (n = 127) and did not receive the SSS intervention during the study. Students in these schools experienced business as usual, including any regularly scheduled school counseling programming. Comparison schools were eligible to receive the SSS curriculum after the study was completed. Participating students in the comparison schools completed the three instruments at the same time intervals (pretest, posttest and post-posttest) as students in the treatment group.

 

Instruments

     Peer Relations Questionnaire – For Children – Short Form. The PRQ (Rigby & Slee, 1993a) was designed to reveal student experiences with bullying at school. The questionnaire takes approximately 5–7 minutes to complete and is comprised of 20 items in which students are asked to circle how often the statements are true for them. The answers range on a 4-point scale from never = 1, once in a while = 2, pretty often = 3, to very often = 4. The PRQ consists of three scales and several filler items: a Bully Scale, a Victim Scale and a Prosocial Scale; students in the present study took all three scales. Scoring is determined by the items contained in each of the scales, with higher scores corresponding to a propensity for bully, victim and/or prosocial behaviors (Rigby & Slee, 1993b). Rigby and Slee (1993b) reported the reliability of the PRQ using the following alpha coefficients: bully scale (.75–.78), victim scale (.78–.86) and prosocial scale (.71–.74), indicating more than adequate internal consistency. Recent evaluation of the PRQ’s psychometric properties by Tabaeian, Amiri, and Molavi (2012) supported it as a highly reliable and valid instrument that should continue to be used in research.

 

     Student Engagement in School Success Skills Survey. The SESSS is a 33-item student self-report of cognitive engagement in SSS program skills and strategies, using language specific to the SSS curriculum, and takes approximately 15 minutes to complete (Carey, Brigman, Webb, Villares, & Harrington, 2013). Students are asked to circle how often they have engaged in a list of behaviors within the last 2 weeks (e.g., “I tried to encourage a classmate who was having a hard time doing something,” “I noticed when another student was having a bad day,” “I listened to music so that I would feel less stressed”). Possible responses include I didn’t do this at all, I did this once, I did this two times or I did this three or more times. The SESSS is intended for use with students in grades 3–12. Though a four-factor model was first revealed in an exploratory factor analysis conducted by Carey et al. (2013), a subsequent confirmatory factor analysis revealed the following three factors: self-direction of learning (which represents the combination of two original factors—management of learning and application of learning strategies), support of classmates’ learning and self-regulation of arousal, which correspond to the three subscales of the SESSS (Brigman et al., 2014). Coefficient alphas for the three SESSS subscales were as follows: self-direction of learning: 0.89, support of classmates’ learning: 0.79 self-regulation of arousal: 0.68, and 0.90 for the SESSS as a whole (Villares et al., 2014), indicating good internal consistency.

 

     My Class Inventory-Short Form-Revised. The MCI-SFR is a 20-item instrument that intends to measure the perceptions of students in grades 4–6 of four areas related to classroom climate (satisfaction, friction, competitiveness and cohesiveness). The instrument takes approximately 10–15 minutes to complete and respondents are asked to select either “yes” (3 points) or “no” (1 point). Omitted or invalidly scored items receive two points. Reports on the psychometric properties for both the MCI-SF and MCI-SFR have indicated strong concurrent validity when comparing long and short versions across each of the scales (.91–.97). Additionally, some degree of internal consistency (largely adequate coefficient alphas) has been reported for class means with Australian children (.58–.81). The MCI-SF yielded more acceptable alpha coefficients for each of the scales (.84–.93) than did the long version, the MCI. Modifications to the revised MCI-SFR produced a better overall instrument, improving factor interpretability and reliability (Fraser, 1982; Sink & Spencer, 2005). Sink and Spencer (2005) reported that interpreting students’ responses from pretest to posttest on the MCI-SFR should be straightforward, with higher scores on the satisfaction and cohesion scales providing positive indicators of a healthy classroom environment, and higher scores on the competitiveness and friction scales suggesting needed improvement in this area.

 

Data Analysis

Individual students were the units of analysis in the study. An alpha level of .05 and one-way analysis of variance (ANOVA) tests were used to analyze differences in prosocial behaviors, bullying behaviors, school engagement skills and perceptions of classroom climate between students who participated in the SSS program (treatment group) and students who did not (comparison group). A post hoc Bonferroni correction was used to lessen the chance of a Type I error. Prior to the analyses, all the variables of interest were examined for accuracy of data entry, missing values, outliers and the normality of distributions. In addition, effect sizes (ES) were calculated to determine the practical significance of the SSS classroom program for the various student outcomes.

 

In this study, a partial eta-squared (ES; hp2) calculation was computed by SPSS (Field, 2009; Howell, 2008; Sink & Mvududu, 2010). The ES addresses the magnitude of the difference between groups or relationships between variables. The following benchmarks were used to determine small, medium, and large or strong ES strengths regarding hp2 calculations: (a) .01 small, (b) .06 medium, and (c) .14 large or strong (Green & Salkind, 2008; Sink & Mvududu, 2010).

 

Results

 

Preliminary ANOVAs were conducted on the students’ PRQ, SESSS and MCI-SFR pretest scores to determine whether statistically significant differences existed among the treatment and comparison groups prior to the implementation of the SSS intervention. No statistically significant differences were found on pretest scores; therefore, no covariates were used in subsequent analyses of students’ PRQ, SESSS and MCI-SFR posttest scores. Table 1 provides a summary of the study’s main findings.

 

Prosocial Behaviors

Research question 1 examined whether fifth-grade students who participated in the SSS classroom program would experience an increase in prosocial behaviors as compared to their peer counterparts who did not receive the intervention. Prosocial behaviors were assessed using the prosocial scale of the PRQ. A total of 188 students from the treatment group (schools A, B and C) and 123 students from the comparison group (schools D and E) were included in this analysis (n = 311). Findings from an ANOVA showed a statistically significant difference between groups, F(1, 308) = 18.708, p = .000 and hp= .06, a medium effect size. Participants in the treatment group (n = 188, M = 12.61, SD = 2.47) reported higher scores for prosocial behaviors at posttest as opposed to participants in the comparison group (n = 123, M = 11.27, SD = 2.81). Results indicated that students in the treatment schools reported engaging in prosocial behaviors more often at posttest than students in the comparison schools, highlighting the practical significance of using this intervention to positively influence student behavior.

 

Table 1

 

Summary Table of P Values, Effect Size Estimates, and Confidence Intervals for All Measures

Measure p value  hp2 ES Strength              CI
PRQ
     Prosocial .000* .06 Medium 95% [11.68, 12.22]
     Bully .017* .02 Small 95% [7.22, 7.69]
SESSS
     Pretest to Posttest .000* .26 Large 95% [2.05, 2.20]
     Pretest to Post-posttest .366 .00 Negligible 95% [2.46, 2.62]
MCI-SFR
     Satisfaction .019* .02 Small 95% [10.36, 10.96]
     Friction .152 .01 Small 95% [9.21, 9.83]
     Competitiveness .831 .00 Negligible 95% [10.79, 11.41]
     Cohesion .414 .00 Negligible 95% [9.18, 9.85]

Note. PRQ = Peer Relations Questionnaire; SESSS = Student Engagement in School Success Skills;
MCI-SFR = My Class Inventory-Short Form-Revised; p = significance at posttest; hp2 = partial eta-squared
effect size; CI = confidence interval;

* p < .05.

 

Bullying Behaviors

The second research question asked whether fifth-grade students who received SSS would experience a decrease in bullying behaviors, assessed by the bully scale of the PRQ, compared to their peers in the comparison group. Results from a one-way ANOVA showed a statistically significant difference between the participants’ (n = 311) posttest scores, F(1, 308) = 5.708, p = .017 and a small effect size, hp2 = .02. These findings confirmed that students in the treatment group evidenced a decrease in mean change scores on the PRQ bully scale after SSS implementation, whereas students in the comparison schools reported an increase. Thus, students in the treatment group who received the SSS classroom intervention reported less bullying behavior at posttest than students in the comparison group.

 

Engagement in School Success Skills

Research question 3 investigated whether participating fifth-grade students who received the SSS classroom program would experience an increase in levels of engagement in student success skills (cognitive and learning, social, self-management) as compared to their peer counterparts. Results from the SESSS instrument were used in this analysis. A total of 115 students in the treatment group (schools A, B and C) and 85 students in the comparison group (schools D and E) were included in the SESSS analysis (n = 200). Table 2 displays the treatment and comparison group means, standard deviations, and change scores for the SESSS by school at the following three data collection periods: pretest (prior to SSS implementation), posttest (immediately following implementation of the five weekly SSS lessons) and post-posttest (at the end of the study).

 

Table 2

 

Treatment and Comparison Group Means, Standard Deviations and Change Scores for the SESSS by School

School n PretestM (SD) PosttestM (SD) Post-posttestM (SD) Pretest-to-posttestM  +/- Posttest-to-post-posttest M  +/- Pretest-to-post-posttest M  +/-
A* 40 2.49 (.61) 2.88 (.63) 2.41 (2.63) +.39 +.47 -.08
B* 38 2.47 (.68) 2.62 (.66) 2.64 (.63) +.15 +.02 +.17
C* 37 2.44 (.58) 2.60 (.60) 2.82 (.64) +.16 +.22 +.38
D 28 2.53 (.53) 2.47 (.57) 2.56 (.65) -.06 +.09 +.03
E 57 2.07 (.77) 1.37 (.12) 2.39 (.48) -.70 +1.02 +.32
TotalT 115 2.47 (.62) 2.50 (.64) 2.62 (.65) +.03 +.12 +.15
Total

C

85 2.22 (.73) 1.73 (.68) 2.45 (.54) -.49 +.72 +.23

 

Note. SESSS = Student Engagement in School Success Skills; n = number; M = mean; SD = standard deviation;

T = treatment group; C = comparison group; * = treatment school; +/- = mean change score.

 

   SESSS posttest score analysis. Findings from an ANOVA on the posttest scores on the SESSS (from the pretest in October to the posttest in December) showed a statistically significant difference between schools, F(1, 197) = 69.295, p = .000 and hp2 = .26, a large effect size. Students in the treatment group (n = 115, M = 2.50, SD = .642) evidenced higher levels of engagement in school success skills from pretest to posttest than their counterparts in the comparison group (n = 85, M = 1.73, SD = .617).

 

SESSS post-posttest score analysis. A second one-way ANOVA showed no statistically significant differences between the treatment and comparison groups scores from pretest (October) to post-posttest (March), F(1, 197) = .820, p = .366 and hp2 = .004, a small effect size.

 

Perceptions of Classroom Climate

Finally, research question 4 investigated whether fifth-grade treatment group students would perceive an improvement in classroom climate as compared to students in the comparison group. Due to attrition, 308 fifth-grade students completed the four scales (satisfaction, cohesion, competitiveness and friction) of the MCI-SFR. Findings from an ANOVA using the MCI-SFR satisfaction scale posttest scores revealed a statistically significant difference between the treatment and comparison groups, F(1, 305) = 5.523, p = .019 and hp2 = .02, a small effect size. In particular, students in the treatment group (n = 187, M = 10.96, SD = 2.86) reported higher scores on the satisfaction scale at posttest than did students in the comparison group (n = 121, M = 10.39, SD = 2.74). The ANOVA tests on the other three scales of the MCI-SFR did not result in statistically significant differences between the treatment and comparison groups.

 

Discussion

 

The findings of this study reflect the connection between prosocial skills and reduced aggression, a finding which has been well documented in previous literature (Endresen & Olweus, 2001; Feshbach, 1997; McMahon & Washburn, 2003). School counselor interventions that focus on teaching prosocial behaviors have been successful in reducing aggressive behaviors such as bullying (Frey, Hirschstein, & Guzzo, 2000); these types of interventions also have been tied to improved academic achievement (Wentzel, 2003; Wentzel & Caldwell, 1997). The American School Counselor Association (ASCA; 2012) recommends that counselors cover academic, personal and social, and career domains as part of a comprehensive school counseling program. Results of this study support the delivery of interventions that incorporate the teaching of cognitive, social and self-management skills as a means to increase prosocial skills, reduce bullying behavior and promote a positive classroom climate. The design of the current study attempted to create a whole-school approach by implementing the SSS classroom program across an entire grade level (grade 5) in the treatment schools. Given that bullying peaks in the transition years, addressing the fifth-grade population was viewed as a proactive approach. SSS implementation resulted in some positive outcomes for those students, indicating that even a modified whole-school approach can be beneficial.

 

Previous SSS studies have documented the intervention’s positive impact on student academic performance as measured by standardized test scores in math and reading (Villares et al., 2012). Professionals in the field of counseling have identified a need to evaluate the link between the SSS program and intermediate variables related to student learning such as engagement in school success skills, prosocial behavior and perceptions of classroom climate (Carey, Dimmitt, Hatch, Lapan, & Whiston, 2008). Findings from the current study indicate that students who received the SSS intervention engaged significantly more in behaviors indicative of school success at posttest. These results are encouraging, since a body of research cites the negative impact that bullying can have on student academic achievement (Beale & Scott, 2001; Boulton et al., 2008; Nansel et al., 2001; Olweus, 1993).

 

The quality of a classroom climate also can impact students’ success. Although improved perceptions of classroom climate were predicted across all areas in the current study, statistically significant differences were only noted on perceptions related to satisfaction. The researchers postulate that treatment students were more likely to tune into questions pertaining to satisfaction, as this is a focus of the SSS program (noticing small improvements, focusing on the positives, and creating a safe, caring, supportive, encouraging classroom). The maintenance of a positive school and classroom climate directly affects whether or not students feel accepted and happy among their peers (Greenberg et al., 2003; Millings, Buck, Montgomery, Spears, & Stallard, 2012; Shochet, Dadds, Ham, & Montague, 2006). The literature indicates that the effectiveness of school counseling interventions can be greatly impacted by the school’s climate (Greenberg et al., 2003). Specifically, factors such as teacher adherence to the curriculum and staff buy-in can affect a program’s success (Biggs, Vernberg, Twemlow, Fonagy, & Dill, 2008; Yoon, 2004). Teachers should be involved in program implementation so that they become invested in its success. The current study addressed this area in that the classroom teachers were collaborators in SSS implementation. The program asks that classroom teachers be present during the counselor-led sessions so that they can cue students to use the skills taught throughout the regular school day. Thus, evidence-based interventions like the SSS program that emphasize school connectedness can be of benefit to students (Millings et al., 2012).

 

Implications for Practice and Future Research

 

The findings of this study support the use of the school counselor-led SSS classroom program as a practical means of impacting students’ prosocial skills, bullying behavior, engagement in school success skills and some perceptions of classroom climate, as indicated by various student self-report measures. Since the bullying literature calls for the use of multiple measures when attempting to link interventions to improvements, we recommend that additional studies track attendance rates, disciplinary referrals, bullying incident reports, and peer and teacher nominations, in addition to student instruments. Future researchers in this area also should gather data from teacher participants and vary the type of measurements specifically tied to prosocial and bullying behaviors (Pellegrini & Bartini, 2000; Van Schoiack-Edstrom, Frey, & Beland, 2002), as well as academic outcomes (Carey et al., 2008; Hall, 2006). This study sought to create a whole-school culture by incorporating the intervention across an entire grade level at each school. Future researchers might consider implementing SSS across several grade levels or throughout the entire school, as students across various grades often come in contact with one another throughout the school day.

 

Limitations

The participants were derived from one suburban school district and randomization procedures were not possible, thereby limiting the sample size and generalizability of the results. Likewise, due to one school dropping out of the study at the onset, the numbers between the treatment and comparison groups were not equivalent. The high level of attrition also was a limitation, specifically regarding the SESSS instrument. Though 336 students were in the original sample, only 200 of these were included in the analysis on the SESSS due to dropping out or not adequately completing the instrument in its totality at all three intervals.

 

The self-report nature of all three of the instruments was an added limitation, particularly with the problem of bullying. Students involved in bullying incidents, whether they were bullies, victims or bystanders, might be hesitant to report or indicate negative behaviors. This reluctance could have resulted in respondent bias and decreased reliability in the results.

 

Finally, the current study used only one component of the SSS curriculum (classroom program). Future studies might involve additional modalities, including individual and small group counseling as well as parent involvement. This study did not examine the impact of the SSS program over time. Follow-up studies are needed to support the long-term effectiveness of school counselor-led interventions that increase prosocial behaviors, reduce bullying behaviors and promote a positive school climate.

 

Conclusion

 

Results of the study provide support that students who receive the SSS classroom intervention led by school counselors (Brigman & Webb, 2010) evidence statistically significant differences in prosocial behaviors, bullying behaviors, engagement in school success skills and perceptions related to satisfaction with their classroom climate, as compared to students who do not receive the program. The findings provide empirical support for the notion that when students are taught skills in key areas (personal and social, self-management, and cognitive and academic) they benefit across social, emotional and behavioral outcomes. The study also suggests that aggressive behaviors such as bullying can be influenced by programs that do not specifically target these behaviors. Finally, this research points to the positive impact school counselors can have on student success, particularly when they deliver interventions that promote social competence among students. Providing school counselors with an evidence-based program that impacts students across several domains is of great value for school counseling practice.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

References

 

American School Counselor Association. (2012). The ASCA national model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.

