A Case Study Exploring Supervisee Experiences in Social Justice Supervision

Clare Merlin-Knoblich, Jenna L. Taylor, Benjamin Newman


Social justice is a paramount concept in counseling and supervision, yet limited research exists examining this idea in practice. To fill this research gap, we conducted a qualitative case study exploring supervisee experiences in social justice supervision and identified three themes from the participants’ experiences: intersection of supervision experiences and external factors, feelings about social justice, and personal and professional growth. Two subthemes were also identified: increased understanding of privilege and increased understanding of clients. Given these findings, we present practical applications for supervisors to incorporate social justice into supervision.

Keywords: social justice, supervision, case study, personal growth, practical applications


Social justice is fundamental to the counseling profession, and, as such, scholars have called for an increase in social justice supervision (Ceballos et al., 2012; Chang et al., 2009; Collins et al., 2015; Dollarhide et al., 2018, 2021; Fickling et al., 2019; Glosoff & Durham, 2010). Although researchers have studied multicultural supervision in the counseling profession, to date, minimal research has been conducted on implementing social justice supervision in practice (Dollarhide et al., 2021; Fickling et al., 2019; Gentile et al., 2009; Glosoff & Durham, 2010). In this study, we sought to address this research gap with an exploration of master’s students’ experiences with social justice supervision.

Social Justice in Counseling
     Counseling leaders have developed standards that reflect the profession’s commitment to social justice principles (Chang et al., 2009; Dollarhide et al., 2021; Fickling et al., 2019; Glosoff & Durham, 2010). For instance, the American Counseling Association’s ACA Code of Ethics (2014) highlights the need for multicultural and diversity competence in six of its nine sections, including Section F, Supervision, Training, and Teaching. Additionally, in 2015, the ACA Governing Council endorsed the Multicultural and Social Justice Counseling Competencies (MSJCC), which provide a framework for counselors to use to implement multicultural and social justice competencies in practice (Fickling et al., 2019; Ratts et al., 2015). All of these standards reflect the importance of social justice in the counseling profession (Greene & Flasch, 2019).

Social Justice Supervision
     Although much of the counseling profession’s focus on social justice emphasizes counseling practice, social justice principles benefit supervisors, counselors, and clients when they are also incorporated into clinical supervision. In social justice supervision, supervisors address levels of change that can occur through one’s community using organized interventions, modeling social justice in action, and employing community collaboration (Chang et al., 2009; Dollarhide et al., 2021; Fickling et al., 2019). These strategies introduce an exploration of culture, power, and privilege to challenge oppressive and dehumanizing political, economic, and social systems (Dollarhide et al., 2021; Fickling et al., 2019; Garcia et al., 2009; Glosoff & Durham, 2010; Pester et al., 2020). Moreover, participating in social justice supervision can assist counselors in developing empathy for clients and conceptualizing them from a systemic perspective (Ceballos et al., 2012; Fickling et al., 2019; Kiselica & Robinson, 2001). When a supervisory alliance addresses cultural issues, oppression, and privilege, supervisees are better able to do the same with clients (Chang et al., 2009; Dollarhide et al., 2021; Fickling et al., 2019; Glosoff & Durham, 2010). Thus, counselors become advocates for clients and the profession (Chang et al., 2009; Dollarhide et al., 2021; Gentile et al., 2009; Glosoff & Durham, 2010).

Chang and colleagues (2009) defined social justice counseling as considering “the impact of oppression, privilege, and discrimination on the mental health of the individual with the goal of establishing equitable distribution of power and resources” (p. 22). In this way, social justice supervision considers the impact of oppression, privilege, and discrimination on the supervisee and supervisor. Dollarhide and colleagues (2021) further simplified the definition of social justice supervision, stating that it is “supervision in which social justice is practiced, modeled, coached, and used as a metric throughout supervision” (p. 104). Supervision that incorporates a focus on intersectionality can further support supervisees’ growth in developing social justice competencies (Greene & Flasch, 2019).

Literature about social justice supervision often includes an emphasis on two concepts: structural change and individual care (Gentile et al., 2009; Lewis et al., 2003; Toporek & Daniels, 2018). Structural change is the process of examining, understanding, and addressing systemic factors in clients’ and counselors’ lives, such as identity markers and systems within family, community, school, work, and elsewhere. Individual care acknowledges each person within the counseling setting independent of their environment (Gentile et al., 2009; Roffman, 2002). Scholars advise incorporating both concepts to address power, privilege, and systemic factors through social justice supervision (Chang et al., 2009; Gentile et al., 2009; Glosoff & Durham, 2010; Greene & Flasch, 2019; Pester et al., 2020).

It is necessary to distinguish social justice supervision from previous literature on multicultural supervision. Although similar, these concepts are different in that multicultural supervision emphasizes cultural awareness and competence, whereas social justice supervision brings attention to sociocultural and systemic factors and advocacy (Dollarhide et al., 2021; Fickling et al., 2019; E. Lee & Kealy, 2018; Peters, 2017; Ratts et al., 2015). For instance, a supervisor practicing multicultural supervision would be aware of a supervisee’s identity markers, such as race, ethnicity, and culture, and address those components throughout the supervisory experience, whereas a supervisor practicing social justice supervision would also consider systemic factors that impact a supervisee, in addition to being culturally competent. The supervisor would use that knowledge in the supervisory alliance and act as a change agent at individual and community levels (Chang et al., 2009; Dollarhide et al., 2021; Fickling et al., 2019; Gentile et al., 2009; Glosoff & Durham, 2010; E. Lee & Kealy, 2018; Lewis et al., 2003; Peters, 2017; Ratts et al., 2015; Toporek & Daniels, 2018).

Researchers have found that multicultural supervision contributes to more positive outcomes than supervision without consideration for multicultural factors (Chopra, 2013; Inman, 2006; Ladany et al., 2005). For example, supervisees who participated in multicultural supervision reported that supervisors were more likely to engage in multicultural dialogue, show genuine disclosure of personal culture, and demonstrate knowledge of multiculturalism than supervisors who did not consider multicultural concepts in supervision (Ancis & Ladany, 2001; Ancis & Marshall, 2010; Chopra, 2013). Supervisees also reported that multicultural considerations led them to feel more comfortable, increased their self-awareness, and spurred them on to discuss multiculturalism with clients (Ancis & Ladany, 2001; Ancis & Marshall, 2010). Although parallel research on social justice supervision is lacking, findings on multicultural supervision are a promising indicator of the potential of social justice supervision.

     In recent years, scholars have called for social justice supervision models to integrate social justice into supervision (Baggerly, 2006; Ceballos et al., 2012; Chang et al., 2009; Collins et al., 2015; Glosoff & Durham, 2010; O’Connor, 2005). However, to date, only three formal models of social justice supervision have been published. Most recently, Dollarhide and colleagues (2021) recommended a social justice supervision model that can be used with any supervisory theory, developmental model, and process model. In this model, the MSJCC are integrated using four components. First, the intersectionality of identity constructs (i.e., gender, race/ethnicity, socioeconomic status, sexual orientation, abilities, etc.) is identified as integral in the supervisory triad between supervisor, counselor, and client. Second, systemic perspectives of oppression and agency for each person in the supervisory triad are at the forefront. Third, supervision is transformed to facilitate the supervisee’s culturally informed counseling practices. Lastly, the supervisee and client experience validation and empowerment through the mutuality of influence and growth (Dollarhide et al., 2021).

Prior to Dollarhide and colleagues’ (2021) model for social justice supervision, Gentile and colleagues (2009) proposed a feminist ecological framework for social justice supervision. This model encouraged the understanding of a person at the individual level through interactions within the ecological system (Ballou et al., 2002; Gentile et al., 2009). The supervisor’s role is to model socially just thinking and behavior, create a climate of equality, and implement critical thinking about social justice (Gentile et al., 2009; Roffman, 2002).

Lastly, Chang and colleagues (2009) suggested a social constructivist framework to incorporate social justice issues in supervision via three delineated tiers (Chang et al., 2009; Lewis et al., 2003; Toporek & Daniels, 2018). In the first tier, self-awareness, supervisors assist supervisees to recognize privileges, understand oppression, and gain commitment to social justice action (Chang et al., 2009; C. C. Lee, 2007). In the second tier, client services, the supervisor understands the clients’ worldviews and recognizes the role of sociopolitical factors that can impact the developmental, emotional, and cognitive meaning-making system of the client (Chang et al., 2009). In the third tier, community collaboration, the supervisor guides the supervisee to advocate for changes on the group, organizational, and institutional levels. Supervisors can facilitate and model community collaboration interventions, such as providing clients easier access to resources, participating in lobbying efforts, and developing programs in communities (Chang et al., 2009; Dinsmore et al., 2002; Kiselica & Robinson, 2001).

Each of these supervision models serves as a relevant, accessible tool for counseling supervisors to use to incorporate social justice into supervision (Chang et al., 2009, Dollarhide et al., 2021; Gentile et al., 2009). However, researchers lack an empirical examination of any of the models. To address this research gap and begin understanding social justice supervision in practice, the present qualitative case study exploring master’s students’ experiences with social justice supervision was undertaken.

