Nov 12, 2018 | Volume 8 - Issue 4
Jeffrey M. Warren, Leslie A. Locklear, Nicholas A. Watson
This study explored the relationships between parenting beliefs, authoritative parenting style, and student achievement. Data were gathered from 49 parents who had school-aged children enrolled in grades K–12 regarding the manner in which they parent and their child’s school performance. Pearson product-moment correlation coefficients and multiple regression modeling were used to analyze the data. Findings suggested that parent involvement, suspension, and homework completion significantly accounted for the variance explained in grade point average. Authoritativeness was positively and significantly related to both rational and irrational parenting beliefs. Irrational parenting beliefs were positively and significantly related to homework completion. School counselors are encouraged to consider the impact of parenting on student success when developing comprehensive programming.
Keywords: student achievement, homework completion, irrational parenting beliefs, authoritative parenting, school counseling
There are many indicators of success as students matriculate through elementary, middle, and high school. Student success is generally defined by the degree to which students meet or exceed a predetermined set of competencies (York, Gibson, & Rankin, 2015). These competencies are often academic in nature and align with state curriculum. Data collected at numerous points (i.e., formal and informal assessment) throughout an academic year are used to monitor student performance. Student achievement data, including end-of-grade tests and grade point average (GPA), are key determinants of student outcomes such as promotion or retention (Schwerdt, West, & Winters, 2017). Although both are distal data points that measure achievement, GPA is a cumulative measure of student performance based on mental ability, motivation, and personality demonstrated throughout the course of a school year (Imose & Barber, 2015; Spengler, Brunner, Martin, & Lüdtke, 2016).
Numerous factors are related to and impact student achievement. According to Hatch (2014), these factors include discipline referrals, suspension, homework completion, and parental involvement. Research suggests that these factors are good indicators of distal or long-term academic success (Kalenkoski & Pabilonia, 2017; LeFevre & Shaw, 2012; Noltemeyer, Ward, & Mcloughlin, 2015; Roby, 2004). Although it is a challenge to determine student progress based on GPA alone, these variables can be monitored across the school year for a real-time snapshot of student success (Hatch, 2014).
The American School Counselor Association (ASCA; 2012) has suggested that school counselors work to promote student success by operating across three distinct areas or domains: academic, social and emotional, and career development. As such, school counselors play an integral role in developing, delivering, and evaluating programs that promote academic achievement. School counselors are challenged to determine the direct impact of services on student achievement. Student achievement–related data can be measured to understand the impact of school counseling interventions. For example, a study skills curriculum such as SOAR® (SOAR Learning Inc., 2018) may increase homework completion by 20%. School counselors can infer that the intervention will lead to increases in student achievement; literature suggests homework completion is positively correlated with GPA (Kalenkoski & Pabilonia, 2017).
Although school counselors often work directly with students, they also can engage in efforts to promote student achievement through work with parents and families. For example, Ray, Lambie, and Curry (2007) suggested school counselors can offer parenting skills training to promote positive parenting practices. Other authors have advocated to strengthen the partnerships with and involvement of parents, which are factors related to student achievement (Bryan & Henry, 2012; Epstein, 2018). In developing interventions that aim to build partnership and increase involvement, it is important for school counselors to understand the values, assumptions, beliefs, and behaviors of parents (Bryan & Henry, 2012). During the initial stages of partnering with families, school counselors should address any biases and assumptions that may impede the partnership (Warren, 2017). Furthermore, strategies and interventions should be data-driven and aim to promote student achievement (Hatch, 2014). In the current study, researchers examined the relationships between parenting beliefs, authoritative parenting style, and student achievement. School counselors who understand the relationships between these factors are best positioned to meet the needs of all students.
Parenting Beliefs
The beliefs parents maintain are especially pertinent to the overall wellness and success of their children (Warren, 2017). At times, parents may place unreasonable demands on themselves, their children, or the practice of parenting in general. For example, a parent may think, “My child should always do what I say, and I cannot stand it otherwise.” This belief can have a detrimental impact on the parent–child relationship and family unit as well as the psychosocial development of the child (Bernard, 1990).