Beale, A. V., & Scott, P. C. (2001). “Bullybusters”: Using drama to empower students to take a stand against bullying behavior. Professional School Counseling, 4, 300–305.

Bernstein, J. Y., & Watson, M. W. (1997). Children who are targets of bullying: A victim pattern. Journal of Interpersonal Violence, 12, 483–498. doi:10.1177/088626097012004001

Biggs, B. K., Vernberg, E. M., Twemlow, S. W., Fonagy, P., & Dill, E. J. (2008). Teacher adherence and its relation to teacher attitudes and student outcomes in an elementary school-based violence prevention program. School Psychology Review, 37, 533–549.

Boulton, M. J., Trueman, M., & Murray, L. (2008). Associations between peer victimization, fear of future victimization and disrupted concentration on class work among junior school pupils. British Journal of Educational Psychology, 78, 473–489. doi:10.1348/000709908X320471

Bradshaw, C. P., O’Brennan, L. M., & Sawyer, A. L. (2008). Examining variation in attitudes toward aggressive retaliation and perceptions of safety among bullies, victims, and bully/victims. Professional School Counseling, 12, 10–21. doi:10.5330/PSC.n.2010-12.10

Brigman, G., & Campbell, C. (2003). Helping students improve academic achievement and school success behavior. Professional School Counseling, 7, 91–98.

Brigman, G., & Webb, L. (2010). Student success skills: Classroom manual (3rd ed.). Boca Raton, FL: Atlantic Education Consultants.

Brigman, G. A., Webb, L. D., & Campbell, C. (2007). Building skills for school success: Improving the academic and social competence of students. Professional School Counseling, 10, 279–288.

Brigman, G., Wells, C., Webb, L., Villares, E., Carey, J. C., & Harrington, K. (2014). Psychometric properties and confirmatory factor analysis of the student engagement in school success skills survey. Measurement and Evaluation in Counseling and Development. doi:10.1177/0748175614544545

Campbell, C. A., & Brigman, G. (2005). Closing the achievement gap: A structured approach to group counseling. Journal for Specialists in Group Work, 30, 67–82. doi:10.1080/01933920590908705

Carey, J., Brigman, G., Webb, L., Villares, E., & Harrington, K. (2013). Development of an instrument to measure student use of academic success skills: An exploratory factor analysis. Measurement and Evaluation in Counseling and Development, 47, 171–180. doi:10.1177/0748175613505622

Carey, J. C., Dimmitt, C., Hatch, T. A., Lapan, R. T., & Whiston, S. C. (2008). Report of the national panel for evidence-based school counseling: Outcome research coding protocol and evaluation of student success skills and second step. Professional School Counseling, 11, 197–206.

Carney, J. V. (2008). Perceptions of bullying and associated trauma during adolescence. Professional School Counseling, 11, 179–188.

Catalano, R. F., Haggerty, K. P., Oesterle, S., Fleming, C. B., & Hawkins, J. D. (2004). The importance of bonding to school for healthy development: Findings from the social development research group. Journal of School Health, 74, 252–261. doi:10.1111/j.1746-1561.2004.tb08281.x

Cook, T. D., & Campbell, D. T. (1979). Quasi-Experimentation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin.

Crothers, L. M., & Levinson, E. M. (2004). Assessment of bullying: A review of methods and instruments. Journal of Counseling & Development, 82, 496–503. doi:10.1002/j.1556-6678.2004.tb00338.x

Daly, E. J., III, Duhon, G. J., & Witt, J. C. (2002). Proactive approaches for identifying and treating children at risk for academic failure. In K. L. Lane, F. M. Gresham, & T. E. O’Shaughnessy (Eds.), Interventions for children with or at risk for emotional and behavioral disorders (pp. 18–32). Boston, MA: Allyn & Bacon.

Deluty, R. H. (1985). Cognitive mediation of aggressive, assertive, and submissive behavior in children. International Journal of Behavioral Development, 8, 355–369. doi:10.1177/016502548500800309

DeVoe, J. F., Peter, K., Kaufman, P., Miller, A., Noonan, M., Snyder, T. D., & Baum, K. (2004). Indicators of school crime and safety: 2004. Retrieved from http://nces.ed.gov/pubs2005/2005002.pdf

Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82, 405–32. doi:10.1111/j.1467-8624.2010.01564.x

Endresen, I. M., & Olweus, D. (2001). Self-reported empathy in Norwegian adolescents: Sex differences, age trends, and relationship to bullying. In A. C. Bohart & D. J. Stipek (Eds.), Constructive & destructive behavior: Implications for family, school, and society (pp.147–165). Washington, DC: American Psychological Association.

Feshbach, N. D. (1997). Empathy: The formative years—implications for clinical practice. In A. C. Bohart & L. S. Greenberg (Eds.), Empathy reconsidered: New directions in psychotherapy (pp. 33–59). Washington, D.C.: American Psychological Association.

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Thousand Oaks, CA: Sage.

Fraser, B. J. (1982). Development of short forms of several classroom environment scales. Journal of Educational Measurement, 19, 221–227.

Frey, K. S., Hirschstein, M. K., & Guzzo, B. A. (2000). Second step: Preventing aggression by promoting social competence. Journal of Emotional and Behavioral Disorders, 8, 102–113. doi:10.1177/106342660000800206

Gini, G., & Pozzoli, T. (2009). Association between bullying and psychosomatic problems: A meta-analysis. Pediatrics, 123, 1059–1065. doi:10.1542/peds.2008-1215

Green, S. B., & Salkind, S. J. (2008). Using SPSS for Windows and Macintosh: Analyzing and understanding data (5th ed.). Upper Saddle River, NJ: Prentice-Hall.

Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58, 466–474. doi:10.1037/0003-066X.58.6-7.466

Hall, K. R. (2006). Using problem-based learning with victims of bullying behavior. Professional School Counseling, 9, 231–237.

Hanish, L. D., & Guerra, N. G. (2000). Children who get victimized at school: What is known? What can be done? Professional School Counseling, 4, 113–119.

Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66, 99–136. doi:10.3102/00346543066002099

Hazler, R. J., & Carney, J. V. (2000). When victims turn aggressors: Factors in the development of deadly school violence. Professional School Counseling, 4, 105–112.

Hermann, M. A., & Finn, A. (2002). An ethical and legal perspective on the role of school counselors in preventing violence in schools. Professional School Counseling, 6, 46–54.

Howell, D. (2008). Best practices in the analysis of variance. In J. Osborne (Ed.), Best practices in quantitative methods (pp. 341–357). Thousand Oaks, CA: Sage.

León, A., Villares, E., Brigman, G., Webb, L., & Peluso, P. (2011). Closing the achievement gap of Latina/Latino students: A school counseling response. Counseling Outcome Research and Evaluation, 2, 73–86. doi:10.1177/2150137811400731

Masten, A. S., & Coatsworth, J. D. (1998). The development of competence in favorable and unfavorable environments: Lessons from research on successful children. American Psychologist, 53, 205–220.

McMahon, S. D., & Washburn, J. J. (2003). Violence prevention: An evaluation of program effects with urban African American students. The Journal of Primary Prevention, 24, 43–62.

Millings, A., Buck, R., Montgomery, A., Spears, M., & Stallard, P. (2012). School connectedness, peer attachment, and self-esteem as predictors of adolescent depression. Journal of Adolescence, 35, 1061–1067. doi:10.1016/j.adolescence.2012.02.015

Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. Journal of the American Medical Association, 285, 2094–2100. doi:10.1001/jama.285.16.2094

Olweus, D. (1993). Bullying at school. Malden, MA: Blackwell.

Ortega, R., & Lera, M.-J. (2000). The Seville anti-bullying in school project. Aggressive Behavior, 26, 113–123. doi:10.1002/(SICI)1098-2337(2000)26:1<113::AID-AB9>3.0.CO;2-E

Payton, J., Weissberg, R. P., Durlak, J. A., Dymnicki, A. B., Taylor, R. D., Schellinger, K. B., & Pachan, M. (2008). The positive impact of social and emotional learning for kindergarten to eighth-grade students: Findings from three scientific reviewsExecutive summary. Retrieved from http://www.lpfch.org/sel/PackardES-REV.pdf

Pellegrini, A. D., & Bartini, M. (2000). An empirical comparison of methods of sampling aggression and victimization in school settings. Journal of Educational Psychology, 92, 360–366. doi:10.1037/0022-0663.92.2.360

Reijntjes, A., Kamphuis, J. H., Prinzie, P., & Telch, M. J. (2010). Peer victimization and internalizing problems in children: A meta-analysis of longitudinal studies. Child Abuse & Neglect, 34, 244–252. doi:10.1016/j.chiabu.2009.07.009

Rigby, K., & Slee, P. T. (1993a). Dimensions of interpersonal relation among Australian children and implications for psychological well-being. The Journal of Social Psychology, 133, 33–42. doi:10.1080/00224545.1993.9712116

Rigby, K., & Slee, P. T. (1993b). The Peer Relations Questionnaire (PRQ) [Measurement instrument]. Adelaide, Australia: University of South Australia.

Roth, D. A., Coles, M. E., & Heimberg, R. G. (2002). The relationship between memories for childhood teasing and anxiety and depression in adulthood. Journal of Anxiety Disorders, 16, 149–164. doi: 10.1016/S0887-6185(01)00096-2

Saleebey, D. (2008). Commentary on the strengths perspective and potential applications in school counseling. Professional School Counseling, 12, 68–75.

Shochet, I. M., Dadds, M. R., Ham, D., & Montague, R. (2006). School connectedness is an underemphasized parameter in adolescent mental health: Results of a community prediction study. Journal of Clinical Child and Adolescent Psychology, 35, 170–179. doi:10.1207/s15374424jccp3502_1

Sink, C. A., & Mvududu, N. H. (2010). Statistical power, sampling, and effect sizes: Three keys to research relevancy. Counseling Outcome Research and Evaluation, 1, 1–18. doi:10.1177/2150137810373613

Sink, C. A., & Spencer, L. R. (2005). My Class Inventory–Short Form as an accountability tool for elementary school counselors to measure classroom climate. Professional School Counseling, 9, 37–48.

Spriggs, A. L., Iannotti, R. J., Nansel, T. R., & Haynie, D. L. (2007). Adolescent bullying involvement and perceived family, peer, and school relations: Commonalities and differences across race/ethnicity. Journal of Adolescent Health, 41, 283–293. doi:10.1016/j.jadohealth.2007.04.009

Swearer, S. M., Espelage, D. L., Vaillancourt, T., & Hymel, S. (2010). What can be done about school bullying? Linking research to educational practice. Educational Researcher, 39, 38–47.

Tabaeian, S. R., Amiri, S., & Molavi, H. (2012). Factor analysis, reliability, convergent and discriminate [sic] validity of “The Peer Relationships Questionnaire” (PRQ). Studies in Learning & Instruction (Journal of Social Sciences and Humanities of Shiraz University), 3(2), 61–62.

Ttofi, M. M., Farrington, D. P., Lösel, F., & Loeber, R. (2011). Do the victims of school bullies tend to become depressed later in life? A systematic review and meta-analysis of longitudinal studies. Journal of Aggression, Conflict and Peace Research, 3, 63–73. doi:10.1108/17596591111132873

Van Schoiack-Edstrom, L., Frey, K. S., & Beland, K. (2002). Changing adolescents’ attitudes about relational and physical aggression: An early evaluation of a school-based intervention. School Psychology Review, 31, 201–216.

Villares, E., Colvin, K., Carey, J., Webb, L., Brigman, G., & Harrington, K. (2014). Convergent and divergent validity of the student engagement in school success skills survey. The Professional Counselor, 4, 541–552. doi:10.15241/ev.4.5.541

Villares, E., Frain, M., Brigman, G., Webb, L., & Peluso, P. (2012). The impact of student success skills on standardized test scores: A meta-analysis. Counseling Outcome Research and Evaluation, 3, 3–16. doi:10.1177/2150137811434041

Wang, J., Iannotti, R. J., & Nansel, T. R. (2009). School bullying among adolescents in the United States: Physical, verbal, relational, and cyber. Journal of Adolescent Health, 45, 368–375. doi:10.1016/j.jadohealth.2009.03.021

Wang, M. C., Haertel, G. D., & Walberg, H. J. (1994). What helps students learn? Educational Leadership, 51(4), 74–79.

Webb, L. D., & Brigman, G. A. (2006). Student success skills: Tools and strategies for improved academic and social outcomes. Professional School Counseling, 10, 112–120.

Webb, L. D., Brigman, G. A., & Campbell, C. (2005). Linking school counselors and student success: A replication of the student success skills approach targeting the academic and social competence of students. Professional School Counseling, 8, 407–413.

Wentzel, K. R. (2003). Sociometric status and adjustment in middle school: A longitudinal study. Journal of Early Adolescence, 23, 5–28. doi:10.1177/0272431602239128

Wentzel, K. R., & Caldwell, K. (1997). Friendships, peer acceptance, and group membership: Relations to academic achievement in middle school. Child Development, 68, 1198–1209. doi:10.2307/1132301

Winne, P. H., & Nesbit, J. C. (2010). The psychology of academic achievement. Annual Review of Psychology, 61, 653–678.

Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81, 267–301. doi:10.3102/0034654311405999

Yoon, J. S. (2004). Predicting teacher interventions in bullying situations. Education and Treatment of Children, 27, 37–45.

Zins, J. E., Weissberg, R. P., Wang, M. C., & Walberg, H. J. (Eds.). (2004). Building academic success on social and emotional learning: What does the research say? New York, NY: Teachers College Press.

 

 

Melissa Mariani is an Assistant Professor at Florida Atlantic University. Linda Webb is Research Faculty III at Florida State University. Elizabeth Villares is an Associate Professor at Florida Atlantic University. Greg Brigman, NCC, is a Professor at Florida Atlantic University. Correspondence may be addressed to Melissa Mariani, 777 Glades Road, COE 47, 274, Boca Raton, FL 33431, mmarian5@fau.edu.

 

Differences in College Greek Members’ Binge Drinking Behaviors: A Dry/Wet House Comparison

Kathleen Brown-Rice, Susan Furr

College Greek life students self-report high rates of binge drinking and experience more alcohol-related problems than students who are not members of the Greek system. But little research has been conducted to measure differences in alcohol-free housing (dry) and alcohol-allowed housing (wet). The purpose of this quantitative study was to investigate the alcohol consumption of Greek houses (dry sorority, wet fraternity, dry fraternity). It was found that in the Greek community, university students’ scores on the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) were significantly lower for dry sorority housing members than both the wet fraternity and dry fraternity housing members, with no significant difference found between the wet and dry fraternity participants. Regardless of type, Greek-affiliated students’ drinking levels appear to be high and exceed what is considered safe on the AUDIT-C for both female and male Greek students.

 

Keywords: binge drinking, college students, AUDIT-C, Greek system, wet/dry housing      

 

Throughout the literature, research findings indicate that university students affiliated with the Greek system consume more alcohol and experience more alcohol-related problems than students who are not members of the Greek system (Barry, 2007; Borsari, Hustad, & Capone, 2009; Ragsdale et al., 2012). In particular, self-reported binge drinking is significantly higher among members of this community (Barry, 2007; Chauvin, 2012; Page & O’Hegarty, 2006). Research also indicates that students who come to college with a prior drinking history may seek out venues for continuing this behavior in college, as indicated by the variable of high school binge drinking being the best predictor of Greek student binge drinking (Chauvin, 2012). Borsari et al. (2009) concluded that students who use alcohol heavily in high school may self-select into the Greek system in order to find an environment supportive of their behavior. However, it also has been found that students who join a fraternity in their first year significantly increase their drinking and alcohol-related consequences compared to those who do not join (Park, Sher, & Krull, 2008).

 

Consequences of Binge Drinking

 

There are numerous costs associated with college students engaging in binge drinking behaviors, both to the students themselves and others. It is estimated that per university, the total yearly cost of alcohol-related emergency department visits is around $500,000 (Mundt & Zakletsaia, 2012). Negative consequences of binge drinking can range in severity from a hangover to alcohol-related problems with law enforcement to suicide attempts (Gillespie, Holt, & Blackwell, 2007). Alcohol consumption among undergraduate college students contributes annually to an estimated 600,000 alcohol-related unintentional injuries, 700,000 assaults by another student who was drinking, 1,500 alcohol-related student deaths, 97,000 sexual assaults, 400,000 acts of alcohol-related unprotected sex and 100,000 incidences of being too intoxicated to know if sex was consensual (Hingson, Zha, & Weitzman, 2009). Further, it has been found that 50% of men who commit rape on college campuses were drinking at the time of the offense (Cole, 2006), and women who drink on college campuses are more likely to be the victim of a sexual assault (McCauley, Calhoun, & Gidycz, 2010).