We selected Chang and colleagues’ (2009) three-tier social constructivist framework in supervision for several reasons. First, the social constructivist framework incorporates a tiered approach similar to the MSJCC (Ratts et al., 2015) and reflects social justice goals in the profession of counseling (Ceballos et al., 2012; Chang et al., 2009; Collins et al., 2015; Glosoff & Durham, 2010). Second, the model is comprehensive. In using three tiers to address social justice (self, client, and community), the model captures multiple layers of social justice influence for counselors. Finally, the model is simple and meets the developmental needs of novice counselors. By identifying three tiers of social justice work, Chang and colleagues (2009) crafted an accessible tool to help new and practicing school counselors infuse social justice into their practice. This high level of structure matches the initial developmental levels of new counselors, who typically benefit from high amounts of structure and low amounts of challenge in supervision (Foster & McAdams, 1998).


The research question guiding this study was: What are the experiences of master’s counseling students in individual social justice supervision? We used a social constructivist theoretical framework and presumed that knowledge would be gained about the participants’ experiences based on their social constructs (Hays & Singh, 2012). The ontological perspective reflected realism, or the belief that constructs exist in the world even if they cannot be fully measured (Yin, 2017).

We selected a qualitative case study methodology because it was the most appropriate approach to explore the experiences of a single group of supervisees supervised by the same supervisor in the same semester. In this approach, researchers examine one identified unit bounded by space, time, and persons (Hancock et al., 2021; Hays & Singh, 2012; Yin, 2017). Qualitative case study research allows researchers to deeply explore a single case, such as a group, person, or experience, and gain an in-depth understanding of that identified situation, as well as meaning for the people involved in it (Hancock et al., 2021; Prosek & Gibson, 2021).

In this study, we selected a case study methodology because the study’s participants engaged in the same supervisory experience at the same counseling program in the same semester, thus forming a case to be studied (Hancock et al., 2021). Given the research question, we specifically used a descriptive case study design, which reflected the study goals to describe participants’ experiences in a specific social justice supervision experience. Case study scholars (Hancock et al., 2021; Yin, 2017) have noted that identifying the boundaries of a case is an essential step in the study process. Thus, the boundaries for this study were: master’s-level school counseling students receiving social justice supervision from the same supervisor (persons) at a medium-sized public university on the East Coast (place) over the course of a 14-week semester (time).

Research Team
     Our research team for this study consisted of our first and third authors, Clare Merlin-Knoblich and Benjamin Newman, both of whom received training and had experience in qualitative research. Merlin-Knoblich and Newman both identify as White, heterosexual, cisgender, middle-class, and trained counselor educators/supervisors. Merlin-Knoblich is a woman (pronouns: she/her/hers) and former school counselor, who completed master’s and doctoral coursework on social justice counseling and studied social justice supervision in a doctoral program. Newman is a man (pronouns: he/him/his) and clinical mental health/addictions counselor, who completed social justice counseling coursework in a master’s counseling program before completing a doctorate in counselor education and supervision. Our second author, Jenna L. Taylor, was not a part of the research team, but rather was a counseling student unaffiliated with the research participants who assisted in the preparation of the manuscript. Taylor identifies as a White, heterosexual, cisgender, and middle-class woman (pronouns: she/her/hers) with prior experience in research courses and on qualitative research teams. Merlin-Knoblich was familiar with all three participants given her role as the practicum supervisor. Taylor and Newman did not know the study participants beyond Newman’s interactions while recruiting and interviewing them for this study.

Participants and Context
     Although some scholars of some qualitative research methodologies call for requisite minimum numbers of participants, in case study research, there is no minimum number of participants sufficient to study (Hays & Singh, 2012). Rather, in case study research, researchers are expected to study the number of participants needed to reflect the phenomenon being studied (Hancock et al., 2021). There were three participants in this study because the supervisory experience that comprised the case studied included three supervisees. Adding additional participants outside of the case would have conflicted with the boundaries of the case and potentially interfered with an understanding of the single, designated case in this study.

All study participants identified as White, heterosexual, cisgender, middle-class, and English-speaking women (pronouns: she/her/hers). Participants were 23, 24, and 26 years old. All the participants were students in the same CACREP-accredited school counseling program at a public liberal arts university on the East Coast of the United States. Prior to the study, the participants completed courses in techniques, group counseling, school counseling, ethics, and theories. While being supervised, participants also completed a practicum experience and coursework in multicultural counseling and career development.

All participants completed practicum at high schools near their university. One high school was urban, one was suburban, and one was rural. During the practicum experience, participants met with Merlin-Knoblich, their supervisor, for face-to-face individual supervision for 1 hour each week. They also submitted weekly journals to Merlin-Knoblich, written either freely or in response to a prompt, depending on their preference. Merlin-Knoblich then provided weekly written feedback to each participant’s journal entry, and, if relevant, the journal content was discussed during face-to-face supervision. Simultaneously, a university faculty member provided weekly face-to-face supervision-of-supervision to Merlin-Knoblich to monitor supervision skills and ensure adherence to the identified supervision model. The faculty member possessed more than 15 years of experience in supervision and was familiar with social justice supervision models.

Merlin-Knoblich applied Chang and colleagues’ (2009) social constructivist social justice supervision model in deliberate ways throughout the supervisees’ 14-week practicum experience. For example, in the initial supervision sessions, Merlin-Knoblich introduced the supervision model and explained how they would collaboratively explore ideas of social justice in counseling related to their practicum experiences. This included defining social justice, discussing supervisees’ previous background knowledge, and exploring their openness to the idea.

Throughout the first 5 weeks of supervision, Merlin-Knoblich used exploratory questions to build participants’ self-awareness (the first tier), particularly around their experiences with privilege and oppression. During the next 5 weeks of supervision, Merlin-Knoblich focused on the second tier, understanding clients’ worldviews. They discussed sociopolitical factors and examined how a client’s worldview impacts their experiences. For example, Merlin-Knoblich discussed how a client’s age, race/ethnicity, socioeconomic status, family structure, language, immigrant status, gender identity, sexual orientation, and other factors can influence their experiences. Lastly, in the final 4 weeks of supervision, Merlin-Knoblich focused on the third tier of social justice implications at the institutional level. For instance, Merlin-Knoblich initiated discussions about policies at participants’ practicum sites that hindered equity. Merlin-Knoblich also explored the role that participants could take in making resources available to clients, advocating in the community, and using leadership to support social justice. Table 1 summarizes how Merlin-Knoblich implemented Chang and colleagues’ (2009) social justice model.


Table 1

Social Justice Supervision in Practice


Merlin-Knoblich addressed fidelity to the supervision model in two ways. First, in weekly supervision-of-supervision meetings with the faculty advisor, they discussed the supervision model and its use in sessions with participants. The faculty advisor regularly asked about the supervision model and how it manifested in sessions in an attempt to ensure that the model was being implemented recurrently. Secondly, engagement with Newman occurred in regular peer debriefing discussions about the use of the supervision model. Through these discussions, Newman monitored Merlin-Knoblich’s use of the social justice model throughout the 14-week supervisory experience.

Data Collection
     We obtained IRB approval prior to initiating data collection. One month after the end of the semester and practicum supervision, Newman approached Merlin-Knoblich’s three supervisees about participation in the study. He explained that participation was an exploration of the supervisees’ experiences in supervision and not an evaluation of the supervisees or the supervisor. Newman also emphasized that participation in the study was confidential, entirely voluntary, and  would not affect participants’ evaluations or grades in the practicum course, which ended before the study took place. All supervisees agreed to participate.

Case study research is “grounded in deep and varied sources of information” (Hancock et al., 2021) and thus often incorporates multiple data sources (Prosek & Gibson, 2021). In the present study, we identified two data sources to reflect the need for varied information sources (Hancock et al., 2021). The first data source came from semistructured interviews with participants, a frequent data collection tool in case study research (Hancock et al., 2021). One month after the participants’ practicum experiences ended, Newman conducted and audio-recorded 45-minute individual in-person interviews with each participant using a prescribed interview protocol that explored participants’ experiences in social justice supervision. Newman exercised flexibility and asked follow-up questions as needed (Merriam, 1998).

The interview protocol contained 12 questions identified to gain insights into the case being studied (Hancock et al., 2021). Merlin-Knoblich and Newman designed the interview protocol by drafting questions and reflecting on three influences: (a) the overall research question guiding the study, (b) the social constructivist framework of the study, and (c) Chang and colleagues’ (2009) three-tier supervision model. Questions included “In what ways, if any, has the social justice emphasis in your supervision last semester influenced you as a counselor?” Questions also addressed whether or not the emphasis on social justice at each tier (i.e., self, client, institution) affected participants. Appendix A contains a list of all interview questions.

The second data source was participants’ practicum journals. In addition to interviewing the participants about experiences in supervision, we also asked participants if their practicum journals could be used for the study’s data analysis. The journals served as a valuable form of data to answer the research question, given their informative and non-prescriptive nature. That is to say, although participants knew during the study interviews that the interview data would be used for analysis for the present study, they wrote and submitted their journals before the study was conceptualized. Thus, the journals reflected in-the-moment ideas about participants’ practicum and social justice supervision. Furthermore, this emphasis on participant experiences during the supervisory experience aligned with the methodological emphasis on studying a case in its natural context (Hancock et al., 2021). All participants consented for their 14 practicum journal entries (each 1–2 pages in length) to be analyzed in the study, and they were added to the interview data to be analyzed together. Such convergent analysis of data is typical in case study research (Prosek & Gibson, 2021).

Data Analysis
     We followed Yin’s (2017) case study research guidelines throughout the data analysis process. We transcribed all interviews, replaced participants’ names with pseudonyms, and sent participants the transcripts for member checking. Two participants approved their interview transcripts without objection. One participant approved the transcript but chose to share additional ideas about the supervisory experience via a brief email. This email was added to the data. The case study database was then formed with the compiled participants’ journal entries, the additional email, and the interview data (Yin, 2017).