Rational emotive behavior therapy (REBT), developed by Ellis (1962), emphasizes two main types of thoughts pertinent to the beliefs of parents: rational and irrational. Rational thoughts are flexible and preferential in nature. These thoughts lead to healthy emotions and functional behaviors. Alternatively, irrational beliefs are rigid and dogmatic and stem from demands placed on the self, others, and life. “Life should always treat me fairly and it is horrible when it does not,” is an example of an irrational belief. This belief can lead to unhealthy emotions (e.g., anger, depression) and result in unhelpful or dysfunctional behavior.
A central goal of REBT is to advance acceptance of the self, others, and life in general. In turn, individuals are encouraged to abstain from global evaluations or rating the self, others, or life as totally bad. When striving toward acceptance, individuals are happier and more successful in life (Dryden, 2014). Researchers have studied REBT and associated constructs among various populations, including children (Gonzalez et al., 2004; Sapp, 1996; Sapp, Farrell, & Durand, 1995; Warren & Hale, 2016), teachers (Warren & Dowden, 2012; Warren & Gerler, 2013), college students (McCown, Blake, & Keiser, 2012; Warren & Hale, in press), and parents (Terjesen & Kurasaki, 2009; Warren, 2017). Literature suggests a strong correlation between irrational beliefs and dysfunction, regardless of the measure used or sample under investigation.
Findings from Hamamci and Bağci (2017) have suggested that a relationship exists between family functioning and the degree to which parents hold irrational expectations about their children. Emotional support and responsiveness of parents deteriorate with an increase in irrational beliefs. Additionally, child behavior issues are more prevalent when parents think irrationally. Hojjat et al. (2016) found that children are more susceptible to substance abuse when their parents maintain irrational beliefs and unrealistic expectations. Parenting styles that advance unrealistic or irrational academic expectations may stifle academic success and promote the development of irrational beliefs and unhealthy negative emotions (e.g., anxiety) in children (Kufakunesu, 2015).
Parenting Styles
Parenting style is most often used to broadly describe how parents interact with their children. In 1966, Diana Baumrind presented three major parenting styles: authoritarian, authoritative, and permissive. Later, Maccoby and Martin (1983) identified a fourth style of parenting: neglectful. Parenting styles are defined by collections of attitudes and behaviors expressed to children by their parents (Darling & Steinberg, 1993) and are often based upon the degree of demandingness/control and responsiveness. Parents who maintain an authoritarian parenting style are highly demanding, yet emotionally unresponsive, while authoritative parents exude high demands, but are communicative and responsive (Baumrind, 1991). Permissive parents, on the other hand, are responsive, yet lack firm control of their children; neglectful parenting involves a lack of emotional support as well as little control (Pinquart, 2016).
The manner in which parents parent can impact their child’s success in school. Of the four parenting styles described, research findings suggested that models of parenting aligning with the authoritative parenting style are most closely linked to student achievement (Carlo, White, Streit, Knight, & Zeiders, 2018; Castro et al., 2015; Kenney, Lac, Hummer, Grimaldi, & LaBrie, 2015; Masud, Thurasamy, & Ahmad, 2015). Additionally, the impact of parenting style on student success seems to vary little across culture. A meta-analysis conducted by Pinquart and Kauser (2018) suggested that children across the world may benefit academically from authoritative parents. Although a plethora of evidence supporting this relationship exists, a meta-analysis conducted by Pinquart (2016) found a small effect size, suggesting the relationship between authoritative parenting and student achievement is minimal. Regardless, the manner in which parents interact with their children impacts many aspects of child development, including their ability to succeed in school.
Purpose of the Study
This article explores the relationships between parenting beliefs, styles, and student achievement. Ellis, Wolfe, and Moseley (1981) suggested parents’ behaviors stem from their thoughts and emotions. These beliefs impact the manner in which parents interact with their children. For example, parents who hold rigid or extreme beliefs may respond to their children more negatively than parents who maintain a flexible belief system. As such, parenting beliefs may impact parenting style, and therefore the success of students. However, the literature is scant when exploring the relationships between parenting beliefs, parenting style, and student achievement.