 

The literature provides that college students who are members of the Greek community are at greater risk for experiencing negative consequences from heavy drinking (LaBrie, Kenney, Mirza, & Lac, 2011; Nguyen, Walters, Rinker, Wyatt, & DeJong, 2011; Soule, Barnett, & Moorhouse, 2015). Fraternity and sorority membership has been positively associated with driving after drinking (LaBrie et al., 2011) and owning a fake ID (Nguyen et al., 2011). Fraternity and sorority members reported that they were twice as likely as non-Greek college students to engage in sex with someone without getting consent and were one and a half times more likely to forget what they did or where they were after drinking (Soule et al., 2015). In fact, sorority members who binge drink are significantly more likely to be injured, drive under the influence of alcohol, be sexually victimized and engage in unwanted sex than non-Greek female binge drinkers (Ragsdale et al., 2012). Given that Greek membership and binge drinking are correlated with more severe negative consequences and that fraternity and sorority members report more peer pressure to drink (Knee & Neighbors, 2002; Young, Morales, McCabe, Boyd, & D’Arcy, 2005), it is important to consider the effect of the type of housing on college student drinking behaviors.

 

Alcohol-Free University Housing

 

Because of the influence of the Greek housing environment on drinking norms, interventions at the residential level have been cited as a strategy for reducing risky drinking levels (Borsari et al., 2009). But what happens when alcohol-free policies are implemented? Do levels of risky drinking decrease? Examining alcohol-free Greek housing in general provides a mixed picture of results. First, at colleges that only allow dry housing, students are significantly less likely to drink alcohol than students at wet schools (29.1% abstainers at dry schools versus 16.1% abstainers at wet schools). But when examining only those students who report drinking while attending colleges that ban alcohol, their drinking patterns do not differ from drinkers at non-ban schools (Wechsler, Lee, Gledhill-Hoyt, & Nelson, 2001). Overall, there are lower rates of secondhand effects of alcohol use (e.g., insults, serious arguments, property damage, interrupted sleep) at schools where alcohol is banned. In residences where both alcohol and smoking are banned, there are lower levels of drinking, but not in residences where only alcohol is banned. Wechsler, Lee, Nelson, and Lee (2001) concluded that this type of substance-free residence may help protect those students who were not heavy drinkers in high school from becoming engaged in episodic drinking in college, but it does not lower drinking levels among those who did drink heavily in high school. It appears that students who are not heavy drinkers in high school are more likely to choose substance-free housing in college.

 

Colleges also have attempted to establish alcohol-free events as a means of decreasing alcohol use on campus. Wei, Barnett, and Clark (2010) found that during the semester that was surveyed, less than half of the students (43.9%) attended an alcohol-free party. However, for students who attended both alcohol and alcohol-free parties, their level of alcohol consumption and intoxication was lower on the nights of the alcohol-free events versus their typical drinking nights. In another study, it was found that students drank less on days they attended alcohol-free programming than when they went to other events where alcohol was present, drinking 41% fewer drinks on the evenings of late-night planned activities (Patrick, Maggs, & Osgood, 2010).

Greek Life Housing

 

The question remains as to how these results apply to the Greek system. Greek housing has been found to create an enabling environment for drinking (Ashmore, Del Boca, & Beebe, 2002; Borsani et al., 2009; Glindemann & Geller, 2003; Harford, Wechsler, & Seibring, 2002; Paschall & Saltz, 2007). There has been some movement toward fraternities establishing alcohol-free housing as a means of reducing risky drinking. Sororities have a history of providing alcohol-free houses, yet members still display higher levels of drinking than students who are not members of sororities (Ragsdale et al., 2012). In general, implementation of alcohol-free housing has not been found to reduce high levels of drinking (Crosse, Ginexi, & Caudill, 2006). In a study of one national college fraternity, Caudill et al. (2006) found that chapters that implement an alcohol-free policy have almost identical drinking levels compared to chapters that do not have an alcohol-free policy. However, fraternities continue to grapple with reducing the impact of alcohol use on their chapters in terms of issues such as the deterioration of living facilities and stabilizing rising liability insurance costs through the development of guidelines for alcohol-free fraternity housing (Whipple, 2005). Thus, there is limited research on whether there are any differences in drinking behaviors based upon type of Greek housing and whether decreases in drinking occur over time.

 

Based on a quantitative study of an alcohol-free fraternity, Robison (2007) found that members joined for environmental factors such as cleaner living conditions, better academic conditions, the ability to separate home and party life, and friendships built on a common bond. Most of the members did drink but drank at different locations. The fraternity was able to maintain its membership through focusing on recruitment, promoting the benefits of environmental factors, providing social alternatives, focusing on brotherhood and friendship, and enforcing alcohol-related rules. Information was not provided for drinking levels, but through examining grade point average, Robinson stated that this fraternity consistently ranked in the top tier academically. However, by-products of alcohol consumption still occurred, such as disturbing the peace, vandalism and threatening behavior. In some cases, students created other opportunities for drinking, such as car bars, where members would park a car in a nearby location and drink from the car. Therefore, it would appear that dry houses have a different set of risk factors. As with some of the other descriptions of alcohol-free fraternities, information on level of drinking was not reported.

 

Given that Greek membership is correlated with more negative consequences when members drink (LaBrie et al., 2011; Nguyen et al., 2011; Soule et al., 2015) and that there is a lack of research determining the differences in binge drinking based upon type of Greek housing and across an academic year, the purpose of the current study was to investigate the alcohol consumption of Greek houses (dry sorority, wet fraternity, dry fraternity) for two independent samples (fall and spring semesters). It is the policy of the National Panhellenic Council that College Panhellenic planned or sponsored events be alcohol free (National Panhellenic Conference, 2015). At this university, there were no sorority houses that allowed alcohol, but the inclusion of data on the drinking patterns of female members provides another aspect of drinking patterns of those involved in the Greek community. We hypothesized that members of dry sorority houses would report lower alcohol consumption than members of wet and dry fraternity houses for both fall and spring semesters, and that members of dry fraternity houses would report lower alcohol consumption than members of wet fraternity houses for both fall and spring semesters.

 

Methodology

 

Participants and Procedures

The population for this study was students residing in Greek housing at a Midwestern university during the 2012–2013 academic year (N = 735). Recruitment of participants was conducted to obtain two independent samples in the fall semester of 2012 and the spring semester of 2013 via announcements at fraternity and sorority chapter meetings. A total of 385 Greek members living in Greek housing took part in the fall recruitment, resulting in a response rate of 50.3%. Respondents with missing or invalid data (n = 22, less than 6%) were eliminated via listwise deletion, leaving a total number of 363 participants who were classified in the fall semester group. For spring, 379 Greek members participated, resulting in a response rate of 49.5%. Respondents with missing or invalid data (n = 7, less than 2%) were eliminated via listwise deletion, leaving a total number of 372 participants classified in the spring semester group.

 

During regular scheduled house meetings, the first author asked participants to complete a researcher-designed survey consisting of five demographic questions (i.e., Greek house, gender, age, cultural/racial background, academic year). The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) was utilized to obtain information about participants’ alcohol use (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001; Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998). Prior to administration, the participants were provided with narrative and visual aids that defined one drink as one 12-ounce beer, one 8.5-ounce malt beverage, one 5-ounce glass of wine, one mixed drink containing one (1.5-ounce) shot of alcohol, or one single (1.5-ounce) shot of liquor. On the Audit-C, the following three questions assess frequency of drinking: (a) How often do you have a drink containing alcohol? (Never = 0 points, Monthly or less = 1 point, 2–4 times a month = 2 points, 2–3 times a week = 3 points, 4 or more times a week = 4 points); (b) How many drinks containing alcohol do you have on a typical day when you are drinking? (1 or 2 = 0 points, 3 or 4 = 1 point, 5 or 6 = 2 points, 7–9 = 3 points, 10 or more = 4 points); and (c) How often do you have six or more drinks on one occasion? (Never = 0 points, Less than monthly = 1 point, Monthly = 2 points, Weekly = 3 points, Daily or almost daily = 4 points). Responses to each item are scored from 0–4, generating a maximum possible score on the AUDIT-C of 12. Higher scores reflect higher intensity of drinking. For men a score of 4 or above and for women a score of 3 or above indicates hazardous drinking or an active alcohol use disorder (Bush et al., 1998). The AUDIT-C has been found to be a valid screening tool for alcohol misuse for men and women, with optimal screening thresholds for alcohol misuse among men being a score of 4 and for women a score of 3 (Bradley et al., 2007; Frank et al., 2008), and valid and reliable for assessing alcohol consumption in college students (Barry, Chaney, Stellefson, & Dodd, 2015). Prior to each administration of the survey, the purposes and procedures of the study, confidentiality of data, and participants’ rights were explained to respondents. All participants gave informed consent prior to completing the survey. All procedures were approved by the first author’s Institutional Review Board, and participants were not offered any incentive for completing the survey. Demographic information regarding participants for fall and spring semesters is provided
in Table 1.

 

Data Analysis

The Statistical Package for Social Sciences software (version 21) was utilized to screen and analyze the data. All statistical analyses are reported with alpha set at 0.05. Preliminary analyses were conducted to check the data for any outliers or errors, and no violations of linearity, normality and homoscedasticity were found. The frequencies of each variable were checked for minimums and maximums. Again, no errors or outliers were found.

 

 

Table 1

 

Demographic Variables by Group

Fall Semester 2012

Spring Semester 2013

n

%

n

%

Greek House:
 Sorority – Dry

148

40.8

234

62.9

 Fraternity – Dry

  50

13.8

  58

15.6

 Fraternity – Wet

165

45.5

  80

21.5

Gender:
  Female

148

40.8

234

62.9

  Male

215

59.2

138

37.1

Age:
  18–20

268

73.8

287

77.2

  21 and older

  95

26.2

  85

22.8

Ethnicity:
African American

    2

   .6

    5

   1.3

Asian/Pacific Islander

    3

   .8

    4

   1.1

Caucasian

344

94.8

351

 94.4

Hispanic/Latino

    1

    .3

    2

     .5

Native American

    5

  1.4

    4

   1.1

Multi-Racial

    8

  2.2

    6

   1.6

Academic Year:
  Freshman

   84

 23.1

 107

   28.8

  Sophomore

  128

 35.3

 138

   37.1

  Junior

    82

 22.6

   74

   19.9

  Senior

   69

 19.0

   51

   13.7

  Graduate

     0

     0

     2

       .5

 

Results

 

For the fall semester sample, a one-way analysis of variance (ANOVA) was used to test for AUDIT-C score differences among the Greek house variable. AUDIT-C scores differed significantly across the three house categories, F(2, 360) = 39.958, p = .000. Scheffe post-hoc comparisons of the three groups indicated that the sorority dry house group (M = 5.02, 95% CI [4.60, 5.44]) had significantly lower scores than the fraternity dry house group (M = 7.94, 95% CI [7.40, 8.48]), p = .000 and the fraternity wet house group (M = 7.42, 95% CI [6.97, 7.88]), p = .000. AUDIT-C scores were not significantly different between the fraternity dry house group and the fraternity wet house group at p = .489. When looking specifically at how often participants consume six or more drinks on one occasion, significant differences were found among the Greek house variable. AUDIT-C scores differed significantly across the three house categories, F(2, 360) = 40.858, p = .000. Scheffe post-hoc comparisons of the three groups indicated that the sorority dry house group (M = 1.22, 95% CI [1.07, 1.38]) had significantly lower scores than the fraternity dry house group (M = 2. 40, 95% CI [2.17, 2.63]), p = .000 and the fraternity wet house group (M = 2.10, 95% CI [1.93, 2.26]), p = .000. Scores were not significantly different between the fraternity dry house group and the fraternity wet house group at p = .175.

 

When looking at the spring respondents, a one-way ANOVA showed that AUDIT-C scores differed significantly across the three Greek house categories, F(2, 369) = 9.526, p = .000. Scheffe post-hoc comparisons of the three groups indicated that the sorority dry group (M = 4.76, 95% CI [4.41, 5.11]) had significantly lower scores than the fraternity dry house group (M = 5.97, 95% CI [5.15, 6.79]), p = .011 and the fraternity wet house group (M = 6.09, 95% CI [5.48, 6.70]), p = .001. AUDIT-C scores were not significantly different between the fraternity dry house group and the fraternity wet house group at p = .967. When looking specifically at how often participants consume six or more drinks on one occasion, significant differences among the Greek house variable were found. AUDIT-C scores differed significantly across the three house categories, F(2, 369) = 10.450, p = .000. Scheffe post-hoc comparisons of the three groups indicated that the sorority dry house group (M = 1.07, 95% CI [.95, 1.19]) had significantly lower scores than the fraternity dry house group (M = 1.57, 95% CI [1.29, 1.85]), p = .002 and the fraternity wet house group (M = 1.53, 95% CI [1.30, 1.75]), p = .002. Scores were not significantly different between the fraternity dry house group and the fraternity wet house group at p = .966.

These findings supported our hypothesis that members of dry sorority houses would report lower alcohol consumption than members of wet and dry fraternity houses for both fall and spring semesters. However, the second hypothesis, that members of dry fraternity houses would report lower alcohol consumption than members of wet fraternity houses for both fall and spring, was not supported. Table 2 details Greek house scores for the three AUDIT-C questions.

 

 

Table 2

 

Mean Scores and Standard Deviations by Semester and Greek House Responses to AUDIT-C Questions

Question by House

      Fall Semester 2012

 Spring Semester 2013

n

M

  SD

n

M

 SD

Question 1:
  Sorority – Dry 148 1.96   .95 234 1.76   .92
  Fraternity – Dry   50 2.50   .71 58 2.03   .99
  Fraternity – Wet 165 2.45   .97 80 2.21   .94
Question 2:
  Sorority – Dry 148 1.89   .98 234 1.91 1.11
  Fraternity – Dry   50 3.10   .86 58 2.36 1.19
  Fraternity – Wet 165 2.87 1.18 80 2.35 1.19
Question 3:
  Sorority – Dry 148 1.22   .97 234 1.07   .93
  Fraternity – Dry   50 2.40   .81 58 1.57 1.06
  Fraternity – Wet 165 2.10 1.08 80 1.53 1.01
Total AUDIT-C:
  Sorority – Dry 148 5.02   .42 234 4.76   .35
  Fraternity – Dry   50 7.94   .54 58 5.97   .82
  Fraternity – Wet 165 7.42   .45 80 6.09   .61

 

 

Limitations

This study has four main limitations. First, this study used a convenience sample of Greek members from one university that is not likely to represent the population of all Greek members. The second limitation is that volunteers may have answered the survey questions differently than members of the population who did not agree to participate would have. Another limitation is that the samples might not be truly independent; some participants could have filled out the survey in both the fall and spring. The final limitation is related to the survey being a self-report measure; participants may have provided answers that did not represent their true behaviors. However, previous researchers have found a statistically significant relationship between college respondents’ self-reported alcohol use when compared to the report from a collateral informant (Hagman, Cohn, Noel, & Clifford, 2010; Laforge, Borsari, & Baer, 2005).

 

Discussion and Implications

 

Regardless of whether Greek houses have a dry or wet status, drinking levels appear to be high and exceed what is considered safe on the AUDIT-C for both men and women living in Greek housing. Sororities have generally had policies that prohibit alcohol use in sorority houses, yet report levels of drinking that are considered hazardous. The lack of differences in drinking levels between men who live in dry fraternity houses versus wet fraternity houses is disappointing, but not totally unexpected given previous studies (Caudill et al., 2006; Crosse et al., 2006). It appears that residents in the Greek system accept the norms of heavy drinking that are associated with Greek membership. Although members may have some benefits from living in dry houses, such as a cleaner environment and less disruption to academic performance, the risks of alcohol abuse continue.

 

The cross-sectional research provides the most interesting results, with a significant difference between drinking levels in the fall semester compared to the spring semester. In particular, a general linear univariate analysis revealed that the scores of the fall groups and the spring groups were significantly different, F(1,729) = 26.179, p = .000, with a significant interaction effect, F(2, 729) = 38.901, p = .005, where fraternity members, whether living in a dry or wet house, reported higher AUDIT-C scores than sorority members living in Greek housing. Because this study is not a repeated measures design, the results do not evaluate changes in individuals. It is not possible to determine whether some of the same students took the survey both semesters, but there is probably some overlap in the two populations. The one environmental change that occurred between the two assessment periods was the implementation of alcohol education programs that a majority of Greek students (75.8%) attended in the fall. We cannot determine that this educational program facilitated the decrease in risky drinking and need to further examine the possibility that continued programming about how to drink alcohol safely and the effects of acute alcohol intoxication may expand students’ knowledge and thus impact their choices. Another consideration may be football tailgating. Glassman, Dodd, Sheu, Rienzo, and Wagenaar (2010) assessed college students at one university to examine their extreme ritualistic alcohol consumption, which is defined as consuming 10 or more drinks on game day for a male, and eight or more drinks for a female. Glassman et al. found that participants who were male, White, a Greek community member and of legal drinking age reported disproportionately higher rates of alcohol consumption on game day. Although tailgating is not observed as a major event on this campus, there may be other variables that contributed to higher drinking levels in the fall semester versus the spring semester.