Next, we read each interview transcript and journal entry twice in an attempt to become immersed in the data (Yin, 2017). We then independently open coded transcripts by identifying common words and phrases while maintaining a strong focus on the research question and codes that answered the question (Hancock et al., 2021). We compared initial codes and then collaboratively narrowed codes into cohesive categories representing participants’ experiences. This process generated a list of tentative categories across data sources (Yin, 2017). Throughout these initial processes, we attended to two of Yin’s (2017) four principles of high-quality data analysis: attend to all data and focus on the most significant elements of the case.

We then independently contrasted the tentative categories with the data to verify that they aligned accurately. We discussed the verifications until consensus was met on all categories. Lastly, we classified the categories into three themes and two subthemes found across all participants (Stake, 2005). During these later processes, we were mindful of Yin’s (2017) remaining two principles of high-quality data analysis: consider rival interpretations of data and use previous expertise when interpreting the case. Accordingly, we reflected on possible contrary explanations of the themes and considered the findings in light of previous literature on the topic.

     We addressed trustworthiness in three ways in this study. First, before data collection, we engaged in reflexivity through acknowledging personal biases and assumptions with one another (Hays & Singh, 2012; Yin, 2017). For example, Merlin-Knoblich acknowledged that her lived experience supervising the participants might impact the interpretation of data during analysis and noted that these perceptions could potentially serve as biases during the study. Merlin-Knoblich perceived that the supervisees grew in their understanding of social justice, but also acknowledged doubt over whether the social justice supervision model impacted participants’ advocacy skills. She also noted her role as a supervisor evaluating the three participants prior to the study taking place. These power dynamics may have influenced her interpretations in the analysis process. Newman shared that his lack of familiarity with social justice supervision might impact perceptions and biases to question whether or not supervisees grew in their understanding of social justice. We agreed to challenge one another’s potential biases during data analysis in an attempt to prevent one another’s experiences from interfering with interpretations of the findings.

In addition, we acknowledged that our identities as White, English-speaking, educated, heterosexual, cisgender, middle-class researchers studying social justice inevitably was informing personal perceptions of the supervisees’ experiences. These privileged identities were likely blinding us to experiences with oppression that participants and their clients encountered and that we are not burdened with facing. Throughout the study, we discussed the complexity of studying social justice in light of such privileged identities. We spoke further about our identities and potential biases when interpreting the data.

Second, investigator triangulation was addressed by collaboratively analyzing the study’s data (Hays & Singh, 2012). Because data included both interview transcripts and journals, we confirmed that study findings were reflected in both data sources, rather than just one information source (Hancock et al., 2021). This process helped prevent real or potential biases from informing the analysis without constraint. We also were mindful of saturation of themes while comparing data across participants and sources during the analysis process. Lastly, an audit trail was created to further address credibility. The study recruitment, data collection, and data analysis were documented so that the research can be replicated (Hays & Singh, 2012; Roulston, 2010).


In case study research, researchers use key quotes and descriptions from participants to illuminate the case studied (Hancock et al., 2021). As such, we next describe the themes and subthemes identified in study data using participants’ journal and interview quotes to illustrate the findings. Three overarching themes were identified in the data: 1) intersection of supervision experiences and external factors, 2) feelings about social justice, and 3) personal and professional growth. Two subthemes, 3a) increased understanding of privilege and 3b) increased understanding of clients, further expand the third theme.

Intersection of Supervision Experiences and External Factors
     One theme evident across the data was that participants’ experiences in social justice supervision did not occur in isolation from other experiences they encountered as counseling students. Coursework, overall program emphasis, and previous work experiences were external factors that created a compound influence on participants’ counselor development and intersected with their experiences of growth in supervision. Thus, external factors influenced participants’ understanding of and openness to a social justice framework. For example, concurrent with their practicum and supervision experiences, participants completed the course Theory and Practice of Multicultural Counseling. While discussing their experiences in supervision, all participants referenced this course. For example, Casey explained that exposure to social justice in the multicultural counseling course while discussing the topic in supervision made her more open and eager to learning about social justice overall.

Participants’ experiences prior to the counseling program also appeared to intersect with and influence their experiences in social justice supervision. Kallie, for instance, previously worked with African American and Latin American adolescents as a camp counselor at an urban Boys and Girls Club. She explained that social justice captured the essence of viewpoints formed in these experiences, saying, “I really like social justice because it kind of is like the title for the way I was looking at things already.” Casey grew up in California and reported that growing up on the West Coast also exposed her to a mindset parallel to social justice. Esther described that though she was not previously exposed to the term “social justice,” studying U.S., women’s, and African American history in college influenced her pursuit of a counseling career. This influence is evident in Esther’s third journal entry, in which she described noticing issues of power and oppression:

My own attention to an “arbitrarily awarded power” and personal questioning as to what to do with this consciousness has been at the forefront of my mind over the past two years. Ultimately this self-exploration led me to school counseling as a vehicle to advocate and raise consciousness in potentially disenfranchised groups.

This quote highlights how Esther’s previous studies in college may have primed her for the content she was exposed to in social justice supervision.

Feelings About Social Justice
     The second theme was a change in participants’ feelings toward social justice over the course of the semester. Two of the participants expressed that their feelings toward social justice changed from intimidation and fear to comfort and enthusiasm. Initially, Casey explained that social justice supervision created feelings of intimidation. Casey felt fear that the supervisor would instruct her to be an advocate at the practicum site, and that in doing so, Casey would upset others. However, Casey reported that she realized during supervision that social justice advocacy does not necessarily look one specific way. Casey said, “I think a lot of that intimidation went away as I realized that I could have my own style integrated into social justice.” Kallie expressed a similar pattern of emotions, particularly regarding examining clients from a social justice perspective. When asked to explore clients through this lens in supervision, an initial uncomfortable feeling emerged, but over the course of practicum, Kallie reported an attitude change. In the sixth journal entry, Kallie explained that she was focusing on examining all clients through a social justice lens, and “found it to be significantly easier this week than last week.”

Esther also shared evidence of changed emotions during social justice supervision. Initially, Esther reported feeling excited, but later, she was confused as to how counselors could use social justice practically. Despite this confusion, Esther shared that she gained new awareness that social justice advocacy is not only found in individual situations with clients, but also in an overall mindset:

Something I will take from it [supervision] . . . is you incorporate that sort of thinking into your overall [approach]. You don’t necessarily wait for a specific event to happen, but once you know the culture of a place, you have lessons geared towards whatever the problem is there.

Despite these mixed feelings, Esther’s experience aligns with Casey’s and Kallie’s, as all reported experiencing a change in emotions toward social justice over the course of supervision.

Personal and Professional Growth
     Participants also demonstrated changes in professional and personal growth throughout the supervision experiences, the third theme identified. In early journal entries, they reported nervousness, doubt, and insecurity regarding their counseling skills and knowledge. Over time, the tone shifted to increased comfort and confidence. This improvement appeared not only related to overall counseling abilities, but specifically to participants’ understanding of social justice in counseling. For example, in Esther’s second journal entry, she noted the influence that social justice supervision had on the ability to recognize oppression and bring awareness to it at practicum. Esther wrote, “Just having this concept be explicitly laid out in our plan has already caused me to be more attentive to such issues.”

Similarly, professional growth was evident in Kallie’s journal entries over time. In the fourth journal entry, Kallie described discomfort and nervousness when reflecting on clients’ sociopolitical contexts. However, in the ninth journal entry, Kallie described an experience in which she adapted her counseling to be more sensitive to the client’s multicultural background. Casey also highlighted growth with an anecdote about a small group she led. Casey explained that the group was for high-achieving, low-income juniors intending to go to college:

In the very beginning, I remember thinking—this sounds terrible now, but—“It’s kind of unfair to the other students that these kids get special privileges in that they get to meet with us and walk through the college planning process.” ’Cause I was thinking, “Wow, even kids who are high-achieving but are middle-class or upper-class, they could use this information, also. And it’s not really fair that just ’cause they’re lower class, they get their hand held during this.” But, throughout the semester, realizing that that’s not necessarily a bad thing for an institution to give another one a little extra help because they’re gonna have a deficit of help somewhere else in their life, and it really is fair. It’s more fair to give them more help ’cause they likely aren’t going to be getting it at home. . . . So, by having that group, it actually is making a greater degree of equity . . . through supervision and through processing all of that, [I learned] it was actually evening the board out more.

     Participants also expressed that their professional growth in social justice competencies was intertwined with personal growth. Casey reported that supervision increased her comfort when talking about social justice issues and led to the reevaluation of personal opinions. Similarly, Kallie summarized:

I am very thankful that I had that social justice–infused model because it changed the way I think about people. . . . It kinda opened my eyes in a way I had not anticipated practicum opening my eyes. I didn’t expect that—social justice. I didn’t realize how big of an impact it would actually have.

Increased Understanding of Privilege
     Participants reported that understanding their privilege was one area of growth. During practicum, participants considered their areas of privilege and how these aligned or contrasted with those of clients. For example, in Esther’s third journal entry, she noted that interactions with clients made her more aware of personal privileges, which led her to create a list regarding gender identity, socioeconomic background, and sexual orientation. Casey and Kallie further described initially feeling resistant to the idea of White privilege. Casey explained:

I was a little resistant to the idea of White privilege originally, which I’ve since learned is a normal reaction. ’Cause I’ve kind of had the thought of “No! It’s America! All of us pull ourselves up by our bootstraps and everyone has the same opportunity,” which just isn’t the case. And so that definitely had a huge influence on me—realizing that I have huge privileges and powers that I did not, maybe didn’t want to, recognize before.