In order to work effectively with parents, it is important that school counselors understand parenting beliefs and styles and their impact on student achievement. Several research questions guided this study, including: (a) Is there a relationship between student achievement and parental involvement, homework completion, discipline referrals, and suspensions?; (b) Is authoritative parenting related to student achievement?; and (c) Are parenting beliefs related to student achievement? Based on these research questions and existing literature, the following hypotheses were generated: Hypothesis #1: A significant relationship exists between GPA and student achievement–related variables. Hypothesis #2: Rational, irrational, and global evaluation parenting beliefs are predictive of authoritative parenting. Hypothesis #3: Authoritative parenting is significantly positively related to student achievement. Hypothesis #4: Parenting beliefs are significantly related to student achievement–related variables.
Method
Participants
This study included parents living in the southeastern United States (N = 49) who self-reported having children enrolled in elementary, middle, or high school. Of the participants, 96% (n = 47) were mothers, while 4% (n = 2) were fathers. Regarding race and ethnicity, 45% (n = 22) identified as White, 41% (n = 20) identified as American Indian, 8% (n = 4) identified as African American, and 6% (n = 3) identified as Hispanic/Latino. The mean age of the participants’ children was 11 years old; ages ranged from 5 to 18. All grade levels (K–12) across elementary (n = 28), middle (n = 6), and high school (n = 15) were represented, with second grade represented most frequently.
G*Power 3.1, developed by Faul, Erdfelder, Lang, and Bucher (2007), was utilized during an a priori power analysis. The author conducted the power analysis to ascertain the minimum number of participants needed to reach statistical significance, should it exist among the variables under investigation. With statistical power set at .80 and alpha level set at .05, the analysis produced a minimum sample size of 40. This sample size was large enough to detect a medium effect size
(f2 = .35). As a result, the sample size was sufficient to explain the relationships between the predictor and criterion variables.
Instruments
The parents who participated in this study completed a demographic questionnaire and two surveys. The demographic questionnaire, developed by the first author, captured race/ethnicity and gender of the parent in addition to the level of involvement in their child’s schooling. Student achievement–related questions also were asked to capture the age of the participant’s child, grade level, GPA, homework completion percentage, and number of discipline referrals and suspensions. Participants responded to questions such as, “What percentage of your child’s homework is completed on a weekly basis?” Other surveys utilized in this study include the following.
Parental Authority Questionnaire–Revised (PAQ-R; Reitman, Rhode, Hupp, & Altobello, 2002). The PAQ-R is a 30-item self-report measure of parenting style. The PAQ-R is a revision of the Parental Authority Questionnaire (PAR; Buri, 1991) and is grounded in the work of Baumrind (1971). Three subscales, Authoritarian, Authoritative, and Permissive, comprising 10 items each, assess the degree to which parents exhibit control, demand maturity, and are responsive and communicative with their child. Participants indicate their level of agreement with statements such as, “I tell my children what they should do, but I explain why I want them to do it” using a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5).
Findings from a study conducted by Reitman et al. (2002) suggested that the PAQ-R is a reliable measure of authoritarian, authoritative, and permissive parenting styles when considering respondents’ demographic characteristics such as socioeconomic status or race. The Authoritarian (r = .87), Authoritative, (r = .61), and Permissive (r = .67) subscales of the PAQ-R have good test-retest reliability at one month. The Authoritarian (r = .25) and Authoritative (r = .34) subscales were positively correlated with the Communication subscale of the Parent-Child Relationship Inventory (Gerard, 1994), suggesting convergent validity. Across three distinct samples of parents, coefficient alphas ranged from .72 to .76 for Authoritarian, .56 to .77 for Authoritative, and .73 to .74 for Permissive, demonstrating internal consistency (Reitman et al., 2002).