 

Directions for Future Research

 

This research study offers contributions and implications for professional counselors. As a result of these findings, some important considerations for future research have emerged. First, if Greek members in dry houses are engaging in risky drinking behaviors at the same degree as members in wet houses, it is important to ascertain where they are drinking since they are not allowed to drink in their residence. Consequently, examining where the drinking occurs and how the alcohol is obtained would be beneficial. If these students are selecting other avenues for drinking that may encourage risky behaviors, such as driving, then dry houses may present some additional risks that need to be addressed. Also, little is known about members of Greek organizations who live in non-Greek housing. Do these students engage in drinking patterns similar to those who live in Greek housing when they attend Greek activities? How might their drinking patterns change when involved in activities in their non-Greek setting? In addition, drinking patterns among females in the Greek system generally reflect risky drinking patterns. Even though alcohol is not permitted in the living environments of the sororities in this sample, females still drink at high levels. More investigation into the role that the interaction of fraternities and sororities plays in levels of drinking needs to conducted. The question of whether females drink more when engaged in fraternity activities needs to be addressed.

 

The second research consideration is related to other communities of which the Greek members may be a part. College athletes have been found to drink more alcohol and engage more often in binge drinking than non-athletes (Hildebrand, Johnson, & Bogle, 2001; Nelson & Wechsler, 2001). In fact, Huchting, Lac, Hummer, and LaBrie (2011) compared independent samples of Greek members’ and athletes’ drinking patterns and found that athletes experienced significantly greater conformity reasons for drinking (i.e., social pressures that push an individual to conform and engage in alcohol use) than Greek members. Greek members experienced significantly more social problems from drinking. However, it is unknown whether there are differences between drinking behaviors of Greek members who are athletes and those who are not. This could be important information to assist clinicians in determining where to target prevention strategies. The final research consideration relates to gaining a better understanding of how individual Greek member’s drinking patterns change over an academic year. Therefore, future studies should include identifiers for participants to determine whether individual changes occur.

 

Conclusion

 

Consistent with other research, banning alcohol in Greek housing does not appear to reduce levels of drinking. Students may benefit from alcohol-free environments for reasons other than reducing drinking, but alcohol-free environments seem to have little impact on student drinking behaviors. There may even be some concerns about the risks involved in drinking away from one’s residence such as driving while intoxicated. The larger issue around alcohol use in the Greek system is how to challenge the established drinking norms in ways that encourage students to drink safely. Helping students focus on the deeper meaning of Greek membership that promotes a sense of community and enhances the values of the fraternity or sorority may be a direction for future interventions.

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

References

 

Ashmore, R. D., Del Boca, F. K., & Beebe, M. (2002). “Alkie,” “frat brother,” and “jock”: Perceived types of college students and stereotypes about drinking. Journal of Applied Social Psychology, 32, 885–907. doi:10.1111/j.1559-1816.2002.tb00247.x

Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). AUDIT: The Alcohol Use Disorders Identification Test—Guidelines for use in primary care (2nd ed.) World Health Organization: Geneva, Switzerland.

Barry, A. E. (2007). Using theory-based constructs to explore the impact of Greek membership on alcohol-related beliefs and behaviors: A systematic literature review. Journal of American College Health56, 307–315. doi:10.3200/JACH.56.3.307-316

Barry, A. E., Chaney, B. H., Stellefson, M. L., & Dodd, V. (2015). Evaluating the psychometric properties of the AUDIT-C among college students. Journal of Substance Use, 20, 1–5. doi:10.3109/14659891.2013.856479

Borsari, B., Hustad, J. T. P., & Capone, C. (2009). Alcohol use in the Greek system, 1999–2009: A decade of progress. Current Drug Abuse Reviews, 2, 216–255.

Bradley, K. A., DeBenedetti, A. F., Volk, R. J., Williams, E. C., Frank, D., & Kivlahan, D. R. (2007). AUDIT-C as a brief screen for alcohol misuse in primary care. Alcoholism: Clinical & Experimental Research, 31, 1208–1217. doi:10.1111/j.1530-0277.2007.00403

Bush, K., Kivlahan, D. A., McDonell, M. B., Fihn, S. D., & Bradley, K. A. (1998). The AUDIT alcohol consumption questions (AUDIT-C): An effective brief screening test for problem drinking. Archives of Internal Medicine, 158, 1789–1795.

Caudill, B. D., Crosse, S. B., Campbell, B., Howard, J., Luckey, B., & Blane, H. T. (2006). High-risk drinking among college fraternity members: A national perspective. Journal of American College Health, 55, 141–155. doi:10.3200/JACH.55.3.141-155

Chauvin, C. D. (2012). Social norms and motivations associated with college binge drinking. Sociological Inquiry, 82, 257–281. doi:10.1111/j.1475-682X.2011.00400.x

Cole, T. B. (2006). Rape at US colleges often fueled by alcohol. Journal of the American Medical Association, 296, 504–505. doi:10.1001/jama.296.5.504

Crosse, S. B., Ginexi, E. M., & Caudill, B. D. (2006). Examining the effects of a national alcohol-free fraternity housing policy. The Journal of Primary Prevention, 27, 477–495. doi:10.1007/s10935-006-0049-5

Frank, D., DeBenedetti, A. F., Volk, R. J., Williams, E. C., Kivlahan, D. R., & Bradley, K. A. (2008). Effectiveness of the AUDIT-C as a screening test for alcohol misuse in three race/ethnic groups. Journal of General Internal Medicine, 23, 781–787. doi:10.1007/s11606-008-0594-0

Gillespie, W., Holt, J. L., & Blackwell, R. L. (2007). Measuring outcomes of alcohol, marijuana, and cocaine use among college students: A preliminary test of the shortened inventory of problems– alcohol and drugs (SIP-AD). Journal of Drug Issues, 37, 549–567.

Glassman, T. J., Dodd, V. J., Sheu, J.-J., Rienzo, B. A., & Wagenaar, A. C. (2010). Extreme ritualistic alcohol consumption among college students on game day. Journal of American College Health, 58, 413–423.

Glindemann, K. E., & Geller, E. S. (2003). A systematic assessment of intoxication at university parties: Effects of the environmental context. Environment and Behavior35, 655–664. doi:10.1177/0013916503254751

Hagman, B. T., Cohn, A. M., Noel, N. E., & Clifford, P. R. (2010). Collateral informant assessment in alcohol use research involving college students. Journal of American College Health, 59, 82–90. doi:10.1080/07448481.2010.483707

Harford, T. C., Wechsler, H., & Seibring, M. (2002). Attendance and alcohol use at parties and bars in college: A national survey of current drinkers. Journal of Studies on Alcohol, 63, 726–733.

Hildebrand, K. M., Johnson, D. J., & Bogle, K. (2001). Comparison of patterns of alcohol use between high school and college athletes and non-athletes. College Student Journal, 35, 358–365.

Hingson, R. W., Zha, W., & Weitzman, E. R. (2009). Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students aged 18–24, 1998–2005. Journal of Studies on Alcohol and Drugs Supplement, 16, 12–20.

Huchting, K. K., Lac, A., Hummer, J. F., & LaBrie, J. W. (2011). Comparing Greek-affiliated students and student athletes: An examination of the behavior-intention link, reasons for drinking, and alcohol-related consequences. Journal of Alcohol & Drug Education, 55, 61–81.

Knee, C. R., & Neighbors, C. (2002). Self-determination, perception of peer pressure, and drinking among college students. Journal of Applied Social Psychology, 32, 522–543. doi:10.1111/j.1559-1816.2002.tb00228.x

LaBrie, J. W., Kenney, S. R., Mirza, T., & Lac, A. (2011). Identifying factors that increase the likelihood of driving after drinking among college students. Accident Analysis & Prevention, 43, 1371–1377. doi:10.1016/j.aap.2011.02.011

Laforge, R. G., Borsari, B., U., & Baer, J. S. (2005). The utility of collateral informant assessment in college alcohol research: Results from a longitudinal prevention trial. Journal of Studies on Alcohol, 66, 479–487.

McCauley, J. L., Calhoun, K. S., & Gidycz, C. A. (2010). Binge drinking and rape: A prospective examination of college women with a history of previous sexual victimization. Journal of Interpersonal Violence, 25, 1655–1668. doi:10.1177/0886260509354580

Mundt, M. P., & Zakletsaia, L. I. (2012). Prevention for college students who suffer alcohol-induced blackouts could deter high-cost emergency department visits. Health Affairs, 31, 863–870. doi:10.1377/hlthaff.2010.1140

National Panhellenic Conference. (2015). NPC policies and best practices. Retrieved from https://www.npcwomen.org/resources/pdf/College%20Panhellenic%20Policies%20and%20Best%20Practices.pdf

Nelson, T. R, & Wechsler, H. (2001). Alcohol and college athletes. Medicine & Science in Sports & Exercise, 33, 43–47.

Nguyen, N., Walters, S. T., Rinker, D. V., Wyatt, T. M., & DeJong, W. (2011). Fake ID ownership in a US sample of incoming first-year college students. Addictive Behaviors, 36, 759–761. doi:10.1016/j.addbeh.2011.01.035

Page, R. M., & O’Hegarty, M. (2006). Type of student residence as a factor in college students’ alcohol consumption and social normative perceptions regarding alcohol use. Journal of Child & Adolescent Substance Abuse15, 15–31. doi:10.1300/J029v15n03_02

Park, A., Sher, K. J., & Krull, J. L. (2008). Risky drinking in college changes as fraternity/sorority affiliation changes: A person-environment perspective. Psychology of Addictive Behaviors22, 219–229. doi:10.1037/0893-164X.22.2.219

Paschall, M. J., & Saltz, R. F. (2007). Relationships between college settings and student alcohol use before, during and after events: A multi-level study. Drug and Alcohol Review, 26, 635–644. doi:10.1080/09595230701613601

Patrick, M. E., Maggs, J. L., & Osgood, D. W. (2010). LateNight [sic] Penn State alcohol-free programming: Students drink less on days they participate. Prevention Science, 11, 155–162. doi:10.1007/s11121-009-0160-y

Ragsdale, K., Porter, J. R., Mathews, R., White, A., Gore-Felton, C., & McGarvey, E. L. (2012). ‘Liquor before beer, you’re in the clear’: Binge drinking and other risk behaviours among fraternity/sorority members and their non-Greek peers. Journal of Substance Use17, 323–339. doi:10.3109/14659891.2011.583312

Robison, A. (2007). Case study analysis of a college fraternity utilizing alcohol-free housing. Dissertation Abstracts International: Section A. Humanities and Social Sciences, 68(10), 4227.

Soule, E. K., Barnett, T. E., & Moorhouse, M. D. (2015). Protective behavioral strategies and negative alcohol-related consequences among US college fraternity and sorority members. Journal of Substance Use, 20, 16–21. doi:10.3109/14659891.2013.858783

Wechsler, H., Lee, J. E., Gledhill-Hoyt, J., & Nelson, T. F. (2001). Alcohol use and problems at colleges banning alcohol: Results of a national survey. Journal of Studies on Alcohol, 62, 133–141.

Wechsler, H., Lee, J. E., Nelson, T. F., & Lee, H. (2001). Drinking levels, alcohol problems and secondhand effects in substance-free college residences: Results of a national study. Journal of Studies on Alcohol, 62, 23–31.

Wei, J., Barnett, N. P., & Clark, M. (2010). Attendance at alcohol-free and alcohol-service parties and alcohol consumption among college students. Addictive Behaviors, 35, 572–579. doi:10.1016/j.addbeh.2010.01.008

Whipple, E. G. (2005). Brotherhood: Our substance of choice. Retrieved from http://www.phideltatheta.org/resources/10_year_anniversary_whitepaper.pdf 

Young, A. M., Morales, M., McCabe, S. E., Boyd, C. J., & D’Arcy, H. (2005). Drinking like a guy: Frequent binge drinking among undergraduate women. Substance Use & Misuse, 40, 241–267. doi:10.1081/JA-200048464

 

Kathleen Brown-Rice, NCC, is an Assistant Professor at the University of South Dakota. Susan Furr is a Professor at the University of North Carolina-Charlotte. Correspondence may be addressed to Kathleen Brown-Rice, 414 E. Clark Street, Vermillion, SD 57069, kathleen.rice@usd.edu.

 

Examining Intimate Partner Violence, Stress and Technology Use Among Young Adults

Ryan G. Carlson, Jessica Fripp, Christopher Cook, Viki Kelchner

Intimate partner violence is a problem among young adults and may be exacerbated through the use of technology. Scant research exists examining the influence of technology on intimate partner violence in young adults. Furthermore, young adult couples on university campuses experience additional stressors associated with coursework that may influence their risk of partner violence. We surveyed 138 young adults (ages 1825) at a large university and examined the relationships between stress, intimate partner violence and technology. Results indicated that those who use technology less frequently are more likely to report inequality in the relationship, thus suggesting a higher risk for partner violence. An exception applies to those who use technology to argue or monitor partner whereabouts. Implications for counseling young adult couples are discussed.

Keywords: intimate partner violence, stress, young adults, technology, couples

Intimate partner violence (IPV) occurs among young adults (ages 1824) at a comparable rate with the general population. IPV in the general population occurs among 25%33% of both men and women (National Intimate Partner and Sexual Violence Survey, 2010), with studies estimating the prevalence of physical violence among college students to be between 20% and 30% (Fass, Benson, & Leggett, 2008; Shook, Gerrity, Jurich, & Segrist, 2000; Spencer & Bryant, 2000). Additionally, IPV is regularly underreported due to the embarrassment and shame victims may feel (Bureau of Justice Statistics, 2003). While causes of IPV are not completely understood, its prevalence among both victims and victimizers has been linked to those who witnessed parental violence as children (Straus, Gelles, & Smith, 1995). However, the increase in college student IPV could be provoked by stress associated with the demands of academics (Mason & Smithey, 2012). IPV victims are more likely to experience symptoms of depression and anxiety, with male victims expressing more shame related to the victimization (Shorey et al., 2011).

 

In the late 1980s and 1990s, researchers identified types of partner violence within adult relationships (e.g., Gottman et al., 1995; Holtzworth-Munroe & Stuart, 1994; Johnson, 1995). Researchers coined these differences as IPV typologies, which helped researchers and practitioners understand that partner violence is heterogeneous, and thus treatment should be tailored to meet the specific needs of the couple (Carlson & Jones, 2010). This perspective differed from the traditional practice of treating all relationship violence as homogeneous, presuming it to be the result of power and control. Additionally, traditional perspectives on IPV assumed that perpetrators were men trying to assert dominance. Typology researchers refuted this perspective, stating that although some violence is male-on-female, the majority is gender mutual and may have more to do with conflict resolution skills than with asserting control. IPV typology research has gained traction due to its potential treatment implications. However, there is a dearth of research examining IPV typologies among young adults and its relationship to the increased use of technology among this population.

 

IPV Typologies

 

Traditionally, relationship violence was more popularly termed domestic violence and deemed homogenous among couple relationships. Thus, all violence was thought to originate from a batterer’s attempt to establish or maintain power and control over a victim. Such violence typically occurred with men as the batterers and women as the victims (in heterosexual relationships). This philosophy gained traction with most practitioners, who assumed that all relationship violence resulted from power and control.

 

Over the past 15 to 20 years, researchers identified types of relationship violence (e.g., Gottman et al., 1995; Holtzworth-Munroe & Stuart, 1994; Johnson, 1995; & Johnson & Ferraro, 2000). Researchers utilized studies indicating that violence is likely to vary in severity, and often the motive is not to establish power and control over one’s partner. As such, relationship violence was deemed heterogeneous among couples. Therefore, researchers began using the term intimate partner violence as a broader term for describing the variances in violence that occur within relationships, as well as the notion that the violence can be gender mutual in some typologies, meaning that violence is just as likely to be female-on-male as male-on-female in heterosexual relationships. Examples of some of Johnson’s (1995) IPV typologies include the following: (a) situational couple violence, marked by violence that is gender mutual and has lower levels of severity; (b) intimate terrorist, marked by violence that is typically male-on-female, the result of one partner establishing power and control over another, and includes higher levels of lethality (e.g., choking); and (c) violent resistance, when the victim attempts to fight back. Other researchers have established typologies (e.g., Gottman et al., 1995; Holtzworth-Munroe & Stuart, 1994); however, Johnson’s appear to be the most recognized.