After initial resistance, participants reported that they transitioned from feeling shame about White privilege to an increased understanding and excitement to use privilege to create change.

Increased Understanding of Clients
     Lastly, participants also reported specific growth in their understanding of the clients whom they counseled. Participants believed they were better able to understand clients’ backgrounds and experiences because of social justice supervision. Kallie described how reflecting on clients’ sociopolitical contexts helped her better understand clients. She noted that the practice became a habit, saying, “It just kinda invaded the way I look at different people and see their backgrounds.” Casey also described an increased understanding of clients by sharing an example of a client who was highly intelligent, low-income, and Mexican American. Casey learned that the client intended to go to trade school to become a mechanic and was not previously exposed to other postsecondary education options like college. Casey described this realization as “a big moment” and said, “My interaction with him, for sure, was influenced by recognizing that there was social injustice there.”


The purpose of this study was to explore counseling students’ experiences in social justice supervision. Findings indicated that participants had meaningful experiences in social justice supervision that impacted them as future counselors. Topics of privilege, oppression, clients’ sociopolitical contexts, and advocacy were reportedly prominent in the participants’ supervision and influenced their experiences.

Despite many calls for social justice supervision in the counseling profession (Baggerly, 2006; Ceballos et al., 2012; Chang et al., 2009; Collins et al., 2015; Glosoff & Durham, 2010; O’Connor, 2005), this is the first known study about supervisees’ experiences with social justice supervision. It represents a new line of inquiry to understand what social justice supervision may be like for supervisees. Findings indicate that participants wrestled with understanding social justice and viewed it as a complex topic. They also suggest that participants found value in making sense of social justice and using it as a tool to better support clients individually and systemically. Similar to research on multicultural supervision, participants indicated that receiving social justice supervision was a positive experience and impacted personal and professional growth (Ancis & Ladany, 2001; Ancis & Marshall, 2010; Chopra, 2013; Inman, 2006; Ladany et al., 2005).

Notably, findings align with some, though not all, of Chang and colleagues’ (2009) delineated tiers in the social justice supervision model. Some of the themes reflect the first tier, self-awareness. For example, participants’ feelings about social justice (Theme 2) and increased understanding of privilege (Theme 3a) highlight how the supervisory experience enhanced their self-awareness as counselors. As their feelings changed and knowledge of privilege grew, their self-awareness improved, a critical task in becoming a social justice–minded counselor (Chang et al., 2009; Dollarhide et al., 2021; Fickling et al., 2019; Glosoff & Durham, 2010). Participants’ increased understanding of clients (Theme 3b) reflects the second tier in Chang and colleagues’ (2009) model, client services. In demonstrating an enhanced understanding of clients and their world experiences, the participants reported thinking beyond themselves and into how power, privilege, and oppression affected those they counseled.

The final tier of the social justice supervision model, community collaboration, was not evident in participant data about their experiences. Despite the supervisor’s intent to address this tier through analyses of school and district policies, as well as community advocacy opportunities, themes about this topic did not manifest in the data. This theme’s absence may suggest that the supervisor’s efforts to address the third tier were not strong enough to impact participants. Alternatively, the absence may suggest that participants were not developmentally prepared to make sense of social justice at a systemic, community level. Instead, their development matched best with social justice ideas at the self and client levels.

Participant findings did align with previous research about supervision. For example, Collins and colleagues (2015) studied master’s-level counseling students and found that their lack of experience in social justice supervision led them to feel unprepared to meet the needs of diverse clients. In this study, the presence of social justice supervision helped participants feel more prepared to support clients, as evidenced in the subtheme of increased understanding of clients. Furthermore, this study reflects similar findings from multicultural supervision research. We found that multicultural supervision was associated with positive outcomes of being prepared to work with diverse clients and engaging in effective supervision (Chopra, 2013; Inman, 2006; Ladany et al., 2005). This pattern is reflected in the current study, as participants reported positive experiences in social justice supervision. Ancis and Ladany (2001) and Ancis and Marshall (2010) found that incorporating multicultural considerations into supervision increases supervisees’ self-awareness and encourages them to engage clients in multicultural discussions. These same results were evident in the present study, with participants reporting personal and professional growth, such as stronger awareness of White privilege and greater willingness to examine clients’ sociopolitical contexts. Findings also reflect general research on supervision, which indicates that supervisees typically experience personal and professional growth in the process (Association for Counselor Education and Supervision, 2011; Watkins et al., 2015; Young et al., 2011).

Furthermore, study findings also align with assertions from supervision scholars regarding the value of social justice supervision. They support Chang and colleagues’ (2009) claim that social justice supervision can increase counselor self-awareness and build an understanding of oppression. Additionally, the findings also reflect Glosoff and Durham’s (2010) assertion that social justice in supervision helps supervisees gain awareness of power differentials. Finally, Ceballos and colleagues (2012) posited that social justice supervision will help counselors develop empathy for clients as counselors conceptualize clients in a systemic perspective. The participants’ enhanced understanding of White privilege and their clients’ contexts supports each of these ideas. Though findings are not generalizable, they appear to confirm scholars’ ideas about social justice supervision and suggest that the approach can be a positive, beneficial experience for counselors-in-training.

     Study findings ought to be considered in light of the study’s limitations. First, although case study research focuses on a single identified case by definition and is not designed for generalization (Hays & Singh, 2012), the case in this study consisted of a demographically homogenous population of only three participants lacking racial, gender, and age diversity. This lack of diversity influenced participants’ experiences and study findings. Second, although the supervisor in this study did not conduct the semistructured interviews with participants in an attempt to prevent bias, participants were aware that Merlin-Knoblich was collaborating on the study, and this knowledge may have influenced their reported experiences. Merlin-Knoblich and Newman also began the study with acknowledged biases toward and against social justice supervision, and although they engaged in reflexivity and dialogue to prevent these biases from interfering with data analysis, there is no way to verify that this positionality did not influence the interpretation of findings. Lastly, our privileged identities served as a potential limitation while studying a topic like social justice supervision. Our racial, educational, class, language, and sexual identity privileges continually blind us to the experiences of oppression that others, including supervisees and clients, face. Seeking to know these perspectives better can increase our understanding of the implications of social injustices in society.

Implications for Counselor Educators and Supervisors
     The positive participant experiences illuminated through this study suggest that supervision based on this model may yield positive experiences for counselors-in-training, such as supporting students in developing self-awareness, understanding of clients’ sociopolitical contexts, and advocacy skills (Chang et al., 2009). Although the supervisor in this study used social justice supervision in individual sessions with participants, counselor educators may choose to apply social justice supervision models to group or triadic supervision. Counseling supervisors in agency, private practice, and school settings may also want to consider using social justice supervision to support counselors and subsequently clients (Baggerly, 2006; Ceballos et al., 2012; O’Connor, 2005). Furthermore, counselor educators teaching doctoral students may want to incorporate social justice supervision models into introductory supervision courses. Including these models into course content may in itself increase student interest in social justice (Swartz et al., 2018).

Regardless of the setting in which supervisors implement social justice supervision, the findings suggest practical implications that supervisors can consider. First, supervisors appear to benefit from considering social justice supervision models in their work (Chang et al., 2009; Dollarhide et al., 2021; Gentile et al., 2009). The findings in this study, plus previous research indicating positive outcomes for multicultural supervision (Chopra, 2013; Inman, 2006; Ladany et al., 2005), suggest that social justice supervision may potentially benefit counseling. Second, supervisors using social justice supervision may encounter supervisee confusion, discomfort, and/or enthusiasm when introduced to social justice supervision. These feelings also may change over the course of the supervisory relationship when learning about social justice. Third, supervisors ought to be mindful of all three tiers of Chang and colleagues’ (2009) social justice supervision model and a supervisee’s developmental match with each tier. As seen in this study, supervisees may be best matched for the first and second tiers of the model (self-awareness and client services), but not the third tier (community collaboration). Supervisors would benefit from assessing a supervisee’s potential for understanding community collaboration before deciding to infuse its focus in supervision.

More research is needed to understand social justice supervision. A variety of future studies, including different models, methods, and settings, would benefit the counseling profession. For example, a study implementing the social justice supervision model proposed by Dollarhide and colleagues (2021) can add to the needed research in this field. Additional qualitative studies with diverse supervisees in different counseling settings would be helpful in understanding if the experiences participants reported encountering in this study are common in social justice supervision. Quantitative studies on social justice supervision interventions would also add to the profession’s knowledge on the value of social justice supervision. Lastly, studies on supervisees’ experiences in social justice supervision compared to other models would highlight benefits and drawbacks of multiple supervision models (Baggerly, 2006; Chang et al., 2009; Glosoff & Durham, 2010).


In this article, we explored master’s-level counseling students’ experiences in social justice supervision via a qualitative case study. Through this exploration, we identified three themes reflecting participants’ experiences in social justice supervision: intersection of supervision experiences and external factors, feelings about social justice, and personal and professional growth, as well as two subthemes: increased understanding of privilege and increased understanding of clients. Findings suggest that social justice supervision may be a beneficial practice for supervisors and counselor educators to consider integrating in their work (Chang et al., 2009; Dollarhide et al., 2021; Gentile et al., 2009; Pester et al., 2020). Further research across contexts and with a range of methodologies is needed to better understand social justice supervision in practice.


Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.



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Appendix A

Semistructured Interview Questions

  1. What brought you to this counseling program?
  2. Overall, how would you describe your practicum experience last semester?
    1. Where did you complete your practicum?
    2. How would you describe the population you worked with at your practicum?
  3. What previous experience, if any, did you have with social justice prior to individual practicum supervision?
  4. During individual practicum supervision on campus last semester, what were some of your initial thoughts and feelings about a social justice–infused supervision model?
  5. In what ways, if any, did those thoughts and feelings about social justice change throughout your
    supervision experience?

These next three questions address three areas of social justice that were incorporated into your individual practicum supervision model: self, students (clients), and institution (school or school districts).

6. Do you think that the emphasis on social justice related to self (i.e., your power, privileges, and experience with oppression) in individual practicum supervision on campus had any influence on you?

    1. If yes, what influence did this emphasis have on you?
    2. If no, why do you think that’s the case?

7. Do you think that the emphasis on social justice related to others (i.e., the sociopolitical context of students, staff, etc.) in individual practicum supervision on campus had any influence on you?

    1. If yes, what influence did this emphasis have on you?
    2. If no, why do you think that’s the case?

8. Do you think that the emphasis on social justice related to institution (i.e., your practicum site, school district) in individual practicum supervision on campus had any influence on you?

    1. If yes, what influence did this emphasis have on you?
    2. If no, why do you think that’s the case?
  1. In what ways, if any, has the social justice emphasis in your individual practicum supervision influenced you as a counselor?
  2. In what ways, if any, has the social justice emphasis in your individual practicum supervision influenced your development as a person?
  3. How would you define social justice?
  4. Is there anything else you would like to add regarding your experience in a social justice–infused model of supervision last semester?
  5. Is there anything else you’d like to share?


Clare Merlin-Knoblich, PhD, NCC, is an associate professor at the University of North Carolina at Charlotte. Jenna L. Taylor, MA, NCC, LPC-A, is a doctoral student at the University of North Texas. Benjamin Newman, PhD, MAC, ACS, LPC, CSAC, CSOTP, is a professional counselor at Artisan Counseling in Newport News, VA. Correspondence may be addressed to Clare Merlin-Knoblich, 9201 University City Blvd., Charlotte, NC 28211, cmerlin1@uncc.edu.


College-Student Personal-Growth and Attributions of Cause

W. P. Anderson Jr., Sandra I. Lopez-Baez

Little is known about levels of personal growth attributed by students to typical college life experiences. This paper documents two studies of student self-reported and posttraumatic growth and compares growth levels across populations. Both studies measure student attributions of cause to academic and non-academic experiences, respectively. It is suggested that future research on the outcome of college life experiences can use a similar approach with a variety of variables.

Keywords: college life, personal growth, posttraumatic growth, life experiences, attributions of cause, outcome of college

The results of a rapidly growing number of studies document high levels of adversarial growth attributed by survivors in retrospect to coping with negative life experiences (Linley & Joseph, 2004) including war, bereavement, or loss of a child. The Posttraumatic Growth Inventory (PTGI) by Tedeschi & Calhoun (1996) has been the most popular measure of adversarial growth to date (Joseph, Linley, & Harris, 2005; Linley & Joseph, 2004; Tedeschi & Calhoun, 2004). In this context, adversarial refers to the negative nature of an experience of interest to researchers. Growth refers to personal growth defined as the positive psychological changes described by assessment-instrument items, changes such as the building of interpersonal relationships, a greater appreciation of the value of life, and a realization of new possibilities in life (examples after Tedeschi & Calhoun, 1996). Thus, adversarial growth is personal growth attributed by participants to a naturalistic negative event of particular interest to researchers. We stumbled on the PTGI and the relationship between adversarial and personal growth while searching for an instrument for measuring personal growth attributed in retrospect to the sum of naturalistic experiences (both positive and negative) occurring during a time period of interest to researchers.

The growth of immediate interest to us is not adversarial, but rather the total personal growth of college students because our long-term goal is to determine how educators can best facilitate growth, whether adversarial or otherwise. Developmental theorists (Chickering, 1969; Chickering & Reiser, 1973; Pascarella & Terenzine, 1991; Perry, 1970) have suggested that students grow in response to the combination of typical experiences of college life. Although researchers have used a variety of instruments to measure both academic and personal growth (for recent examples see Hassan, 2008; Higgins, Lauzon, Yew, Brasweth, & Morley, 2009), little is known about how college-student growth compares to adversarial growth because comparisons require measures obtained with the same instrument. Therefore, we want to measure college-student growth with an instrument that has been used to measure adversarial growth. Tedeschi and Calhoun (2004) implied that the PTGI might be suitable for our purposes by characterizing its items as capturing the core of personal growth (without distinguishing between adversarial and personal growth). We decided to use the PTGI after noting that the developers themselves conducted the first study of non-adversarial growth based on the PTGI by using it to measure levels of personal growth described by college students in a small (n = 32) non-trauma comparison group (see study 3 in Tedeschi & Calhoun, 1996).

We conducted the second and third studies of interest (N = 347, N = 117) by using the PTGI to measure levels of college student personal growth, whether adversarial or otherwise attributed to a single semester of college life (Anderson & Lopez-Baez, 2008, 2011). All three previous studies documented mean levels of student personal growth near the midpoint of the range reported for posttraumatic studies (range 46.00–83.47 for 14 studies summarized by Linley & Joseph, 2004). In our second study, we elicited brief explanations from students to learn how they accounted for their growth (see Anderson & Lopez-Baez, 2011). Explanations were in the form of percentage attributions of total personal growth to student-identified naturalistic experiences and to a researcher-identified 3-hour course designed to facilitate growth (student explanations like: college internship accounted for 30% of my total personal growth, finding a job for 40%, 3-hour course of interest to researchers for 15%, miscellaneous other experiences for the remaining 15%). We refer to these explanations as attributions of cause. Student attributions of cause to academic experiences supported the appealing idea (to counselors and educators) that personal growth as defined and measured by the PTGI can be intentionally facilitated by activities designed to do so.

The two studies described in the current paper are the fourth and fifth based on the PTGI to measure college-student personal growth that was not strictly adversarial. Both samples are generally similar to those of our previous studies (Anderson & Lopez-Baez, 2008, 2011). The current studies extend the results of our previous ones by (1) measuring the growth attributed by sample members to substantially longer periods of college life and (2) eliciting separate attributions of cause for academic and non-academic experiences, respectively. The primary purpose of Study 1 was to examine the internal validity of student data collected with the PTGI by comparing the descriptive statistics among the results of Study 1 with those of the three previous studies described above (Anderson & Lopez-Baez, 2008, 2011; Tedeschi & Calhoun, 1996). The primary purpose of Study 2 was to measure graduating college-senior attributions of annual personal growth, in retrospect to their freshman, sophomore, junior, and senior years, respectively. Study 2 was descriptive.

Study 1: Cumulative Growth

Research questions 1 and 2 are designed to collect descriptive data. Research question 3 is designed to collect information about the internal validity of our data by testing hypotheses on the basis of comparisons of the results of Study 1 with the results of previous studies (Anderson & Lopez-Baez, 2008, 2011).
Research question 1. What levels of cumulative personal growth do participants describe for their college undergraduate years (descriptive statistics for PTGI scores)?
Research question 2. What cumulative percentage attributions of cause do participants describe for academic experiences (college credit) and non-academic (all other) experiences, respectively, during their college undergraduate years (descriptive statistics for attributions of cause)?
Research question 3. To what extent do the results of comparable studies reflect internal validity (comparisons of statistical results across studies)?


We recruited participants from among the 147 students in a 3-hour elective course, Problems of Personal Adjustment, taught by the first author at a southeastern university. The course covered topics of psychological adjustment and included activities to facilitate student personal growth. Data was collected at the end of the fall semester of 2007. Most students were third- and fourth-year undergraduates in the College of Arts and Sciences or the School of Commerce. A total of 137 students elected to participate (response rate 93.20%). After 15 questionnaires with missing data were eliminated, sample size was 122 (67 men, 55 women). Most participants were Caucasian (4 African Americans, 4 Hispanics, Asians, and Asian Americans) with a mean age of 21.05 (.79) years and a mean college career of 6.54 (1.34) completed semesters. Students were given extra credit for electing to participate.


Posttraumatic Growth Inventory. Each of the 21 PTGI items (Cronbach alpha = .90, test-retest r = .71) describes a single positive psychological change (Tedeschi & Calhoun, 1996). Examples (with corresponding subscale and number of subscale items) include: “A sense of closeness with others” (Relating to Others, 7 items), “I developed new interests” (New Possibilities, 5 items), “A feeling of self-reliance” (Personal Strength, 4 items), “A better understanding of spiritual matters” (Spiritual Change, 2 items), and “My priorities about what is important in life” (Appreciation of Life, 3 items). Participants of trauma studies are instructed to describe the degree of each change resulting from their trauma. Responses are positions on a 6-point Likert-type scale anchored by 0 (no change) and 5 (great change). Total score range is 0 to 105 (per-item basis 0 to 5.00). Tedeschi and Calhoun (1996) reported evidence for concurrent and discriminant validity (their second study 1996) and construct validity (their third study). We have previously reported mean levels of college-student growth of 59.07 (SD = 15.77, N = 347; Anderson & Lopez-Baez, 2008) and 60.42 (SD = 16.61, N = 117; Anderson & Lopez-Baez, 2011).