In the current study, only the Authoritative subscale was used. The demographic characteristics of participants in Sample A in a study conducted by Reitman et al. (2002) most closely aligned with the sample in the present study. Factor loadings for Sample A were identical to the Authoritative subscale of the original PAR and therefore used in this study. For the present study, the Authoritative subscale has an internal consistency of .69.
Parent Rational and Irrational Belief Scale (PRIBS; Gavita, David, DiGiuseppe, & DelVecchio, 2011). The PRIBS was used in this study to assess participants’ beliefs related to their child’s behavior and parenting roles. The self-report instrument contains a total of 24 items; four are control items. Three subscales, Rational Beliefs (RB), Irrational Beliefs (IB), and Global Evaluation (GE), comprise the remaining 20 items. The RB subscale contains 10 items and assesses the degree to which preferential and realistic thoughts related to parenting are maintained. The IB subscale includes six items and evaluates the demands parents place on themselves and their child. The GE subscale comprises four items and assesses the degree to which parents globally rate themselves or their children.
A 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5) is used to respond to items such as, “My child must absolutely respect and obey me.” Scores on the PRIBS generally range from 39 (very low) to 60 (very high). The PRIBS and its subscales are significantly correlated with other measures of irrationality and negative emotion, including the General Attitudes and Beliefs Scale-Short Form (Lindner, Kirkby, Wertheim, & Birch, 1999) and the Parental Stress Scale (Berry & Jones, 1995). Gavita et al. (2011) suggested the PRIBS is a reliable measure of parent irrationality; test-retest reliability (r = .78) for the full scale was acceptable after two months. Internal consistency for the PRIBS was .73. The coefficient alphas for RB, IB, and GE were .83, .78, and .71, respectively. For the current study, an internal consistency coefficient of .46 was found for the PRIBS. Additionally, coefficient alphas for the subscales are .62 (RB), .80 (IB), and .43 (GE). All PRIBS subscales were used in this study.
Procedure
A review of literature was conducted in an effort to identify the measures for use in this study. Additionally, a brief demographic instrument was developed to obtain relevant parent and child demographic information. Qualtrics survey software was utilized to prepare the survey packet (i.e., informed consent, demographic questionnaire, and surveys) for electronic dissemination. An application to complete the study then was submitted for review to the institutional review board (IRB) at the researchers’ university. Upon IRB approval, the researchers disseminated an electronic message containing a link to the research packet via a graduate counseling student listserv. An email also was distributed to staff who worked in the School of Education at the researchers’ university. The email contained a request for parents of K–12 students to participate in the study; recipients also were asked to forward the email to family, friends, and colleagues. The email was disseminated on three occasions across two weeks. Participants who completed the study were entered into a drawing for a chance to win $50.
Results
Preliminary Analyses
In order to gain a better understanding of the student achievement–related data collected during this study, initial analyses were conducted. Prior to analysis, GPA was calculated using a letter grade–GPA conversion table; parents reported letter grades on the survey. As such, grades of A+, A, and A- equated to GPAs of 4.33, 4.0, and 3.67, respectively. The student achievement–related variables included in the initial analyses were parental involvement, discipline referrals, suspensions, and homework completion.
Pearson product-moment correlation coefficients and multiple regression analyses were used to test the hypothesis that GPA is related to and predicted by these student achievement–related variables. The degree of parental involvement and homework completion were positively and significantly related to GPA. Suspensions were negatively and significantly related to GPA. Discipline referrals were not significantly related to GPA. The descriptive statistics and correlations for these variables are offered in Table 1.

Prior to additional analysis, basic assumptions of multiple regression analysis were tested and satisfied. Standardized residual plots and Q-Q plots were inspected; bivariate correlations also were examined. Next, a multiple regression analysis including parent involvement, suspensions, and homework completion as predictors was conducted with GPA as the criterion variable. Discipline referrals were not included in the regression analysis. A significant regression equation was found: F(3, 45) = 11.539, p < .001. The model with these three predictors explained a significant amount of the variance in GPA (R2 = .435). Significant contributions were made to the model by each of the three predictor variables: parent involvement (β = .284, p < .05), suspensions (β = -.369, p < .05), and homework completion (β = .273, p < .05).