 

Carlson and Jones (2010) developed the continuum of conflict and control to synthesize violence typology research. They asserted that violence typologies could be conceptualized through variances in the type and severity of violence, characteristics of the victimizer, and perceptions of the victim. Assessing information across those three domains can help determine the nature and severity of the violence, and have potential treatment implications. For example, some researchers have examined the effectiveness of relationship interventions when couples present with lower levels of severity in relationship violence (e.g., Bradley, Friend, & Gottman, 2011; Braithwaite & Fincham, 2014; Simpson, Atkins, Gattis, & Christensen, 2008). However, such interventions require counselors to make informed and intentional treatment decisions that consider the safety of the couple.

 

Counselors may not typically screen for partner violence or make treatment decisions based on the safety of a victim (Schacht, Dimidjian, George, & Berns, 2009). Partner violence screening protocols are beyond the scope of this paper; however, readers are referred to Daire, Carlson, Barden, and Jacobson (2014). Counselors who become aware of partner violence typically refer their clients, with the assumption that treatment is contraindicated. However, couples counseling and other relationship interventions, such as relationship education, appear to reduce overall levels of relationship violence and increase relationship satisfaction (Bradley et al., 2011; Simpson et al., 2008). Couples who participated in this research were identified as having low levels of aggression, and as not attempting to establish power and control over their respective partners. Our review of the literature did not yield any research discussing how IPV typologies translate to young adult relationships, and what effect technology might have on the types of violence. Thus, it is not clear what evidence exists supporting best practice guidelines for counselors who work with young adults experiencing IPV in their relationships.

 

Dating Violence

 

The Centers for Disease Control and Prevention (CDC) has defined dating violence as the consistent act of physical and/or sexual violence, as well as the possible emotional or psychological distress perpetrated by a current or previous dating partner (CDC, 2014). Additionally, the CDC has reported that dating violence contributes to health risks including, but not limited to, injury, heavy drinking, suicidal ideation, promiscuity, substance use, issues with self-esteem and perpetuating the act of violence in future relationships. When violence is enacted toward adolescents, healthy development of intimacy, identity and sexuality is hindered (Foshee & Reyes, 2009).

 

Draucker, Martsolf, and Stephenson (2012) studied the history of dating violence among the adolescent population and found that the risk factors correlating with later dating violence include parenting issues, such as inconsistent parental supervision, discipline and warmth. In addition to identifying factors that contribute to violence (e.g., exposure to violence at a young age, experiencing varying styles of parenting), Stephenson, Martsolf, and Draucker (2012) recognized the role of peers in exacerbating dating violence in young adulthood. Adelman and Kil (2007) purported that peers are directly and indirectly involved in adolescent dating violence, including assisting in the confrontation of a friend’s partner or helping a friend make his or her partner jealous. According to Banister and Jakubec (2004), females often feel isolated by their peers in adolescent dating violence, as many of their friends may not approve of the relationship. Thus, it is possible they may not disclose the nature of the violence within the relationship.

 

Technology and Conflict Resolution

Cyber aggression has been more thoroughly researched in child and adolescent populations than in young adult populations. Among children and adolescents, technology offers young people an additional medium for aggression, but does not appear to contribute directly to the development of cyber aggression among those who are not aggressive in non-cyber roles (Burton, Florell, & Wygant, 2013; Dempsey, Sulkowski, Dempsey, & Storch, 2011; Werner, Bumpus, & Rock, 2010). Werner et al. (2010) demonstrated that among sixth, seventh and eighth graders, higher rates of relational aggression approval predicted higher rates of Internet aggression. Peer attachment, however, is negatively correlated with both cyber aggression and non-cyber aggression (Burton et al., 2013). In addition to correlations between user beliefs and use of technology, Draucker and Martsolf (2010) found that many individuals who experienced dating violence as adolescents described technology as a medium for violence. Among 56 emerging adults who were interviewed about their adolescent dating violence experiences, participants reported technology use for arguing (6), perpetrating verbal or emotional aggression (30), monitoring or controlling (30), and limiting a partner’s access to self (e.g., avoiding partner; 29). It is unclear whether these same patterns hold true for young adults’ dating experiences, as the members of this sample were asked to reflect on their experiences as adolescents.

 

In addition to studies focused on children and adolescents, research demonstrates a link between individual beliefs about aggression and the use of technology in a way that is consistent with those beliefs among emerging adults. Thompson and Morrison (2013) studied the relationships between several individual-, social- and community-level predictors of technology-based sexually coercive behavior (TBC) among college students. Thompson and Morrison’s (2013) findings suggest that rape-supportive beliefs and peer approval of forced sex were significant predictors of TBC. However, women who are more assertive in the relationship appear to mitigate cyber aggression (Schnurr, Mahatmya, & Basche, 2013).

 

Technology use has been identified as a key component in conflict resolution strategies and romantic relationship mediation among young adults as well. Weisskirch and Delevi (2013) found that college students who had positive feelings about conflict resolution were more likely to use technology, specifically text messaging, to terminate relationships. Text messaging was the most commonly cited use of technology for the purpose of initiating or receiving a relationship-ending message. In a study of 1,039 adults aged 17 and older, Coyne, Stockdale, Busby, Iverson, and Grant (2011) found that younger participants were more likely to use technology in communicating with their romantic partner, and that technology was used to communicate in a variety of ways within the romantic relationship, including the expression of affection (75%), discussion of serious issues (25%), apologizing (12%) and hurting their partner (3%). Given the extent to which young adults use technology as a medium for relationship communication, and the prevalence of dating violence, more research is needed to understand how technology use may be correlated with risks of partner violence.

 

Research Questions

 

     Despite researchers’ attempts to understand IPV among college-aged students, as well as to identify primary prevention interventions, IPV typologies have not been determined among the college student population. Further, the emergence of social media has provided a new mechanism for IPV implementation. Schnurr et al. (2013) found that cyber aggression mitigates physical IPV for men. However, few studies have examined the prevalence of cyber aggression in college students or considered the role of cyber aggression within the IPV typology framework. Thus, the current study aims to explore college students’ perceptions of how technology is used in their relationships, as well as the influence of technology, stress and attitudes toward violence on overall risk for IPV. As such, we examined the following research questions: (a) What relationship exists between young adults’ perceptions of partners’ technology use in relationships, risk for partner violence, acceptance of couple violence and perceived stress?; (b) Can perceptions of partners’ technology use, acceptance of couple violence or perceived stress be considered predictors of risk for partner violence? If so, which exerts the most influence on risk for partner violence?; and (c) What differences exist between individual responses (i.e., yes/no) regarding perceptions of partners’ use of technology in relationships and outcomes (i.e., risk for violence, perceived stress, acceptance of violence)?

 

Method

 

Participants

Data collection occurred at a large university in the Southeast region of the United States. We invited undergraduate and graduate students aged 1825 who were currently in a relationship or had recently been in a relationship to participate. We utilized a convenience sampling approach and recruited participants through both active and passive methods (Yancey, Ortega, & Kumanyika, 2006). Active methods included acquiring instructor permission and speaking briefly to students during class about the study. Passive methods comprised posting study flyers around campus, as well as contacting various departments and programs requesting that they send study information to students on their e-mail listserv. All eligible students were invited to complete the assessment packet online using Survey Monkey. Students began the survey by reading the study information form, which included a warning about the sensitive nature of the questions. At the conclusion of the survey, we provided all participants with a list of domestic violence resources.

 

Recruitment efforts resulted in 155 students attempting to complete the survey. However, we removed 17 participants, 11 of whom indicated an age of 26 or older (making them ineligible) and six of whom did not complete any of the survey questions. We did not offer any incentives for survey completion as participation was voluntary, but it is possible that instructors provided incentives of their own accord. Instructor-initiated incentives could explain the six participants who did not answer any questions. Therefore, the total sample for the study was 138 participants.

Eighty-six participants (62%) indicated currently being in a relationship, with relationships lasting an average of 30 months. Others were recently in a relationship (n = 49; three participants did not indicate relationship status), reporting an average of 20 months since their last relationship. Women (n = 119; 87%) comprised the majority of the sample. The sample included mostly heterosexual participants (n = 127), with some same-sex participants (n = 10; one person did not report). Participants ranged in grade level; most were graduate students (n = 48; 35%), followed by seniors (n = 42; 30%), juniors (n = 28; 20%), sophomores (n = 17; 12%) and freshmen (n = 3; 2%). See Table 1 for additional demographic information and descriptive statistics for constructs of interest.

 

 

Table 1

 

Descriptive Statistics for Study Constructs

Constructs

                  M    

                SD 

            Range

Age

21.45

1.53

18–25

Credit hours

14.67

3.04

3–23

Perceived stress (PSS)

6.31

2.77

1–13

Intimate justice (IJS)

26.97

10.96

15–64

Acceptance of violence (ACV)

5.61

1.22

5–12

Use of technology (UTR)

8.96

1.15

5–10

Note. M = mean; SD = standard deviation; PSS = Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983; Cohen & Williamson, 1988); IJS = Intimate Justice Scale (Jory, 2004); ACV = Acceptance of Couple Violence (Foshee, Fothergill, & Stuart, 1992); UTR = Use of Technology in Relationships (Draucker & Martsolf, 2010; Schnurr et al., 2013).

 

Instruments

     Demographic information. The demographic information form consisted of 13 questions and asked participants about basic information such as age, gender, grade, current relationship status, length of relationship (if current) and length of previous relationship (as well as length of time since previous relationship). Participants completed the demographic information form prior to completing the other study assessments.

 

   Perceived Stress Scale. The Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983; Cohen & Williamson, 1988) is a 10-item measure assessing the perception of stress. We incorporated the PSS to examine the relationship of respondents’ perceived stress to relationship violence (or risk of violent behaviors). Respondents indicate on a five-point Likert scale (0 = Never, 1 = Almost Never, 2 = Sometimes, 3 = Fairly Often and 4 = Very Often) the extent to which situations in life are deemed stressful. The PSS asks general questions, such as “In the last month, how often have you been upset because of something that happened unexpectedly?” The PSS is scored by summing the item responses. The factor structure of the PSS has been supported in a sample of community participants as well as college students (Cohen et al., 1983; Roberti, Harrington, & Storch, 2006). There are several versions of the PSS (each consisting of 14, 10 or four items). The short four-item scale comprises items 2, 4, 5 and 10 of the PSS and has shown support in use with data collected during telephone interviews. We utilized the short form in the current study to reduce the overall number of questions asked of each participant. Cohen et al. (1983) reported an alpha coefficient in their study of .84 for the PSS with 14 items. They examined the test-retest reliability utilizing 65 college students and identified an alpha of .85. The PSS 10-item instrument has demonstrated sound reliability in a sample of college students as well (Dehle, Larsen, & Landers, 2001). Cronbach’s alpha was low (.58) for participants in the current study. However, the PSS short form demonstrated better reliability (.72) in the study conducted by Cohen et al. (1983).

 

     Acceptance of Couple Violence. We incorporated the Acceptance of Couple Violence (ACV; Foshee, Fothergill, & Stuart, 1992) questionnaire to assess for attitudes toward violence in couple relationships. Participants received an adapted version of the ACV to include same-sex relationships. The adapted ACV contains 17 items and comprises five subscales (acceptance of male-on-female violence, acceptance of female-on-male violence, acceptance of male-on-male violence, acceptance of female-on-female violence and acceptance of general dating violence). Scores are summed across responses to calculate a total score within each subscale. We used only acceptance of general dating violence for the current analyses. Cronbach’s alpha reliability for participant scores in the current study was .67.

 

     Use of Technology in Relationships. We used questions adapted by Schnurr et al. (2013) from Draucker and Martsolf (2010) to examine how participants perceived their partners’ use of technology in their relationships (UTR). As such, participants were asked whether their partners used technology in the following ways: (a) to embarrass them, (b) to make them feel bad, (c) to control them, (d) to monitor them and (e) to argue with them. Participants responded by indicating either “yes” (1) or “no” (0) and the responses were summed to acquire a total score. Reliability was low (α = .54) in the current study. However, Schnurr et al. (2013) reported internal consistencies of .76 for men and .71 for women in their sample of dating, emerging adult couples.

 

     Intimate Justice Scale. The Intimate Justice Scale (IJS; Jory, 2004) is a 15-item instrument designed for use in clinical practice to screen for psychological abuse and physical violence. The purpose of the instrument is to aid clinicians in identifying violations of intimate justices (e.g., equity, fairness) that are believed to contribute to relationship violence so that appropriate treatment decisions can be rendered. Participants respond to items on a Likert scale of 1–5, with 1 indicating “I do not agree at all” and 5 indicating “I strongly agree.” Scores are summed across responses, with a minimum possible score of 15 and a maximum possible score of 75. Higher scores indicate violations of intimate justice and a likelihood of relationship abuse. Jory (2004) provided the following guidelines when interpreting total IJS scores: “Scores 15 to 29 may suggest little risk of violence, scores between 30 and 45 may indicate a likelihood of minor violence, and scores > 45 may be a predictor of severe violence” (p. 39). To our knowledge, no assessment currently exists to classify specific IPV typologies. Other popular assessments of IPV exist, such as the Revised Conflict Tactics Scale (CTS; Straus, Hamby, Boney-McCoy, & Sugarman, 1996), but the CTS results do not classify types of IPV behavior with considerations for the victim or the victimizer. The IJS has potential to distinguish between degrees of violence severity, and has been used in studies to differentiate between lower levels and higher levels of violence aggression (e.g., Friend, Bradley, Thatcher, & Gottman, 2011). Scores in the current study ranged from 15–64 (M = 27.02). Alpha reliabilities for participants in the current study were .92.

 

Results

 

Preliminary Analysis

Prior to data analyses, we conducted preliminary analyses to test for assumptions, outliers and missing data. The ACV, IJS, and UTR did not meet the assumption of normality, with K-S p values falling below .001. The ACV and IJS resulted in a positive skew, while the UTR resulted in a negative skew. The distributions indicated that most respondents did not report favorable attitudes toward violence, the overall existence of relationship inequality (risk for IPV) or perceptions of partners using technology in an unhealthy manner. This finding is consistent with the mean IJS score (27.02), indicating minimal risk of violence in the sample. Thus, we did not implement any transformation procedures. Potential outliers existed for the ACV and IJS scores. However, examination of the 5% trimmed mean indicated minimal influence on the mean score. Furthermore, these scores represented participants reporting different attitudes and experiences with IPV.

 

Sixteen participants had missing data points. We created a dummy variable to compare some demographics for those who had complete data versus those who did not. No differences existed between those with and without missing data on age and credit hours taken during the semester of survey administration. We determined that the data were likely missing at random, although it is possible data were missing due to some variable not measured. We used hot deck imputation to address the missing variables (Andridge & Little, 2010; Myers, 2011). Hot deck imputation calculates an average score on an identified outcome variable by matching the score to like variables in the sample (i.e., donor variables). We used participants’ gender, grade level and current relationship status as the donor variables. SPSS averaged the score for matching participants and imputed. Matches existed for 13 of the 16 missing scores. Hot deck imputation provides less bias than mean imputation, and is deemed a better overall solution than the oft-used listwise deletion (Andridge & Little, 2010; Myers, 2011).

 

Primary Analysis

To begin testing the research questions, we conducted Pearson correlations to examine the relationships between demographics and other constructs of interest (i.e., PSS, IJS, ACV and UTR). Pearson correlation indicated (a) a significant positive correlation between gender and IJS scores, (b) a significant negative correlation between gender and UTR scores, (c) a significant positive correlation between PSS scores and IJS scores, (d) a significant positive correlation between the ACV and IJS scores and (e) a significant negative correlation between UTR scores and IJS scores (See Table 2 for correlations). A scatterplot matrix indicated that (a) increases in stress correlate to increases in intimate justice scores, (b) more favorable attitudes toward couple violence correlate to increases in intimate justice scores; and (c) lower perceived use of technology (i.e., more responses of “no”) correlates with higher intimate justice scores.

 

Table 2

 

Correlations Between Constructs of Interest

1 2 3 4 5
1. Gender 1 .02 .22* .13 -.17*
2. Perceived stress (PSS) 1 .19* .05 -.04
3. Intimate justice (IJS) 1 .26** -.05**
4. Acceptance of violence (ACV) 1 -.05
5. Use of technology (UTR) 1
Note. PSS = Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983; Cohen & Williamson, 1988); IJS = Intimate Justice Scale (Jory, 2004); ACV = Acceptance of Couple Violence (Foshee, Fothergill, & Stuart, 1992); UTR = Use of Technology in Relationships (Draucker & Martsolf, 2010; Schnurr et al., 2013).

* p < .05. ** p < .001.

 

The significant correlations supported a hierarchical linear regression analysis to examine the predictive relationships between variables. The IJS served as the dependent variable, while PSS, ACV and UTR scores served as independent variables. The model included three steps, adding predictor variables one step at a time to examine the contribution of each variable. Model one included ACV scores, contributing 6.8% of the variance and demonstrating statistical significance; F(1, 133) = 9.70, p = .002. Model two included UTR scores, adding 18.9% of the variance and achieving significance; F(1, 132) = 33.65, p < .001. Finally, model three added PSS, contributing 2.5% of variance and also achieving significance; F(1, 131) = 4.54, p = .035 (See Table 3). The model as a whole contributed to 26.6% of the variance, although UTR contributed the most variance to IJS scores.