Blank table. We use a blank, 2-column table to elicit attributions of cause (see Appendix A). In column 1, participants list life experiences thought to have contributed most to their growth during their college years. Participants list estimates of corresponding percentage contributions to total growth in column 2. For purposes of this study, we tailored the table to provide subtotals of attributions of cause to academic experiences (lines 1–5) and non-academic experiences (lines 6–10). We pre-labeled Lines 4, 5, and 10. Line 4 refers to the course taught by the principal author from which participants were recruited.


Like subjects in our two previous studies, participants were instructed to complete a preliminary exercise to stimulate thinking about personal growth by answering questions that distinguished between level and salience of growth. We do not report the results because the research questions of the current study do not involve salience.

After completing the preliminary exercise, participants were given the following instructions for completing the PTGI (after study 3, Tedeschi & Calhoun, 1996):
Consider the degree to which each change listed below [21 items] has occurred in your life during your years as an undergraduate, whether or not the change was directly related to university class work. For each change, select the best response from the following scale [6 Likert-type options of Tedeschi & Calhoun, 1996] and write the number in the space provided.

Participants provided attributions of cause by completing the form in Appendix A. Participants were instructed to complete lines 1–10 of column 1 (brief descriptions of experiences thought to have contributed most to total growth) and column 2 (corresponding percentage contributions). Participants also provided 1-line qualitative explanations of how each experience on lines 1–10 of column 1 contributed to their growth (entries not analyzed because not required by research questions).


Cronbach alphas for each subscale calculated from the data of Study 1 are: .87 (all), .82 (Relating to Others), .65 (New Possibilities), .56 (Personal Strength), .82 (Spiritual Change), and .54 (Appreciation of Life). The mean female PTGI score of 71.71 (11.72) is greater than the mean male score of 66.94 (10.98), t (120) = 2.32, p = .022 < α = .05 (2-tail), d = .42. Male and female scores were combined because no significant gender differences were found by t-tests of corresponding subscale means (Bonferroni-corrected α = .01).

Research Question 1

Table 1 contains descriptive statistics for participant PTGI scores on a total and per-item basis. Magnitudes of Cronbach alphas reported above can be seen to reflect the variations in magnitude among the standard deviations reported for per-item subscale scores in Table 1 (larger Cronbach alphas are associated with larger standard deviations). This observation suggests that restricted ranges among our data for Personal Strength and Appreciation of Life account for the low values of alpha for each subscale.

The mean total of 69.09 (SD = 11.52) is greater than the midpoint of 52.50 for the maximum range of 0–105 and greater than the midpoint of 64.74 for the range of 46.00–83.47 reported for trauma studies (Linley & Joseph, 2004). The per-item mean of 3.29 (SD = .55) exceeds 3 or “moderate growth” on the 0 to 5 response scale (scale midpoint = 2.50). The per-item means for four of the five subscales are between 3.30 (SD = .77) and 3.65 (SD = .62), inclusive. The per-item mean for Spiritual Change is lower, 1.68 (SD = 1.17).

Research Question 2

Table 2 contains descriptive statistics for participant percentage attributions of cause to academic and non-academic experiences, respectively (from column 2 of Appendix A). Both subtotals are large although the non-academic subtotal is larger by a ratio of approximately 3:2. The three academic experiences thought by each participant to have contributed most to his or her growth account for 30.76% (14.79 + 8.03 + 7.94%) of the mean subtotal of 39.63% (SD = 11.94) of total personal growth. Illustrative examples include internships, terms abroad, and three-credit semester courses. Participants attribute the third largest contribution (7.94%) to a course designed to facilitate growth. Three non-academic experiences account for 50.31% (25.96 + 15.18 + 9.17%) of the mean subtotal 60.36% (SD = 11.94). Examples include illnesses, extracurricular activities, deaths of family members, and positive and negative changes in significant relationships.

Research Question 3

Numerical PTGI scores and percentage attributions of cause are subjective assessments by participants, as are most self-reports. Our research purposes require data with a degree of internal validity (veridicality or truthfulness; that is, correspondence between self-reports and actual subjective impressions). Internal validity can be assessed by comparing results of analyses across samples of multiple studies. For purposes of the following comparisons, we will use words to number our previous studies and a numeral to designate Study 1 of the current paper. Thus, our sample one (Anderson & Lopez-Baez, 2008) was a group of 347 students who described total growth in 2005 and 2006 for their preceding semester, M = 59.07 (SD = 15.77). Our sample two (Anderson & Lopez-Baez, 2011) was a group of 117 students who described total growth and attributions of cause in May of 2007 for their preceding semester, M= 60.42 (SD = 16.61). Our sample 1 is that of the current study. Samples are characterized by similar demographic characteristics.

Comparison one. Members of samples one and two described total growth for a single semester. Therefore, we expected samples one and two to have similar mean PTGI scores (null 1: unequal mean PTGI scores). A visual inspection finds similar means. Null 1 is rejected on the basis of that visual inspection, Cohen’s d of .08 (minimal effect size), and 95% C. I. of – 4.71 to 2.01 that includes 0.00 (SE difference = 1.71). As expected, mean total scores of samples one and two are similar.

Comparison two. Members of sample 1 described total growth over more semesters than did members of samples one or two. Therefore, we expected our sample 1 mean PTGI score to be higher than the mean of either sample one or sample two (nulls 2 and 3: sample 1 mean less than or equal to means of samples one and two, respectively). Nulls 2 and 3 are rejected on the basis of a 1-way ANOVA F (2, 593) = 20.02, p = .000, η2 = .064 with an independent variable of 3 groups and dependent variable of student PTGI scores; and Bonferroni post-hoc t-tests of student PTGI scores: sample one and sample 1, t (467) = 6.44, p = 0.000 < α = .0167 (2-tail), d = .73; sample two and sample 1, t (237) = 4.70, p = 0.000 < α = .0167 (2-tail), d = .61; sample one and sample two, t (462) = .79, p = .430 > α = .0167 (2-tail), d = .08. As expected, the sample 1 mean of 69.09 is greater than the means of both sample one and sample two.

Comparison three. Visual inspection of the subscale per-item mean scores of sample one (Anderson & Lopez-Baez, 2008) and sample two (Anderson & Lopez-Baez, 2011) found that four of the per-item means in each sample are approximately equal and that all four are greater than the corresponding mean Spiritual Change score by 1.50 to 2.00 scale divisions. Therefore, we expected the subscale scores of our sample 1 to exhibit the same pattern. Visual inspection (Table 1) finds that they do. Thus, as expected, the five subscale per-item mean scores of each of our three samples are characterized by four comparatively high and approximately equal subscale mean scores and a comparatively low mean Spiritual Change score.

Comparison four. Members of all three samples completed the same 3-credit course (during different semesters) designed to facilitate personal growth. Members of sample two and sample 1 were asked for attributions of cause for the course (Anderson & Lopez-Baez, 2011). The resulting attributions of cause to a course designed to facilitate growth are themselves evidence of internal validity. We expected members of sample two to attribute a larger percentage of total growth for one semester to the class than members of sample 1 attributed for the longer period of several semesters (directional null 4: sample two mean attributions of cause to course less than or equal to corresponding mean of sample 1). Null 4 is rejected on the basis of an independent t (237) = 12.18, p = .000 < α = .05 (1-tail), d = 1.56. As expected, the sample two mean attribution of cause to the course of 25.28 (14.28)% is greater than the corresponding sample 1 mean attribution of 7.94 (6.46)%.

Comparison five. Concurrent validity is the extent to which different measures of the same construct agree. We formulated Comparison five only after we saw an opportunity to investigate concurrent validity among the data of sample 1 by measuring the correspondence between high and low Spiritual Change scores in sample 1 and the presence or absence of corresponding written attributions of cause, respectively (null 5: no agreement). We defined high participant Spiritual Change scores as per-item mean scores of at least 3.50 scale divisions (at least 1 division above scale midpoint 2.50). Sample 1 contains 11 high scorers (2 students scored 5.00, 1 scored 4.50, 3 scored 4.00, 5 scored 3.50). We defined low Spiritual Change scores as per-item mean scores no larger than 1.50 scale divisions. Sample 1 contains 64 low scorers (14 students scored .00, 17 scored .50, 20 scored 1.00, and 13 scored 1.50). We added 19 questionnaires selected at random from those of the 64 low scorers to provide a sample of 30 questionnaires for analysis.

We examined each sample member’s written attributions of cause for evidence of spiritual growth. We defined evidence of spiritual growth narrowly by the wording of the Spiritual Change items (PTGI items 5 and 18). Thus, we defined evidence of spiritual growth as student descriptions of one or more attributions of cause with at least one explicit reference to religion or spirituality including references to worship, God (or other deity or deities), prayer, or religious writings; but not references to ethics, morality, or meaning of life in the absence of the required explicit references. (Example attributions interpreted as evidence include: my faith deepened, came to accept God’s will, learned more about Bible; but not: grew in commitment to boyfriend, learned importance of moral behavior, decided on career choice, or obtained new understanding of life). We easily reached consensus on all identifications because of the simple and specific definition of evidence adopted beforehand. Attributions by 6 of the 11 high scorers and 2 of the 19 low scores contained at least one reference to spiritual growth. Half of the 8 attributions with references to spiritual growth referred to religious studies classes.