Main Analyses
A multiple linear regression was used to test the hypothesis that authoritative parenting is predicted by parenting beliefs. Authoritative parenting served as the criterion variable. RB, IB, and GE were predictor variables. A combination of these predictor variables yielded a significant regression equation: F(3, 38) = 14.536, p < .000. The model explained a significant portion of variance (53%) in authoritative parenting (see Table 2). Additionally, RB (β = .38, p < .05) was positively and significantly related to authoritative parenting. IB (β = .46, p < .001) also was positively and significantly correlated with authoritative parenting. GE did not contribute significantly to the model (β = -.25 p > .05).

A simple linear regression was performed to test the hypothesis that authoritative parenting predicts student achievement. Based on prior research findings that suggest authoritative parenting is related to positive student achievement, authoritative parenting served as a predictor variable; the criterion variable was GPA. Output from the analysis revealed that authoritative parenting did not predict GPA for this sample of parents: F(1, 40) = .642, p > .05.
Finally, the hypothesis that parenting beliefs predict student achievement–related variables was tested using multiple linear regression modeling. IB, RB, and GE were predictor variables and parent involvement, suspension, and homework completion were criterion variables. A combination of these predictor variables yielded a non-significant regression equation when parental involvement was the criterion variable: F(3, 37) = 1.773, p = .169. Suspensions were not predicted by RB, IB, or GE: F(3, 37) = 1.232, p = .312. Finally, a combination of these predictor variables yielded a non-significant regression equation when homework completion was the criterion variable: F(3, 37) = 2.382, p = .085. Although this model did not explain variance in homework completion, IB was positively and significantly related to homework completion: t(39) = 2.34, p = .025; β = .357.
Discussion
The hypotheses put forth based on previous research and literature were supported and refuted in various instances based on the analyses of the data collected. In regard to the first hypothesis, all student achievement–related factors except discipline referrals were significantly related to GPA. This finding is consistent with research that explores the relationships of proximal student achievement–related factors and distal student achievement outcomes. Parental involvement in their child’s schooling, homework completion, and suspensions are predictors of GPA. Each of these variables contributed to the overall model for predicting student achievement. This finding demonstrates the value of parental involvement and homework completion in the success of students. Additionally, the negative impact of suspension on the academic achievement of students is highlighted. This outcome signals the importance of fostering safe and inviting schools and establishing policies that offer alternatives to suspension unless absolutely necessary.
The second hypothesis purported that authoritative parenting is significantly related to RB, IB, and GE. This hypothesis was supported; combined, RB, IB, and GE predicted authoritative parenting. Research findings suggest that authoritative parenting is related to student achievement, so it is counterintuitive that both RB and IB are positively related to authoritativeness. IB typically lead to dysfunction rather than positive outcomes such as student achievement (Terjesen & Kurasaki, 2009). However, according to Reitman et al. (2002) and others, authoritative parents are demanding, yet supportive of their children. The demandingness described in this parenting style may be a derivative of irrational thinking, as evidenced by the significant contribution of IB to the model. As suggested by Bernard (1990), parents who place rigid demands on their children may be less supportive; effective communication also may fluctuate. It is likely that an acceptable balance of demands, free of unrealistic, rigid expectations, coupled with support, is most effective when considering the role of authoritative parenting on student achievement. Excessive or unrealistic demands may lead to increases in student achievement, but at the expense of the parent–child relationship as well as mental health. In turn, these demands may serve as a barrier to home and school success (Terjesen & Kurasaki, 2009; Warren, 2017).