 

Table 3

 

Predictors of Partner Violence Risk (Intimate Justice)

Variable

            Δ R2

            β

               p

Model 1: ACV

.068

.261

.002

Model 2: UTR

.189

-.435

< .001

Model 3: PSS

.025

.158

.035

Note. ACV = Acceptance of Couple Violence (Foshee, Fothergill, & Stuart, 1992); UTR = Use of Technology in Relationships (Draucker & Martsolf, 2010; Schnurr et al., 2013); PSS = Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983; Cohen & Williamson, 1988).

 

 

 

Next, we examined differences between individuals’ responses (i.e., yes/no) regarding perceptions of their partners’ use of technology in the relationships (UTR) and outcome variables (i.e., IJS, ACV and PSS scores). Table 4 presents the frequency of responses for each of the five items on the UTR. A MANOVA indicated that the only significant differences between responses on all five UTR questions and outcomes existed for question four (“Has your partner ever used technology to monitor you?”), F(1, 112) = 4.08, p = .04,  = .04, and question five (“Has your partner ever used technology to argue with you?”), F(1, 112) = 5.12, p = .03,  = .04. Simple effects revealed that respondents who indicated “yes” to UTR question four had significantly higher IJS scores (M = 33.38, SD = 11.09) than those who indicated “no” (M = 24.71, SD = 9.81); F(1, 129) = 19.81, p < .001,  = .13. Participants who indicated “yes” to UTR question five had significantly higher IJS scores (M = 30.79, SD = 11.13) than those who indicated “no” (M = 24.14, SD = 9.78); F(1, 129) = 13.24, p < .001,  = .09. Therefore, use of technology to argue with a partner and monitor a partner’s location appear associated with increases in relationship inequality, and place the young couples in our sample at a higher risk of experiencing partner violence.

 

Table 4

 

Frequency of Responses to Questions Regarding Use of Technology

Question (Has partner used technology to . . .)

% “Yes”

% “No”

1. Embarrass you?

 6.5

89.1

2. Make you feel bad?

15.2

15.9

3. Control you?

 5.1

94.7

4. Monitor you?

28.3

67.4

5. Argue with you?

44.9

50.7

 

Discussion

 

The purpose of this study was to understand the influence of young adults’ use of technology in intimate relationships and examine relationships among stress, attitudes toward violence and overall risk for IPV. First, we examined the relationships among the variables, then we used a regression analysis to understand the contribution of each variable to risk for partner violence. Finally, we explored differences between responses regarding partners’ perceptions of technology use and other outcomes.

 

Results indicate positive correlations between participants’ stress scores and intimate justice scores, suggesting that as stress increases, so too does risk for partner violence. This finding is similar to the conclusions of Mason and Smithey (2012), who utilized Merton’s Classical Strain Theory as the foundation for testing the influence of life strain on IPV among college students. Their results indicated that some forms of strain increased dating violence among college students. However, the results of our study do not suggest the existence of any relationship between technology use and stress. A potential explanation is that increases in IPV-related behaviors associated with increases in stress may present during face-to-face interactions.

We also found that participants who reported perceptions that partners used technology (e.g., to monitor, argue, embarrass, control, make them feel bad) less frequently were associated with increased intimate justice scores, or risk for partner violence. Although initially suprising, this result appears somewhat consistent with the findings of Coyne et al. (2011) indicating that younger participants are more likely to use technology to communicate in a variety of ways. In fact, it could be that communication via technology is an expectation in young adult relationships, and when that expectation is not met, tension arises. However, further research is needed to explore this conclusion.

 

Perceived stress (PSS: 2.4% of variance), acceptance of violence (ACV: 6.8% of variance) and use of technology (UTR: 18.9% of variance) were all significant predictors of risk for partner violence (IJS), with UTR contributing the most variance in IJS. This finding is consistent with the correlation and appears to support the notion that a lack of communication via technology may contribute to problems in young adult relationships. In fact, 45% of our sample indicated that their current or past partner used technology to argue with them. Again, this finding could support the notion that conflict resolution via technology is normal or expected in young adult relationships. However, results indicate that participants who perceived their partners as using technology as a means of arguing and monitoring them had higher risk for partner violence (i.e., IJS). The IPV typology literature has identified various characteristics associated with types of violence in couple relationships. A more controlling type, such as Johnson’s (1995) intimate terrorist, may exhibit nonviolent control tactics such as monitoring his or her partner’s location. Thus, it is possible that this behavior is more indicative of controlling IPV typologies. However, more research is needed to understand the influence of using technology to monitor a partner on overall risk for IPV.

 

Implications for Practice

 

According to Bergdall et al. (2012), emerging adults frequently use technology to establish relationships with others. Conversely, technology use has been a common medium for sustaining and terminating romantic or intimate relationships. Young adults between the ages of 18 and 29 typically use social media, cell phones and the Internet to communicate (Coyne et al., 2011). Although Bergdall et al. (2012) confirmed that young adults rely heavily on technology to form and dissolve relationships, the authors did not factor in the effect technology may have on psychosocial development, sexual behavior or dating violence.

 

The findings from our study, as well as from others, indicate that technology is frequently used in young adult relationships. Therefore, when screening for IPV, counselors should consider questions related to how partners use technology in their relationship (e.g., for communicating, announcing the relationship, resolving conflict). Daire et al. (2014) described an IPV protocol for community agencies and practitioners that includes screening clients. Such a protocol also should include technology and consider its overall influence on the functioning of the couple.

 

Continued research in this area may reveal the ways in which young adults communicate with each other via technology. Individuals who have grown up amidst advances in technology have adapted to a lifestyle in which the ability to communicate with friends and gain entry into one’s personal life is readily available. Due to this factor, the ability to communicate with, gain access to or monitor a partner has increased. Draucker and Martsolf (2010) indicated that technology has changed the course of relationship quality and communication because boundaries have shifted. Counselors can incorporate healthy technology communication into their treatment plans. Bergdall et al. (2012) reported that technology does close the social gap between all people, but if utilized in efforts to educate young adults about healthy and safe ways to communicate with each other, it may have a positive effect on intimate relationships and the potential to reduce violence.


Limitations

 

This study’s findings should be considered with caution because there are limitations to consider. We did not incorporate a random sampling method, as there were no large student lists or databases for generating random samples. We were unable to calculate a response rate due to the nature of our convenience sampling approach. Thus, the study results might not be representative of the young adult population at all colleges and universities. Additionally, the majority of the sample was comprised of white, heterosexual females.

 

Another limitation is that two of the assessments we used revealed low Cronbach’s alpha scores (PSS and UTR), while the ACV had a Cronbach’s alpha just below the accepted cutoff. Cronbach’s alpha is not a measure of the overall assessment’s internal consistency as much as it is a measure of the sample’s consistent responses to items (Helms, Henze, Sass, & Mifsud, 2006; Lance, Butts, & Michels, 2006). Thus, the low Cronbach’s alpha suggests diversity in responses to items among the study sample. However, the low Cronbach’s alpha scores may indicate higher measurement error, and results should be considered with caution.

This study also is limited because it incorporated self-report measures, with some participants reflecting on past relationships. Self-report, especially when thinking about a relationship that did not work out, may not provide accurate information. Additionally, we did not collect data from both members of a couple. Finally, there were missing data because participants skipped items, marked two items instead of one or skipped enough items that their results were not interpretable. We used a data imputation method with reduced bias, but there is no certainty in the accuracy of the imputed responses.

 

Conclusion

 

Recent research has contributed to the formation of IPV typologies and has challenged traditional models, yet much remains unknown about partner violence among young adults. The use of technology in relationship communication and conflict resolution is an expanding area of research due to technology’s increased use in daily living. Given the need for more information about both IPV and the use of technology in relationship communication, this study looked at technology use as a risk factor for IPV among young adults. Our study both confirmed prior results and contributed new results. Results suggest that emerging adults may expect technology to be an important means of relationship communication. Those counseling college-aged couples should consider discussing healthy avenues for incorporating technology. Furthermore, technology use should be considered when counselors screen couples for risk factors associated with IPV. However, more research is warranted regarding the use of technology in young adult relationships.

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

References

 

Adelman, M., & Kil, S. H. (2007). Dating conflicts: Rethinking dating violence and youth conflict. Violence Against Women, 13, 1296–1318. doi:10.1177/1077801207310800

Andridge, R. R., & Little, R. J. A. (2010). A review of hot deck imputation for survey non-response. International Statistical Review, 78, 40–64. doi:10.1111/j.1751-5823.2010.00103.x

Banister, E., & Jakubec, S. (2004). “I’m stuck as far as relationships go”: Dilemmas of voice in girls’ dating relationships. Child & Youth Services, 26, 33–52. doi:10.1300/J024v26n02_03

Bergdall, A. R., Kraft, J. M., Andes, K., Carter, M., Hatfield-Timajchy, K., & Hock-Long, L. (2012). Love and hooking up in the new millennium: Communication technology and relationships among urban African American and Puerto Rican young adults. Journal of Sex Research, 49, 570–582. doi:10.1080/00224499.2011.604748

Bradley, R. P. C., Friend, D. J., & Gottman, J. M. (2011). Supporting healthy relationships in low-income, violent couples: Reducing conflict and strengthening relationship skills and satisfaction. Journal of Couple & Relationship Therapy, 10, 97–116. doi:10.1080/15332691.2011.562808

Braithwaite, S. R., & Fincham, F. D. (2014). Computer-based prevention of intimate partner violence in marriage. Behaviour Research and Therapy, 54, 12–21. doi:10.1016/j.brat.2013.12.006

Bureau of Justice Statistics. (2003). Family violence statistics. Retrieved from http://www.bjs.gov/content/pub/pdf/fvs.pdf

Burton, K. A., Florell, D., & Wygant, D. B. (2013). The role of peer attachment and normative beliefs about aggression on traditional bullying and cyberbullying. Psychology in the Schools, 50, 103–115. doi:10.1002/pits.21663

Carlson, R. G., & Jones, K. D. (2010). Continuum of conflict and control: A conceptualization of intimate partner violence typologies. The Family Journal, 18, 248–254. doi:10.1177/1066480710371795

Centers for Disease Control and Prevention. (2014). Understanding teen dating violence. National Center for Injury Prevention and Control, Division of Violence Prevention. Retrieved from http://www.cdc.gov/violenceprevention/pdf/teen-dating-violence-factsheet-a.pdf

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396.

Cohen, S., & Williamson, G. M. (1988). Perceived stress in a probability sample of the United States. S. Spacapan & S. Oskamp (Eds.), The Social Psychology of Health (pp. 31–67). Newbury Park, CA: Sage.

Coyne, S. M., Stockdale, L., Busby, D., Iverson, B., & Grant, D. M. (2011). “I luv u :)!”: A descriptive study of the media use of individuals in romantic relationships. Family Relations, 60, 150–162. doi:10.1111/j.1741-3729.2010.00639.x

Daire, A. P., Carlson, R. G., Barden, S. M, & Jacobson, L. (2014). An intimate partner violence (IPV) protocol readiness model. The Family Journal, 22, 170–178. doi:10.1177/1066480713513708

Dehle, C., Larsen, D., & Landers, J. E. (2001). Social support in marriage. American Journal of Family Therapy, 29, 307–324. doi:10.1080/01926180126500

Dempsey, A. G., Sulkowski, M. L., Dempsey, J., & Storch, E. A. (2011). Has cyber technology produced a new group of peer aggressors? Cyberpsychology, Behavior, and Social Networking, 14, 297–302. doi:10.1089/cyber.2010.0108

Draucker, C. B., & Martsolf, D. S. (2010). The role of electronic communication technology in adolescent dating violence. Journal of Child and Adolescent Psychiatric Nursing, 23, 133–142. doi:10.1111/j.1744-6171.2010.00235.x

Draucker, C. B., Martsolf, D., & Stephenson, P. M. (2012). Ambiguity and violence in adolescent dating relationships. Journal of Child and Adolescent Psychiatric Nursing, 25, 149–157. doi:10.1111/j.1744-6171.2012.00338.x

Fass, D. F., Benson, R. I., & Leggett, D. G. (2008). Assessing prevalence and awareness of violent behaviors in the intimate partner relationships of college students using internet sampling. Journal of College Student Psychotherapy, 22(4), 66–75. doi:10.1080/87568220801952248

Foshee, V. A., Fothergill, K., & Stuart, J. (1992). Results from the teenage dating abuse study conducted in Githens Middle School and Southern High School Unpublished technical report. Chapel Hill, NC: University of North Carolina.

Foshee, V. A., & Reyes, H. L. M. (2009). Primary prevention of adolescent dating abuse perpetration: When to begin, whom to target, and how to do it. In D. J. Whitaker & J. R. Lutzker (Eds.), Preventing partner violence: Research and evidence-based intervention strategies (pp. 141–168). Washington, DC: American Psychological Association.

Friend, D. J., Bradley, R. P. C., Thatcher, R., & Gottman, J. M. (2011). Typologies of intimate partner violence: Evaluation of a screening instrument for differentiation. Journal of Family Violence, 26, 551–563. doi:10.1007/s10896-011-9392-2

Gottman, J. M., Jacobson, N. S., Rushe, R. H., Shortt, J. W., Babcock, J., La Taillade, J. J., & Waltz, J. (1995). The relationship between heart rate reactivity, emotionally aggressive behavior, and general violence in batterers. Journal of Family Psychology, 9, 227–248. doi:10.1037/0893-3200.9.3.227

Helms, J. E., Henze, K. T., Sass, T. L., & Mifsud, V. A. (2006). Treating Cronbach’s alpha reliability coefficients as data in counseling research. The Counseling Psychologist, 34, 630–660. doi:10.1177/0011000006288308

Holtzworth-Munroe, A., & Stuart, G. L. (1994). Typologies of male batterers: Three subtypes and the differences among them. Psychological Bulletin, 116, 476–497. doi:10.1037/0033-2909.116.3.476

Johnson, M. P. (1995). Patriarchal terrorism and common couple violence: Two forms of violence against women. Journal of Marriage and Family, 57, 283–294.

Johnson, M. P., & Ferraro, K. J. (2000). Research on domestic violence in the 1990s: Making distinctions. Journal of Marriage and Family, 62, 948–963. doi:10.1111/j.1741-3737.2000.00948.x

Jory, B. (2004). The intimate justice scale: An instrument to screen for psychological abuse and physical violence in clinical practice. Journal of Marital and Family Therapy, 30, 29–44. doi:10.1111/j.1752-0606.2004.tb01220.x

Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria: What did they really say? Organizational Research Methods, 9, 202–220. doi:10.1177/1094428105284919

Mason, B., & Smithey, M. (2012). The effects of academic and interpersonal stress on dating violence among college students: A test of classical strain theory. Journal of Interpersonal Violence, 27, 974–986. doi:10.1177/0886260511423257

Myers, T. A. (2011). Goodbye, listwise deletion: Presenting hot deck imputation as an easy and effective tool for handling missing data. Communication Methods and Measures, 5, 297–310. doi:10.1080/19312458.2011.624490

Roberti, J. W., Harrington, L. N., & Storch, E. A. (2006). Further psychometric support for the 10-item version of the perceived stress scale. Journal of College Counseling, 9, 135–147. doi:10.1002/j.2161-1882.2006.tb00100.x

Schacht, R. L., Dimidjian, S., George, W. H., & Berns, S. B. (2009). Domestic violence assessment procedures among couple therapists. Journal of Marital and Family Therapy, 35, 47–59. doi:10.1111/j.1752-0606.2008.00095.x

Schnurr, M. P., Mahatmya, D., & Basche, R. A., III. (2013). The role of dominance, cyber aggression perpetration, and gender on emerging adults’ perpetration of intimate partner violence. Psychology of Violence, 3, 70–83. doi:10.1037/a0030601

Shook, N. J., Gerrity, D. A., Jurich, J., & Segrist, A. E. (2000) Courtship violence among college students: A comparison of verbally and physically abusive couples. Journal of Family Violence, 15, 1–22. doi:10.1023/A:1007532718917

Shorey, R. C., Sherman, A. E., Kivisto, A. J., Elkins, S. R., Rhatigan, D. L., & Moore, T. M. (2011). Gender differences in depression and anxiety among victims of intimate partner violence: The moderating effect of shame proneness. Journal of Interpersonal Violence, 26, 1834–1850. doi:10.1177/0886260510372949

Simpson, L. E., Atkins, D. C., Gattis, K. S., & Christensen, A. (2008). Low-level relationship aggression and couple therapy outcomes. Journal of Family Psychology, 22, 102–111. doi:10.1037/0893-3200.22.1.102

Spencer, G. A., & Bryant, S. A. (2000). University students’ dating violence behaviors. Journal of the New York State Nurses Association, 31(2), 15–20.