High and low Spiritual Change scores corresponded to the presence or absence, respectively, of attributions of cause for 24 of 30 sample 1 members, percentage agreement = 80.00%. Spearman r is a measure of agreement between two series of nominal data that does not take into account the probability of chance matches. The probability of chance matches is high among data of interest because the sample is characterized by many low scores and many non-positive attributions of cause. Cohen’s κ (1960) is a measure of agreement between two series of nominal data that accounts for the probabilities of chance matches. Cohen’s κ was originally developed to assess inter-rater agreement and is widely used for that purpose. We used it instead to measure agreement after accounting for chance between two series of nominal data rated jointly by consensus. Kappa values have a range 0 to 1 and are interpreted like positive correlation coefficients. Null 5 is rejected on the basis of a Spearman r = .51, p = .005 (1-tail) and κ = .44, approximate SE = .19, approximate p = .013.


This study is based on a mixed-methods design with two measures for data collection. The first is the PTGI, a self-report instrument developed from standard psychometric techniques. Posttraumatic researchers use the PTGI to measure personal growth attributed to a trauma of interest to researchers. We use the PTGI as described by the instrument developers (Tedeschi & Calhoun, 1996) to measure total personal growth attributed to the sum of experiences during a time period of interest. Our second measure is an open table for obtaining participant attributions of cause for total personal growth. Participants complete the table with written descriptions of personal experiences and estimates of corresponding percentage contributions. The table design is adapted from one introduced in a previous study (Anderson & Lopez-Baez, 2011).

The purposes of the current study require a degree of veridicality (truthfulness), a form of internal validity. Researchers have reported little evidence of any kind for the validity of subscale scores beyond the results of exploratory factor analysis (see review in Anderson & Lopez-Baez, 2008). This is perhaps the reason why researchers have drawn few conclusions about growth from subscale scores in their results. Our comparisons one to three in the current study demonstrate the existence of predicted relationships among total PTGI scores of three samples and therefore a degree of internal validity for the collection of individual items and total PTGI scores. Comparison four demonstrates the existence of a predicted relationship between the attributions of cause from two studies and therefore a degree of internal validity for data obtained with the table-based approach. Comparison five demonstrates a degree of concurrent validity for both the subscale of Spiritual Change and the table-based approach.

Posttraumatic Growth Inventory as a Measure of Personal Growth
The PTGI was developed as a measure of posttraumatic growth. Tedeschi and Calhoun (2004) described their instrument as reflecting the core of personal growth. The results of the current study offer strong support for Tedeschi and Calhoun’s description because the results report high levels of personal growth for students without regard to prior experiences of trauma. We believe that if the PTGI can be used to measure the personal growth of samples of populations as dissimilar as college students and trauma survivors, then the PTGI can probably be used as a measure of personal growth under other circumstances in future studies.

College-Student Growth and Posttraumatic Growth
Magnitude. Theorists have identified the college-student undergraduate years as a time of personal growth in response to both academic and non-academic experiences (Chickering, 1969; Chickering & Reisser, 1993; Perry, 1970) in terms that suggest the definition of personal growth embodied in the items of the PTGI. Researchers have generally confirmed theorist predictions of student growth (c.f., Hassan, 2008; Higgins et al., 2009), but not with measures that allow for comparison of personal growth in response to trauma. Therefore, we are not surprised that the results of the current study reflect student growth. We are surprised, however, by the magnitude of that growth, m = 69.09 (SD = 11.52) because it is so near the maximum of the range reported in previous studies of posttraumatic growth (Linley & Joseph, 2004). We believe that this comparison helps readers appreciate the magnitude of growth described by each population.

Factor structure and subscales. The developers of the PTGI reported a 5-factor structure for the items of their instrument on the basis of an exploratory factor analysis (Tedeschi & Calhoun, 1996) and developed five corresponding subscales of unequal length. Subsequent posttraumatic studies have reported 2- and 3-factor structures (see review in Anderson & Lopez-Baez, 2008). Taken together, we interpret these EFA results as evidence that the factor structure of posttraumatic growth is not highly differentiated or stable across different samples. The results of several EFA described in our first study (Anderson & Lopez-Baez, 2008) demonstrated that the factor structure of a sample of student scores also lacked differentiation and stability, and therefore resembled that of posttraumatic growth.

Posttraumatic researchers have typically reported descriptive statistics for the total PTGI score and for the original five subscales. We report our results this way in Table 1, but also include recalculations on a per-item average basis (total score and subscale scores divided by number of corresponding items). The per-item format allows for comparisons of scores for subscales of unequal length. The reader can verify by inspection of Table 1 that all of our subscale scores round to 3.50 (to nearest .5 scale units) except the score for Spiritual Change, which rounds to 1.50. We have observed a similar pattern among the results of our previous studies (Anderson & Lopez-Baez, 2008, 2011).

During preparation of this manuscript, we conducted an informal visual comparison (not based on comparative statistics) of intra-sample per-item subscale scores listed in the results of posttraumatic studies cited by Linley and Joseph (2004) and observed that most subscale scores in each sample were of almost equal magnitude. Most of the few exceptions were low subscale scores (greater than 1.00 per-item average scale value) for Spiritual Change. We do not interpret our observation of this pattern as empirical evidence of anything; however, the observation makes us wonder about the empirical relationship between Spiritual Change and the other four subscales and highlights the importance of assessing the internal validity of Spiritual Change to lay the groundwork for any future studies of the internal structure of growth.

Spiritual Change and spiritual growth. Tedeschi and Calhoun (2004) described the PTGI subscales as measures of five domains of personal growth. The results of our comparison three (see results section) reflect a pattern among mean subscale scores in which the per-item mean score for the Spiritual Change subscale is relatively low. This pattern is empirical evidence that growth does not occur uniformly across all domains at the same time, at least for spiritual growth as measured by Spiritual Change. The occurrence of the similar pattern we observed in the samples of many studies invites the following attempt to explain the pattern. An explanation might be especially important to educators and administrators of religious colleges and universities who actively seek to promote spiritual growth of students.

Developmental factors are probably at least partly responsible for the larger Spiritual Change scores in posttraumatic studies. Trauma study participants have generally been older than participants in samples of college students. Perhaps older people like those in many of the trauma-study samples are more likely than college undergraduates to describe spiritual growth. Perhaps spiritual growth is more characteristic of growth in response to trauma than of growth in response to college life. However, these two possibilities do not completely account for the pattern of interest (similar per-item subscale scores for four subscales and lower subscale score for Spiritual Change) because the pattern is not as pronounced among the five per-item subscale means reported by Tedeschi and Calhoun (1996) for their non-trauma comparison group of college undergraduates. The results of our comparison five (see results section) suggest another developmental factor. Comparison five is based on the narrow definition of spiritual growth embodied in the 2-item Spiritual Change scale. Participant interpretations of item content necessarily influence patterns among subscale scores. In particular, differences in religious background could contribute to different interpretations of Spiritual Change items. We recruited our sample members 12 years after Tedeschi and Calhoun recruited theirs, and we recruited ours from a different university in a different part of the United States. Perhaps our sample members have different religious backgrounds than the members of Tedeschi and Calhoun’s sample.

Intentional facilitation of growth. Personal growth can occur in response to very different kinds of experiences, from coping with horrible trauma to caring deeply for friends and significant others. Most of these experiences, certainly most of the traumatic ones, seem to arise spontaneously. This conclusion is important to philosophers and developmental theorists because it suggests that personal growth is central to the human condition. Developmental theorists have suggested that personal growth also can occur in response to planned academic activities. This prediction is important to educators and others concerned with how to facilitate growth.

Students in the current study sample attributed 40% of their personal growth to academic activities (see Table 1). We interpret these results as strong support for the prediction of personal growth in response to academic activities. Students in the current study and in the sample of our second study (Anderson & Lopez-Baez, 2011) also attributed substantial growth to a single 3-hour course designed to facilitate personal growth. We interpret these results as support for the prediction that substantial levels of personal growth can be facilitated by specific academic activities designed to do so.

Current study results include attributions of cause for a single academic course designed in part to facilitate growth. The course is generally similar to that described by Hassan (2008) in a study of growth attributed to a health education course. The percentage attributions of cause to the course described in the current study (Table 2) are consistent with percentages reported in our previous study (Anderson & Lopez-Baez, 2011). The results of that preceding study and the study of Hassan (2008) strongly suggest that personal growth can be intentionally facilitated if not taught explicitly. The results of the current study suggest that substantial growth is attributed by students to coursework in general.

Current study results include subtotals of attributions of cause for academic and non-academic experiences, respectively. Sample members report attributions of almost 40% of total personal growth to academic coursework and provide further evidence that personal growth can be intentionally facilitated.

Study 2: Annual Growth

Taken as a whole, the results of Study 1 and three previous studies (Tedeschi & Calhoun, 1996; Anderson & Lopez-Baez, 2008, 2011) suggest that college students like those in the samples attribute substantial personal growth to both academic and non-academic experiences of their college years. These conclusions lead us to wonder just how much growth graduating seniors attribute to each college year. We believe the answer is of interest to college educators and administrators charged with fostering student growth. Answering this question requires a descriptive study of a representative sample drawn from a population of interest. Study 2 uses a simple descriptive design to answer the question for the population of students similar to those in the sample of Study 1.

Research Question

What levels of personal growth do members of a sample of college seniors attribute to each year of their 4-year college careers and what percentages do they attribute to academic and non-academic experiences, respectively?