Closely tied to the second hypothesis, the third hypothesis suggested that authoritative parenting is significantly positively related to student achievement. Based on the data set analyzed, this hypothesis was not supported. This finding is inconsistent with previous research, which stated that the authoritative parenting style correlates to positive student achievement. In a meta-analysis conducted by Pinquart (2016), a small effect size was found in the relationship between authoritativeness and student achievement. It is possible that a significant relationship exists, yet was not found in this study because of a small sample size. Alternatively, authoritative parenting was not related to student achievement, a finding contrary to Pinquart (2016). Demographic variables such as race/ethnicity of participants (e.g., 40.8% American Indian) were not accounted for in this study and also may have implications for the findings.
The final hypothesis indicated that parenting beliefs are significantly related to student achievement–related variables; this hypothesis was partially supported. Although parenting beliefs were not predictive of parental involvement or suspensions, IB were significantly related to homework completion. Students who consistently complete their homework appear to have parents who maintain IB. Although homework completion is important and leads to academic achievement (Kalenkoski & Pabilonia, 2017), according to REBT, irrational thinking is unproductive and leads to unhealthy negative emotion and dysfunctional behaviors (Dryden, 2014). In some instances, it is possible that parents model unhelpful psychosocial processes (e.g., irrational thinking, anger, yelling) when facilitating the completion of their child’s homework. This may lead to rifts in the parent–child relationship and a general disdain for doing homework, especially that which is difficult or challenging. As such, it is important for parents to set realistic, high expectations for homework completion. These expectations should be based on the child’s strengths and weaknesses, clearly communicated, and consistently followed. Parents are encouraged to hold their children accountable without placing demands on themselves, their child, or the homework process in general (Warren, 2017).
Overall the data from this study presented interesting findings related to authoritative parenting style, beliefs, and student achievement. Certain factors such as homework completion and parental involvement were positively related to GPA; school suspension had a negative impact on GPA. Although these findings are not novel, consideration for the relationships between authoritativeness, parent beliefs, and student achievement in this investigation is noteworthy. Although homework completion was positively related to GPA, it also was correlated with IB. In combination, these findings provide an interesting perspective on the ways in which authoritativeness is related to parenting beliefs, which, in turn, appear to influence homework completion, a key determinant of positive distal student achievement outcomes. Although limitations exist, this study can help to facilitate the development of additional research and offers practical implications for school counselors.
Limitations
As suggested, there are several limitations of this study. When considering the generalizability of these results and potential implications for practice, readers should account for the method of data collection and the sample used in this study. First, data were gathered using self-report measures. Because of the nature of the questions asked on the survey, parents participating in this study may have provided socially desirable responses rather than indicating their actual parenting beliefs and behaviors. Additionally, the sample size was small, yet it was sufficiently sized to detect moderate effects. A convenience sample was used and likely is not representative of the general population. Students and faculty affiliated with a university listserv were contacted and asked to participate and disseminate the study information to their family and friends. A larger sample size would have increased the generalizability of these results and yielded greater power, including the ability to detect smaller effect sizes among the variables.
Future Research
Research investigating parenting styles, beliefs, and student achievement variables such as discipline referrals, suspension, and homework completion is sparse. This study offers a foundation for future empirical and action-based research in this area. Researchers initially are encouraged to replicate this study using a larger, more representative sample of parents with school-aged children. Replication may shed additional light on the strengths of the relationships of the variables explored in this study. Given the achievement gap, including the disproportionate suspension rates that exist in K–12 schools among students of color, it is especially important for researchers to explore the impact of parenting styles and beliefs on the achievement of students from historically underrepresented backgrounds. The American Indian population, specifically, is largely absent in research that explores factors of K–12 student success, yet over 500,000 American Indian students are enrolled in schools across the nation (Snyder, de Brey, & Dillow, 2018). This lack of research is a barrier for school counselors and other educators who seek to better support and understand American Indian families and students. Research that explores these relationships within and across specific racial/ethnic groups, including African American, Hispanic/Latino, and American Indian, can serve as a catalyst for school counselors to enhance service delivery and meet the needs of all students.