Stephenson, P. S., Martsolf, D., & Draucker, C. B. (2012). Peer involvement in adolescent dating violence. The Journal of School Nursing, 29, 204–211. doi:10.1177/1059840512469232

Straus, M. A., Gelles, R. J., & Smith, C. (Eds.). (1995). Physical violence in American families: Risk factors and adaptations to violence in 8,145 families. New Brunswick, NJ: Transaction.

Straus, M. A., Hamby, S. L., Boney-McCoy, S., & Sugarman, D. B. (1996). The revised conflict tactics scales (CTS2): Development and preliminary psychometric data. Journal of Family Issues, 17, 283–316. doi:10.1177/019251396017003001

Thompson, M. P., & Morrison, D. J. (2013). Prospective predictors of technology-based sexual coercion by college males. Psychology of Violence, 3, 233–246. doi:10.1037/a0030904

Weisskirch, R. S., & Delevi, R. (2013). Attachment style and conflict resolution skills predicting technology use in relationship dissolution. Computers in Human Behavior, 29, 2530–2534. doi:10.1016/j.chb.2013.06.027

Werner, N. E., Bumpus, M. F., & Rock, D. (2010). Involvement in internet aggression during early adolescence. Journal of Youth and Adolescence, 39, 607–619. doi:10.1007/s10964-009-9419-7

Yancey, A. K., Ortega, A. N., & Kumanyika, S. K. (2006). Effective recruitment and retention of minority research participants. Annual Review of Public Health, 27, 1–28. doi:10.1146/annurev.publhealth.27.021405.102113

 

Ryan G. Carlson, NCC, is an Assistant Professor at the University of South Carolina. Jessica Fripp is a doctoral candidate at the University of South Carolina. Christopher Cook is a doctoral candidate at the University of South Carolina. Viki Kelchner, NCC, is a doctoral candidate at the University of South Carolina. Correspondence may be addressed to Ryan G. Carlson, University of South Carolina, College of Education, Wardlaw 258, Columbia, SC 29208, rcarlson@sc.edu.

 

Learner-Centered Pedagogy: Considerations for Application in a Didactic Course

Randall M. Moate, Jane A. Cox

A learner-centered teaching approach is well known in higher education but has not been fully addressed within counselor education. Instructors who adopt this approach value a collaborative approach to teaching and learning, one that honors students’ wisdom and contributions. Teachers create a learning environment encouraging students to actively engage in and take ownership of their learning experiences, an environment inspiring students to think deeply about how they might apply what they are learning to their future practice. It may be particularly challenging for counselor educators to incorporate learner-centered teaching strategies into didactic courses that are traditionally heavy in content versus smaller experiential courses such as practica and internships. In this article, learner-centered teaching is described, and a case study demonstrates how a learner-centered approach may be applied to a traditionally didactic counseling course.

 

Keywords: pedagogy, teaching, learner-centered, counselor education, didactic

 

For the past decade, there has been a call in higher education for a shift from teacher-centered methods of instruction to learner-centered pedagogy (Brown, 2003; Crick & McCombs, 2006; Harris & Cullen, 2008). Educators who use a learner-centered model view learning as nonlinear, multidimensional and a phenomenon that occurs relationally within a social context (Cornelius-White, 2007). Their use of learner-centered pedagogy favors a democratic approach to teaching that shifts the instructor from the center of the learning environment to a more peripheral position. This shift is achieved by increasing students’ opportunities to actively participate in the classroom and engage in self-directed learning outside the classroom, as well as providing forums through which they can share learned information with peers (Wright, 2011). Educators who use learner-centered pedagogy favor differentiated modalities to facilitate learning, in contrast to instructors who use teacher-centered models of teaching that rely on lecture as the primary means of instruction.

 

While learner-centered literature is well known within the domain of higher education, as of yet it has not been thoroughly addressed within the scope of counselor education. Scholars and researchers in counselor education have focused on what content should be included in curricula (Granello, 2000) or specific teaching techniques used in class (May, 2004; Shepard & Brew, 2005; Stinchfield, 2006), rather than comprehensive approaches toward teaching that are helpful for engaging student learning. Yet several pedagogies are present in the counselor education literature such as contextual teaching (Granello, 2000), constructivist pedagogy (Nelson & Neufeldt, 1998), experiential teaching approaches (Grant, 2006), and transparent counseling pedagogy (Dollarhide, Smith, & Lemberger, 2007). These authors have described alternative and innovative methods for engaging student learners.

 

Teaching practices such as contextual teaching, constructivist pedagogy, experiential teaching approaches and transparent counseling pedagogy share commonalities with, and reflect certain ideals of, learner-centered pedagogy. We believe that learner-centered pedagogy could represent an overarching theoretical umbrella, under which previous teaching practices presented in the counseling literature could represent different forms of learner-centered instruction. In this way, learner-centered pedagogy may serve as a conceptual framework that educators can use to provide an impactful learning experience for counseling students.

 

We provide a brief description of the learning needs of counselor education students based on the demands they will face working as professional counselors, followed by an explanation of how learner-centered pedagogy may ultimately help professional counselors meet these demands. A case study is then presented to demonstrate how learner-centered pedagogy was applied in a couples counseling class.
Preparing Counselor Trainees for Professional Practice: Facilitating Deep Learning

Master’s degree programs in counselor education are designed to prepare students to begin working as professional counselors upon graduation. To learn to be professional counselors, students must develop a sense of comfort with ambiguity and a capacity for independent and reflective thinking (Dollarhide et al., 2007). Counseling students also must develop competent clinical skills and adequate knowledge to pass licensure examinations. Traditionally, courses thought to be didactic (e.g., theories, ethics, diagnosis, couples and family counseling) have tended to emphasize the acquisition of important content knowledge. In contrast, seminar courses (e.g., prepracticum, practicum, internship) are oriented to experiential learning and the development of clinical skills (Sperry, 2012). Counselor educators have designed curricula with the dual focus of acquiring important content knowledge and the development of clinical skills. Yet it is unclear what approaches to teaching are helpful for preparing counselor trainees for the demands of being a professional counselor, particularly approaches to teaching didactic courses.

One means of gaining insight into this question of helpful teaching approaches to didactic or seminar courses is to explore what counseling students and practicing counselors believe is important in their training. A comprehensive review of the literature revealed only a few articles that offer some evidence of what students and practicing counselors perceive as important learning experiences during their graduate degree programs, experiences that help to prepare them for professional counseling careers. Orlinsky, Botermans, Rønnestad, and the SPR Collaborative Research Network (2001) found that professional therapists recall practical and experiential learning as most helpful in facilitating their professional development. Similarly, Furr and Carroll (2003) found that experiential learning activities and activities that involve immediate application of knowledge have a greater impact on students’ development than cognitive teaching strategies. Grant (2006) supported these research findings; she posited that counselor education programs should expand beyond didactic-intensive approaches to teaching to incorporate more opportunities for experiential learning and activities that generate reflective thinking. Grant surmised that these approaches to teaching are helpful for preparing counselor trainees for the complexity of working with challenging client populations.

Experiential and applied learning are important facets of learner-centered pedagogy that can help instructors move away from didactic-intensive styles of teaching and enhance deeper approaches to learning in their students. Researchers have identified a deep approach as one of two approaches students take toward learning (Diseth, 2007; Parpala, Lindblom-Ylänne, Komulainen, Litmanen, & Hirsto, 2010). A deep approach toward learning is characterized by students’ intent to understand the richness and meaning of what they are studying (Diseth, 2007). The second is a surface approach, which prioritizes the reproduction of knowledge with precision rather than depth of understanding, as students’ motivation tends to be based on minimizing their chances of being wrong (Parpala et al., 2010). A surface approach to learning can be compared to the processes of a copying machine—students are presented with information, which they attempt to reproduce neatly and accurately, so that the copy mirrors the original as closely as possible. Students who adopt a deep approach toward a learning task are typically regarded as having intrinsic motivations for learning (Diseth, 2007). Such students are more likely to conceptualize, problem solve, and be reflective during a learning task as they wrestle to construct personal knowledge and understanding.

Students’ perception of their learning environment is a factor that influences the type of learning approach they use during the course. Some researchers have found a positive correlation between learner-centered classroom environments and students developing deep approaches to learning (Vanthournout, Donche, Gijbels, & Van Petegem, 2004; Wilson & Fowler, 2005). Students who have positive perceptions of a learning environment (e.g., see meaning and purpose in a course, perceive that what they are learning will be useful to them, are stimulated by classroom activities, perceive the classroom as a safe place) tend to adopt deep approaches toward learning. Students who hold a negative perception of a learning environment (e.g., do not see purpose or meaning in a course, are not intellectually stimulated, struggle to grasp what is being taught, feel unsafe or overwhelmed in the classroom) are more likely to adopt surface approaches toward learning (Lindblom-Ylänne, 2004).

Counselor educators are tasked with creating an engaging learning environment in didactic-oriented classes that invites students to learn thoughtfully and deeply as they prepare for professional counseling practice. Creating an environment that counseling students perceive as meaningful, useful and safe may encourage students to use deep approaches to learning. Counseling students who use a deep approach toward their learning may develop greater personal meaning and understanding about what they are learning, so they can more effectively apply what they have learned when working as professional counselors. Aspects of leaner-centered pedagogy may be useful to counselor educators in creating a learning environment that is perceived as positive by counseling students, whether in the context of a didactic or seminar course.

Teacher-Centered and Learner-Centered Pedagogies

A factor that can influence how counselor trainees perceive their learning environment is the teaching approach used by their instructor. Teacher-centered and learner-centered pedagogies are differing approaches to teaching that are based on contrasting ideological assumptions.

Teacher-Centered Pedagogy
Teacher-centered pedagogy is associated with traditional conceptions of teaching in which instructors prioritize acquiring pertinent content knowledge as a primary learning objective (Brown, 2003). The teacher is the fulcrum of the learning environment, having a greater wealth of knowledge about the subject being taught, relative to students’ inexperience and lack of knowledge (Wright, 2011). This distinction can engender a hierarchical relationship between teacher and students in the classroom. Teacher-student relationships primarily are defined by intellectual explorations chosen by the teacher, in which the teacher is an arbiter and distributor of knowledge and students are receivers of knowledge (Wright, 2011).

Instructors using a teacher-centered approach predominantly rely on lecture to transmit knowledge to students, and typically prioritize the acquisition of content, as students are evaluated on their ability to accurately reproduce knowledge that they are provided (Brown, 2003). While lecturing is acknowledged in the literature as a tool that can be helpful for stimulating student learning, instructors who rely heavily on lecture-intensive approaches have come under criticism and have been linked with students adopting surface approaches to learning (Diseth, 2007). Bain (2004) cautioned that instructors’ use of didactic-intensive forms of instruction may stunt students’ curiosity and appetite for learning, as students may become accustomed to being passive receptacles for information. Various authors in the counseling literature have posited that supplementing lecture with alternative or innovative teaching approaches can help engage student learning so that students can more effectively access and apply what they have learned in their work as professional counselors (May, 2004; Shephard & Brew, 2005; Stinchfield, 2006).

Learner-Centered Pedagogy
Learner-centered pedagogy emerged from constructivist learning theory and represents a countermovement to traditional teacher-centered pedagogical practices (Baeten, Dochy, & Struyven, 2012; McAuliffe & Eriksen, 2002). Educators who use learner-centered pedagogy view knowledge through lenses of social and relational processes and therefore prioritize students’ individual processes of constructing personal knowledge and understanding rather than rote mastery of course content (Baeten et al., 2012). These instructors must be comfortable with the uncertainty and needed flexibility that come with self-reflection and change, both in themselves and their students (McAuliffe & Eriksen, 2002). Such instructors place learning at the center of the classroom environment, where both teacher and students share responsibility for creating a meaningful learning experience. In contrast, teacher-centered instructors assume the majority of responsibility for teaching and ensuring that learning is occurring, and they represent the most prominent aspect of the learning environment rather than having that space filled by the topic of interest.

The primary task of an instructor using a learner-centered approach is to create an environment that is conducive to learning. Although a strong grasp of course content and use of lecture may be helpful in this endeavor, they represent only two of several important components of such a learning environment. Brown (2003) stated that the focus on the process of learning and the context in which learning occurs is considered to be as integral as, or more integral than, the specific content knowledge presented to students. McCombs (as cited in Cornelius-White, 2007) described some characteristics of learning environments that are based on learner-centered assumptions:

[Learning is] non-linear, recursive, continuous, complex, relational, and natural in humans. . . . Learning is enhanced in contexts where learners have supportive relationships, have a sense of ownership and control over learning processes, and can learn with and from each other in safe and trusting learning environments. (p. 7)
Two important components that learner-centered teachers consider when establishing a positive learning environment are providing supportive relationships in the classroom and creating a space that feels safe and trusting to student learners (Weimer, 2002). Instructors using a learner-centered approach foster supportive relationships and cultivate a safe learning environment by diffusing power differentials between the teacher and students. Instructors diffuse power differentials through intentionally creating opportunities for students to become active in the classroom, honoring and utilizing student learners’ individual experiences and perspectives, and treating students as partners in the learning process (Crick & McCombs, 2006). Thus, instead of the instructor being the primary arbiter of content, intellectual queries and structure in a classroom, a learner-centered instructor favors democratic and collaborative approaches to teaching that empower students to be active participants in their learning (Wright, 2011). An example of this practice occurs when an instructor intentionally defers from immediately answering a student’s question and rather redirects the question to the students in the classroom. Such an approach diminishes the instructor’s role as “expert” in the classroom; connotes a belief that student learners possess the collective knowledge, experiences and perspectives to provide useful insight to answer the question; and encourages students to become intellectually active in the classroom.

Such collaborative learning is an important aspect of learner-centered teaching since collaboration is a social process believed to help students develop problem-solving skills, challenge their beliefs through honoring many viewpoints in the classroom and construct deeper personal understandings of course content (Brown, 2003). Instructors can nurture collaborative relationships by following two learner-centered principles: students prefer to have a sense of ownership and control over their learning experiences, and students should receive opportunities to teach each other what they have learned (Weimer, 2002).  Therefore, student learners’ preferences and opinions are taken into account when possible during course planning (e.g., having a class discussion about setting class rules) and when selecting reading assignments or major course projects (e.g., allowing student learners to create their own projects; providing student learners with a variety of assignments from which to select their course projects). Student learners then perceive that they are able to shape their learning experience in a meaningful way. After students have engaged in self-directed learning projects outside the classroom, they are then given opportunities to deepen their learning through sharing what they have learned with their classmates (Brown, 2003).

In addition to increased autonomy to construct their learning experiences, student learners receive autonomy to pursue areas of intellectual interest in the classroom. Learner-centered instructors provide opportunities for their students to explore topics of interest in depth by adhering less strictly to course content (Baeten, Struyven, & Dochy, 2013). Course content is used as a starting point for stimulating intellectual exploration in students. Students are encouraged to explore content and topics of interest when their instructors create space for inquiry, discussion or other spontaneous learning experiences in the classroom (Weimer, 2002). Thus, learner-centered instructors favor flexible approaches to teaching that create space for students to learn about topics of interest with greater depth, rather than teacher-centered approaches that ensure a broad coverage of course content.

Student learners’ active role and sense of autonomy during class is counterbalanced by learner-centered instructors taking a more peripheral role, acting as guides who encourage students on their own path of inquiry and understanding (Wright, 2011). Teachers using a learner-centered approach help facilitate students’ learning interests as they arise by guiding discussion and inquiry, while being mindful to incorporate various learning experiences in the classroom. Incorporating flexible and varied teaching practices (e.g., lecture, multimedia, experiential activities, discussion) is a key aspect of facilitating a learner-centered classroom environment so that a wider range of student learner preferences can be satisfied (Brown, 2003). Teachers using a learner-centered approach attempt to formulate their teaching practices based on the learning preferences of students in their classes, unlike instructors who use teaching practices that are based on the instructors’ preferences.

By teaching with a learner-centered focus, counselor educators may increase the likelihood that trainees will perceive their classroom as a positive learning environment. Counselor trainees’ positive appraisal of a learning environment can help them to see the purpose and meaning in their learning experience, which may in turn influence their use of a deep approach to learning. Using a deep approach to learning, in which counselor trainees are reflective and ascribe personal meaning to knowledge that is learned, can help prepare trainees for future work as professional counselors when they will be required to think independently and tolerate ambiguity (Dollarhide et al., 2007). Therefore, counselor educators teaching didactic classes with a learner-centered focus are concerned with helping counselor trainees develop how they think (e.g., critically, reflectively, complexly) rather than simply what they think (i.e., memorization of specific content). This phenomenon is demonstrated in the following case study.