Participants were recruited from graduating college seniors enrolled during the spring semesters of 2009 and 2010 in the course described in Study 1. Students earned extra course credit for participating. A total of 117 participants were recruited (70 of the 84 graduating seniors among the 142 students enrolled in the spring of 2009 and 47 of the 67 graduating seniors among the 144 students enrolled in the spring of 2010). A total of 108 participants (59 women and 49 men) remained after eliminating 9 questionnaires with missing data. Most participants were Caucasian (5 African American, 9 Hispanics, Asians, Asian Americans and other). Mean age was 21.64 (SD = .48) years. Academic concentrations (and number of students) were: college of arts and sciences (77), commerce school (18), college of engineering (12) and school of architecture (1). Participants completed the survey at the end of the last class meeting, approximately three weeks before graduation.

We used the PTGI to measure total personal growth. We used the table in Appendix B to elicit attributions of cause. Participants completed the table with annual estimates (in percent) of their personal growth, growth from academic experiences, and growth from non-academic experiences. We also asked participants to identify on a separate page (not shown in Appendix B) the experiences that they thought contributed most to their growth each year and to identify these experiences as either academic or non-academic.


The first two pages of the Study 1 and Study 2 questionnaires (warm-up exercise and PTGI) were identical. Study 2 participants followed the instructions in Appendix B to provide attributions of cause.


Subscale Cronbach alphas for our data are .85 (Relating to Others), .57 (New Possibilities), .59 (Personal Strength), .88 (Spiritual Change), and .77 (Appreciation of Life). The male mean PTGI score of 65.71 (SD = 12.27) is less than the female mean of 70.02 (SD = 11.50), but not significantly less, t (106) = 1.93, p = .056, α = .05 (2-tail). A single significant gender difference was found among subscale scores between the male mean RTO score, M = 21.27 (SD = 6.05) and the corresponding female mean, M = 24.46 (SD = 5.51), t (106) = 2.87, p = .005, α = .01 (Bonferroni-corrected 2-tail). Subsequent analyses of PTGI scores in the sample of the current study are based on the combined scores shown in Table 3.

No gender differences were suggested by the results of independent t-tests of corresponding male and female percentages of total, academic, or non-academic growth, respectively, for any college year, α = .0125 (Bonferroni-corrected 2-tail for each set of 4 analyses). Therefore, subsequent analyses of percentages of growth are based on the combined scores in Table 4.

Table 3 contains the descriptive statistics for the sample of Study 2. A visual comparison of the contents of Table 3 with those of Table 1 shows that all corresponding entries are approximately equal. Table 4 documents mean attributions of substantial levels of personal growth to both academic and non-academic experiences for each of four years. The mean attribution to non-academic experiences exceeds the mean attribution to academic experiences for every year. The greatest mean growth is attributed to the senior year; the least is attributed to the sophomore year.

Participants were asked to identify the year of occurrence of the single experience (as defined by participants) to which they attributed the most personal growth (question not shown in Appendix B). Year and corresponding frequencies of selection by participants are: freshman (22), sophomore (9), junior (35), and senior (42). Participants also were asked to identify whether the experience that contributed most to growth was academic or non-academic. Type of experience and frequencies of selection are: academic (27) and non-academic (81). Finally, participants were asked to describe (in words) the experience that contributed most to their growth. Illustrative descriptions include: “getting a job,” “finding a new girlfriend,” “death of a friend,” “learning to live with fraternity brothers,” and “finishing my undergraduate thesis.”


The current study is the fourth we have conducted based on the PTGI for measuring student personal growth and the third based on a table for collecting attributions of cause in the form of quantitative percentages and qualitative descriptions. We believe the results of the four studies support the utility of both measures for measuring growth and the flexibility of both for measuring growth under different circumstances.

College-Student Personal Growth
The mean PTGI score and standard deviation described by the 108 college seniors with a college career of exactly 8 semesters in the sample of Study 2, M = 68.01 (SD = 12.01) are almost identical to the corresponding statistics described by the 122 college students with a mean college career of 6.54 (SD = 1.34) semesters in the sample of Study 1, M = 69.09 (SD = 11.52). We had expected the mean PTGI score of Sample 2 to exceed that of Sample 1 because the results of our previous studies reflected higher mean scores for more college semesters (Anderson & Lopez-Baez, 2008, 2011; Study 1 of this paper). The simplest way to explain the similarity between the total scores of Study 1 and Study 2 is to remind ourselves that we are comparing data from a cohort as opposed to longitudinal studies, and remind ourselves that college student growth varies widely among students in each sample as illustrated by the large standard deviations for total PTGI scores in each of our studies. The results reflected in Table 4 suggest that the college seniors in our sample attributed substantial growth to both academic and non-academic experiences during all four college years. We believe this pattern is evidence that college faculty and staff can influence the personal growth of many students during every year of a student cohort’s progression toward graduation.
General Discussion

We believe Study 1 provides substantial information about the validity of total PTGI scores and also of subscale scores for Spiritual Change. For this reason, we believe the results of Study 1 are of interest to researchers using the PTGI to measure adversarial or personal growth. The high levels of personal growth attributed by students to the sum of their college years and attributions of cause to academic activities will probably interest college administrators and educators.

The same results led us to wonder how student growth and attributions of cause might be distributed over each college year. For this reason, Study 2 seemed a natural extension of Study 1. The descriptive results of Study 2 are among the first to reflect levels of growth attributed by graduating seniors in retrospect to each year of their undergraduate careers. We believe these results will also interest college personnel concerned with facilitating student growth.

We developed the table-based approach used in Studies 1 and 2 to measure attributions of cause. Researchers can adapt the approach for use in future studies of personal growth. We believe that researchers with other research interests can use a similar approach to study a wide variety of other variables.


The two studies described in the current paper are based on self-reports. Thus, the results of both are subject to the many potential validity threats associated with self-reports including additional threats to the historical validity of retrospective self-reports. Our research purposes require data with sufficient internal validity. Comparisons of the results of our three studies reflect a degree of internal validity as described in Study 1. However, PTGI scores might be associated with ceiling effects if higher levels of growth are attributed to longer time periods at diminishing rates, and percentage attributions might be associated with recency effects if more vivid recall of recent experiences leads to larger attributions of growth. Finally, because our samples are representative of populations of similar students, but not university students in general, our conclusions do not necessarily apply beyond the population represented by our samples.

Future Research

We plan more studies of college student growth with the long-term goal of learning how best to facilitate growth. We hope that readers can adapt our approach to measuring attributions of cause for use in future studies of personal growth and other variables.


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W. P. Anderson, Jr., is an Adjunct Professor in the Counselor Education Department and Sandra I. Lopez-Baez, NCC, is an Associate Professor and Chair of Counselor Education Programs, both at the University of Virginia. Correspondence can be addressed to W. P. Anderson, Jr., University of Virginia, Counselor Education Department, Box 400270, Charlottesville, VA 22904, wpa@alumni.virginia.edu.

Appendix A

Specific Experiences that Contributed to your Personal Growth

Consider the experiences (both academic and personal) that contributed most to your Personal Growth during your undergraduate years, whether or not the experiences were directly related to work for which you earned academic credit. Name and briefly describe the 3 academic experiences (besides PPA) that contributed most to your Personal Growth on lines 1–3 of Column 1 in Table 1 below. For purposes of this study, academic experiences are defined as those for which you earned academic credit. Note that PPA (including your project) is listed on line 4 for comparison purposes. Next, on lines 6–10, name and briefly describe the 4 non-experiences that contributed most to your Personal Growth.

Column 1 Column 2
Specific experiences Contribution (%)

Academic credit experiences that contributed most to your Personal Growth during your undergraduate years (i.e., classes, internships, practicums, etc.)

1. ________________________________________________ ______ %
2. ________________________________________________ ______ %
3. ________________________________________________ ______ %
4. PPA (including project) %
5. Misc. other academic experiences %

Subtotal: Personal Growth from academic experiences ______ %

Non-academic experiences (if any) that contributed most to Personal Growth during your undergraduate years (i.e., friends, relationships, gains, losses, etc.)

6. ________________________________________________ ______ %
7. ________________________________________________ ______ %
8. ________________________________________________ ______ %
9. ________________________________________________ ______ %
10. Misc. other non-academic experiences %

Subtotal: Personal Growth from academic experiences _____ %


Appendix B

History of Personal Growth during your College Undergraduate Years

Step I. Recall the beginning and ending dates of each of your undergraduate years including the current one. Fill in
dates in the blanks of the line below to describe each year.

Freshman year Sophomore Year Junior Year Senior Year

Dates __/__ – __/__ __/__ – __/__ __/__ – __/__ __/__ – __/__
Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr Mo Yr

Step II. Consider the total Personal Growth you have experienced during your undergraduate years. Estimate the
percent of that total experienced during each year including the current year and place your estimate in the
corresponding blank of the following line. Make sure that the total of your entries on the following line =

% of total ______ % + ______ % + ______ % + _____ % = 100 %
1a 2a 3a 4a

Step III. For each year above consider what percent of your Personal Growth that year resulted from academic
experiences (experiences for which you earned academic credit) and how much from non-academic (all other)
experiences. Fill in the corresponding percentages in the two lines below. Make sure that your academic and
non-academic growth for each year (column entries) equal the entry for the corresponding year in Step II
(above). That is, make sure: 1b + 1c = 1a, 2b + 2c = 2a, . . . .

Growth in response to academic experiences

______% ______% ______% ______%
1b 2b 3b 4b

Growth in response to non-academic experiences

______% ______% ______% ______%
1c 2c 3c 4c