Researchers also are encouraged to explore the effects of targeted parenting interventions, such as rational emotive-social behavioral (RE-SB) consultation (Warren, 2017) on parenting and student achievement. School counselors can implement large group, small group, or individual RE-SB consultation with parents to address IB and promote student success (Warren, 2017). School counselors, in collaboration with researchers, can play a central role in the development, delivery, and evaluation of parenting interventions that aim to promote student success; these efforts also can further establish evidence-based practice in school counseling.
Implications for School Counselors
According to the ASCA National Model (ASCA, 2012), school counselors play an integral role in supporting the academic, social-emotional, and career development of all students through work with various stakeholders, including students, teachers, and parents. The findings of this study offer insight into the connection between parenting and student success. Operating in the academic domain, school counselors can deliver direct and indirect services to support the success of all students. The recommendations provided below serve to guide school counselors in identifying and delivering targeted programming that yields positive student outcomes.
A broad strategy for promoting academic success involves the establishment of a comprehensive school counseling program that includes interventions that aim to increase homework completion, decrease suspension rates, and increase parental involvement. As the findings of this study suggest, these factors have a direct impact on student achievement. Therefore, school counselors should leverage their roles as leaders, advocates, and consultants to ensure students are adequately supported by parents and positioned by their teachers to meet the daily expectations of school.
As educational leaders, school counselors are encouraged to engage parents, teachers, administrators, and students in ongoing, critical discussion about the relationships between student achievement–related factors and GPA. Classroom guidance, staff development sessions, and parent workshops are viable opportunities to disseminate this information and engage stakeholders. School counselors can involve teachers and administrators in discussions surrounding classroom and school policies and procedures that impact homework completion, suspension, and parent involvement. Leveraging student and school data during these conversations are more likely to lead to classroom and school policy revisions that accommodate all students and their families. When school counselors collaborate with teachers and administrators, innovative strategies and support structures to promote homework completion and alternatives to suspension will emerge.
School counselors also can use the findings of this study to increase their awareness of the values and beliefs of parents. Used within the context of culture, these findings can offer school counselors additional insights that may be useful when working with parents. For instance, when working with American Indian families, school counselors should consider how customs and traditions impact the manner in which parents engage with their children (Castagno & Brayboy, 2008). By understanding the culture of students and families while considering parenting styles and beliefs, school counselors can partner with parents in intentional ways in an effort to promote student achievement. It is especially important to consider strategies to engage parents who may experience barriers to visiting school. School counselors can seek community resources and partnerships that can be leveraged to increase parental involvement. Using asset mapping as promoted by Griffin and Farris (2010), school counselors can help parents connect to school via the workplace, church, or community centers.
School counselors are encouraged to work closely with parents to establish programming that best supports parents’ efforts to help their children succeed. RE-SB consultation, as described by Warren (2017), is a viable service to educate parents about parenting styles and the impact of their thoughts on emotions and behaviors. For example, school counselors can hold a workshop for parents during a PTA event to promote rational thinking. “Rational reminders” disseminated via the school’s social media account also can be useful for parents not familiar with REBT who are attempting to set realistic expectations and provide optimal support to their children. Interventions such as these can increase parents’ self-awareness of the influence they have on their children and lead to positive student outcomes.
Finally, school counselors should explore strategies that foster social-emotional development for all students and especially those with little parental support. Establishing support systems among students can increase their academic success (Sedlacek, 2017). Mentoring programs that resemble or simulate the parent–child relationship and model rational thinking may yield academic success, given the findings of this study. School counselors also can develop programming that aligns with non-cognitive factors as promoted by Warren and Hale (2016). Students who have a positive self-concept, realistically appraise themselves, are involved in the community, take on leadership roles, have experience in a specific field, and have a support network are better positioned to succeed in school and in life (Sedlacek, 2017). These efforts may position students for school success by neutralizing or reducing the negative impact a lack of parental involvement has on achievement.