Case Study: A Commentary
When Randy (first author) first asked me (Jane; second author) to join in this project about learner-centered teaching, I was excited to do so. At the time, Randy was a doctoral candidate and I was a faculty member in a counselor education program. I consider myself to be student-centered, an effective facilitator of student learning and a postmodernist who takes a nonexpert stance with students. Randy asked me to develop a case study of a traditionally didactic course taught from a learner-centered course approach. Again, I was excited to do so, thinking that this would be an easy task, in light of my learner-centered approach to teaching.

Yet when I began to think about a course to use as a case study, one that would demonstrate a learner-centered approach, I began to doubt that I was truly learner-centered. The course I was considering was a couples counseling course that I had taught for years, a traditionally “didactic” course. Though I had incorporated a number of experiential activities into this course, I continued to lecture frequently (about half of the class time), believing that students benefit from listening to and asking questions about the theories and techniques they are learning. So was I learner-centered? Did I even have a class that I could present as a case study?

Randy and I had lively conversations that expanded my thinking about learner-centered teaching. I told him that I was struggling to differentiate experiential learning from learner-centered teaching, and that I did not think I was as learner-centered as I had believed. Experiential learning, contextual learning and problem-based learning all became a bit of a muddle for me, as there is considerable overlap between these concepts about teaching. Randy noted Barrett’s (2007) view that teaching does not have to be either-or, teacher-centered or learner-centered, but can be on a continuum between both. With this idea in mind, I reconsidered the couples counseling course and reflected on ways that my teaching might evidence a learner-centered approach.

The couples counseling course that I teach typically has 20–25 master’s students enrolled, along with a few doctoral students. It could be considered a content-heavy, didactic course covering couples therapy theories, focusing on concepts and techniques specific to couples counseling and their application in the therapeutic setting. As mentioned, I lecture in the course about these concepts and techniques and also provide students with experiences through class activities and homework assignments that aim to help students think about how they might eventually apply their learning to counseling practice.

I set up one such in-class experience by inviting an underrepresented couple, often a same-sex couple, to class to talk about their experiences as a couple. Either I or a doctoral student interview the couple about the development of their relationship, experiences they have had with others recognizing (or not recognizing) their relationship, misperceptions heterosexual counselors might have about them as a couple, and so forth. The hope is that students will gain some understanding of the issues and oppression that face nondominant couples.

Before the class session during which the couple visits, I ask six students to serve as a team who will reflect on the interview at its conclusion. Members of this reflecting team (Andersen, 1991) talk together about what stood out to them from the interview, what they saw as the couple’s strengths and how they understood the couple’s challenges (especially as related to their couple status in the eyes of others), holding this conversation together as the other class members and the couple quietly listen. At the conclusion of the team’s conversation, the couple respond to what they have heard, and the rest of the students have the opportunity to comment and ask the couple questions.

I consider this activity to be learner-centered, since much of the conversation is driven by the students on the reflecting team and the class as a whole. Yet it also is a structured activity, guided and facilitated by me as the instructor. I am very intentional about how I structure this activity. For instance, I would not have a class immediately start interacting with the couple, perhaps in an effort to protect the couple. Rather, the structure is intended to give students time to think about the couple and their life circumstances, time to be thoughtful about what they wish to say to the couple. In this sense, I orchestrate the experience, though eventually allow for improvisation by students. As the conductor and facilitator, I hope to encourage all the individual, unique voices of the students while also sharing responsibility with students for creating a moment that is meaningful and causes reflection and learning.

In sharing this responsibility, I have to share power with the students (as all facilitators must do) by having them interact with the couple during the reflecting team process and the following large group discussion. I cannot control the student responses, nor would I want to. Yet I have my moments of concern that a student will be insensitive to the couple, perhaps even add to the oppression they have experienced throughout their relationship. Being more learner-centered does not mean that I fully trust, at all times, all that students have to offer; it means that I believe the risk is worth the potential gain.

After this experience, students write a reflection paper about what they learned from the interview with the couple and the following classroom conversations and what questions linger for them. Students (perhaps straight students) often write that they have a new perspective on gay couples, realizing that many of their challenges are similar to challenges faced by all couples, gay or straight. They also reflect on the many ways that gay couples are discriminated against, often sharing their surprise at instances of discrimination that the couple has experienced. In their course evaluations at the end of the semester, students often comment that this classroom experience is the highlight of the course, the piece they remember most.

In addition, a homework assignment in the couples class complements the in-class couples interview. Outside class, students are asked to conduct two interviews with couples in different phases of their couple developmental cycle. Students are asked to interview a nondominant couple (e.g., gay, lesbian, interracial, interreligious) for at least one of these interviews in order to better understand some of the concerns these couples have due to living in our society, concerns that would most likely not be experienced by more highly represented couples (e.g., straight, same race, same religion). Students then write about and share in class what they learned from these interviews. As with the in-class interview, this out-of-class assignment is an experiential activity that hopefully expands students’ notions of who couples are, what their concerns are as a couple and how they find satisfaction as a couple. The goal of both the in-class and out-of-class interviews is to help students gain multiple perspectives to aid them in their future work with couples in counseling.

Although I greatly value experiential learning (such as described above), I also share information with students through a lecture format and, in that sense, take on somewhat of an expert role. Some educators may assume a nonexpert role much of the time, serving primarily as a facilitator of students’ learning through application and experience. Tärnvik (2007) even stated that the teacher need not be overly familiar with the material being taught. Rather, a teacher’s role is to create experiences for students. Though this approach may work for some, it does not fit with my philosophy of teaching. It is hard to imagine asking students to get close to course content if I do not have strong knowledge of the material. Being learner-centered does not mean that there are not times when I help students better understand the material, either by asking questions for them to respond to or by directly telling them about the content. Though this is an “expert” stance, I have come to believe that being learner-centered does not mean that, at all times, I let students take the lead while I follow. Learner-centered ways of teaching do not have to be either-or—that is, either I totally give control to students or I am teacher-centered and take full control. Rather, teaching can be both-and; there are times to give more control in the classroom to students and there are times to take back the reins. The skill, or perhaps the art, of learner-centered teaching may be to discern when it is best to do one or the other. In the in-class experience discussed above, I was intentional in setting up the structure for the couple’s experience with the class (I controlled this), as well as opening up space for student involvement (during the reflecting team experience and the following group discussion). This notion of opening up space for students to learn seems to be at the core of learner-centered teaching. In reflection on Parker Palmer’s (1990) quote “to teach is to create a space,” O’Reilley (1998) wrote the following:

These are revolutionary words, because most of us think in terms of filling a space: filling the number of minutes between the beginning and end of class, filling the student’s notebook, filling the student’s head. . . . To “create a space” acknowledges both our sphere of responsibility and our lack of control. (p. 2)

It is exciting, although rather scary, to think about both “our sphere of responsibility and our lack of control” (O’Reilley, 1998, p. 2). This open space is less certain than space that I fill and presents certain questions for me, such as “What will students say?,” “Will I know how to respond to what they say?” and “Will they say anything at all?” Yet it also is troubling to think that there are no spaces during a class that provide students with the opportunity for improvisation, expression and contribution.

When I teach classes such as the couples therapy course, I find myself often reflecting on how I can balance teaching a large class, covering content that is essential to the subject and creating space for my students to interact with the content (to improvise). There are many ways to accomplish this task, but I have found that when I lecture I tend to conceptualize the content as a starting point for student engagement, rather than an end point. As such, when I lecture I try to leave space open for student inquiry and for discussion to occur naturally, rather than sticking rigidly to my teaching agenda. Though students certainly benefit from learning important conceptual knowledge, it has been my experience that some of the richest learning experiences for both students and me occur during spontaneous discussions that begin with the lecture material and end in a place I did not plan for or anticipate. My hope is that rich discussions, often filled with ambiguity and complexity, contribute to students’ preparation for their multifaceted work as counselors.

 

Limitations of Learner-Centered Pedagogy and Future Research

There is a danger in thinking of teacher-centered and learner-centered methods of teaching as dichotomous and discrete. This either-or simplification may be appropriate for generating clear theoretical distinctions, but it is not appropriate for capturing the complex practices of teachers and teaching (Barrett, 2007). It would probably be inaccurate to describe most teachers as being either teacher-centered or learner-centered. In practice, teachers draw on a variety of pedagogical influences, which manifest themselves in a blend of approaches that are unique to that individual (Barrett, 2007). It may be more helpful to conceptualize teacher-centered and learner-centered pedagogy as ideological bookends that exist on a continuum. Thus, an approach to teaching could be considered more teacher-centered or more learner-centered, rather than either teacher-centered or learner-centered.

 

Although some researchers have provided a favorable outlook on learner-centered pedagogy (Vanthournout et al., 2004; Wilson & Fowler, 2005), other researchers have found that students may learn best through teacher-centered approaches (Baeten et al., 2012) or a combination of teacher-centered and learner-centered pedagogical approaches (Baeten et al., 2013). These mixed findings, in conjunction with limited pedagogical research in counselor education, highlight a need for future research to investigate student learner preferences in master’s counseling programs. A fruitful direction for future research would be to explore the perceptions of recent graduates who are now working in professional counseling environments to gain an understanding of what novice counselors perceive as being helpful pedagogical practices during their master’s program. These graduates could offer valuable insight into what teaching practices were most helpful for preparing them for the demands they face working as novice professional counselors. Greater understanding of what pedagogical practices are preferred by students in master’s programs in counselor education, from the perspective of counselor trainees or novice professional counselors, could help educators become more learner-centered by allowing them to tailor their own teaching practices to meet the needs of student learners in their classrooms.

 

Another area of possible research to investigate is how counselor education doctoral students learn to teach. Researchers could review syllabi of college teaching courses to examine how doctoral students are being taught to teach, particularly noting if and how the syllabi reflect a learner-centered or teacher-centered approach. Researchers also could interview counselor education doctoral students and recent graduates to explore ways they learned to be instructors, especially ways that reflect learner-centered or teacher-centered approaches. Learning more about how doctoral students are being taught to teach will illuminate current teaching practices in counselor education at the doctoral level and assist counselor educators to thoughtfully and intentionally examine their beliefs about teaching and make corresponding changes to their courses.

Conclusion

Counselor educators can benefit from being reflective about our own teaching practices. Thinking about learner-centered pedagogy may be a useful way to reflect on one’s teaching practice and to consider integrating other pedagogical practices into one’s own style of teaching. Although some counselor educators may identify as being either teacher-centered or learner-centered, it is likely that many will see merit in both approaches. It is not necessary for counselor educators to wholly endorse learner-centered pedagogy as their preferred teaching identity in order to infuse learner-centered principles into their teaching. General learner-centered principles compatible with diverse teaching styles and classroom settings include the following: assessing the learning needs and interests of students in the classroom as a starting point for making decisions about what will be taught, creating spaces during class time where spontaneous learning can occur, and providing opportunities for autonomous and self-directed learning experiences (Brown, 2003). Infusing learning-centered pedagogy into one’s teaching may facilitate a deep learning experience for students, which will augment their development as emergent counselors.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

References

 

Andersen, T. (Ed.). (1991). The reflecting team: Dialogues and dialogues about the dialogues. New York, NY: Norton.

Baeten, M., Dochy, F., & Struyven, K. (2012). Using students’ motivational and learning profiles in investigating their perceptions and achievement in case-based and lecture-based learning environments. Educational Studies, 38, 491–506. doi:10.1080/03055698.2011.643113

Baeten, M., Struyven, K., & Dochy, F. (2013). Student-centred teaching methods: Can they optimise students’ approaches to learning in professional higher education? Studies in Educational Evaluation, 39, 14–22. doi:10.1016/j.stueduc.2012.11.001

Bain, K. (2004). What the best college teachers do. Cambridge, MA: Harvard University Press.

Barrett, A. M. (2007). Beyond the polarization of pedagogy: Models of classroom practice in Tanzanian primary schools. Comparative Education, 43, 273–294. doi:10.1080/03050060701362623

Brown, K. L. (2003). From teacher-centered to learner-centered curriculum: Improving learning in diverse classrooms. Education, 124, 49–54

Cornelius-White, J. (2007). Learner-centered teacher-student relationships are effective: A meta-analysis. Review of Educational Research, 77, 113–143. doi:10.3102/003465430298563

Crick, R. D., & McCombs, B. L. (2006). The assessment of learner-centered practices surveys: An English case study. Educational Research and Evaluation, 12, 423–444. doi:10.1080/13803610600697021

Diseth, Ǻ. (2007). Students’ evaluation of teaching, approaches to learning, and academic achievement. Scandinavian Journal of Educational Research, 51, 185–204. doi:10.1080/00313830701191654

Dollarhide, C. T., Smith, A. T., & Lemberger, M. E. (2007). Counseling made transparent: Pedagogy for a counseling theories course. Counselor Education and Supervision, 46, 242–253.

Furr, S. R., & Carroll, J. J. (2003). Critical incidents in student counselor development. Journal of Counseling & Development, 81, 483–489. doi:10.1002/j.1556-6678.2003.tb00275.x

Granello, D. H. (2000). Contextual teaching and learning in counselor education. Counselor Education and Supervision, 39, 270–283. doi:10.1002/j.1556-6978.2000.tb01237.x

Grant, J. (2006). Training counselors to work with complex clients: Enhancing emotional responsiveness through experiential methods. Counselor Education and Supervision, 45, 218–230. doi:10.1002/j.1556-6978.2006.tb00144.x

Harris, M., & Cullen, R. (2008). Learner-centered leadership: An agenda for action. Innovation in Higher Education, 33, 21–28. doi:10.1007/s10755-007-9059-3

Lindblom-Ylänne, S. (2004). Raising students’ awareness of their approaches to study. Innovations in Education and Teaching International, 41, 405–421. doi:10.1080/1470329042000277002

May, K. M. (2004). How do we teach family therapy theory? The Family Journal: Counseling and Therapy for Couples and Families, 12, 275–277. doi:10.1177/1066480704264545

McAuliffe, G., & Eriksen, K. (2002). Teaching strategies for constructivist and developmental counselor education. London, England: Bergin & Garvey.

Nelson, M. L., & Neufeldt, S. A. (1998). The pedagogy of counseling: A critical examination. Counselor Education and Supervision, 38, 70–88. doi:10.1002/j.1556-6978.1998.tb00560.x

O’Reilley, M. R. (1998). Radical presence: Teaching as contemplative practice. Portsmouth, NH: Boynton/Cook.

Orlinsky, D. E., Botermans, J.-F., Rønnestad, M. H., & the SPR Collaborative Research Network (2001). Towards an empirically grounded model of psychotherapy training: Four thousand therapists rate influences on their development. Australian Psychologist, 36, 139–148. doi:10.1080/00050060108259646

Palmer, P. J. (1990). Good teaching: A matter of living the mystery. Change: The Magazine of Higher Learning, 22(1), 11–16. doi:10.1080/00091383.1990.9937613

Parpala, A., Lindblom-Ylänne, S., Komulainen, E., Litmanen, T., & Hirsto, L. (2010). Students’ approaches to learning and their experiences of the teaching–learning environment in different disciplines. British Journal of Educational Psychology, 80, 269–282. doi:10.1348/000709909X476946

Shepard, D. S., & Brew, L. (2005). Teaching theories of couples counseling: The use of popular movies. The Family Journal: Counseling and Therapy for Couples and Families, 13, 406–415. doi:10.1177/1066480705278470

Sperry, L. (2012). Training counselors to work competently with individuals and families with heath and mental health issues. The Family Journal: Counseling and Therapy for Couples and Families, 20, 196–199. doi:10.1177/1066480712438527

Stinchfield, T. A. (2006). Using popular films to teach systems thinking. The Family Journal: Counseling and Therapy for Couples and Families, 14, 123–128. doi:10.1177/1066480705285559

Tärnvik, A. (2007). Revival of the case method: A way to retain student-centred learning in a post-PBL era. Medical Teacher, 29, e32–e36. doi:10.1080/01421590601039968

Vanthournout, G., Donche, V., Gijbels, D., & Van Petegem, P. (2004). Alternative data-analysis techniques in research on student learning: Illustrations of a person-oriented and developmental perspectives. Reflective Education, 5(2), 35–51.

Weimer, M. (2002). Learner-centered teaching: Five key changes to practice. San Francisco, CA: Jossey-Bass.

Wilson, K., & Fowler, J. (2005). Assessing the impact of learning environments on students’ approaches to learning: Comparing conventional and action learning designs. Assessment & Evaluation in Higher Education, 30, 87–101. doi:10.1080/0260293042000251770

Wright, G. B. (2011). Student-centered learning in higher education. International Journal of Teaching and Learning in Higher Education, 23, 92–97.

 

Randall M. Moate, NCC, is an Assistant Professor at the University of Texas-Tyler. Jane A. Cox, NCC, is an Associate Professor at Kent State University. Correspondence may be addressed to Randall M. Moate, Department of Psychology and Counseling, University of Texas-Tyler, 3900 University Blvd., Tyler, TX  75707, randallmoate@gmail.com.