Conclusion
School counselors play a critical role in today’s schools. Serving as leaders, advocates, collaborators, and consultants with an aim of promoting student success, school counselors work with many stakeholders, including teachers, administrators, and students and their parents. This study sheds light on the impact of suspension, homework completion, and parental involvement on student achievement. The relationships between parent beliefs and authoritativeness and student achievement also are explored. The authors hope the findings of this study foster awareness and lead school counselors to further consider the impact parents have on student achievement. An understanding of parenting style and beliefs and their impact on student achievement affords school counselors the opportunity to develop targeted programs that increase parent involvement, strengthen the school–parent partnership, and promote academic success.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
American School Counselor Association. (2012). ASCA national model: A framework for school counseling programs
(3rd ed.). Alexandria, VA: Author.
Baumrind, D. (1966). Effects of authoritative parental control on child behavior. Child Development, 37, 887–907. doi:10.2307/1126611
Baumrind, D. (1971). Current patterns of parental authority. Developmental Psychology, 4, 1–103.
doi:10.1037/h0030372
Baumrind, D. (1991). Effective parenting during the early adolescent transition. In P. A. Cowan & E. M. Hetherington (Eds.), Advances in Family Research Series. Family Transitions (pp. 111–163). Hillsdale, NJ: Lawrence Erlbaum Associates.
Bernard, M. E. (1990). Rational-emotive therapy with children and adolescents: Treatment strategies. School Psychology Review, 19, 294–303.
Berry, J. O., & Jones, W. H. (1995). The parental stress scale: Initial psychometric evidence. Journal of Social and Personal Relationships, 12, 463–472. doi:10.1177/0265407595123009
Bryan, J., & Henry, L. (2012). A model for building school–family–community partnerships: Principles and process. Journal of Counseling & Development, 90, 408–420. doi:10.1002/j.1556-6676.2012.00052.x
Buri, J. R. (1991). Parental authority questionnaire. Journal of Personality Assessment, 57, 110–119.
doi:10.1207/s15327752jpa5701_13
Carlo, G., White, R. M., Streit, C., Knight, G. P., & Zeiders, K. H. (2018). Longitudinal relations among
parenting styles, prosocial behaviors, and academic outcomes in U.S. Mexican adolescents. Child
Development, 89, 577–592. doi:10.1111/cdev.12761
Castagno, A. E., & Brayboy, B. M. J. (2008). Culturally responsive schooling for Indigenous youth: A review of the literature. Review of Educational Research, 78, 941–993. doi:10.3102/0034654308323036
Castro, M., Expósito-Casas, E., López-Martín, E., Lizasoain, L., Navarro-Asencio, E., & Gaviria, J. L. (2015). Parental involvement on student academic achievement: A meta-analysis. Educational Research Review, 14, 33–46. doi:10.1016/j.edurev.2015.01.002
Darling, N., & Steinberg, L. (1993). Parenting style as context: An integrative model. Psychological Bulletin, 113, 487–496. doi:10.1037/0033-2909.113.3.487
Dryden, W. (2014). Rational emotive behaviour therapy: Distinctive features. London, UK: Routledge.
Ellis, A. (1962). Reason and emotion in psychotherapy: A new and comprehensive method of treating human disturbance.
Secaucus, NJ: Citadel.
Ellis, A., Wolfe, J. L., & Moseley, S. (1981). How to raise an emotionally healthy, happy child. Carlsbad, CA: Borden.
Epstein, J. L. (2018). School, family, and community partnerships: Preparing educators and improving schools. New York, NY: Routledge.
Faul, F., Erdfelder, E., Lang, A.-G., & Bucher, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. doi:10.3758/BF03193146
Gavita, O. A., David, D., DiGiuseppe, R., & DelVecchio, T. (2011). The development and validation of the Parent Rational and Irrational Beliefs Scale. Procedia – Social and Behavioral Sciences, 30, 2305–2311. doi:10.1016/j.sbspro.2011.10.449
Gerard, A. B. (1994). Parent-child relationship inventory. Los Angeles, CA: Western Psychological Services.
Gonzalez, J. E., Nelson, J. R., Gutkin, T. B., Saunders, A., Galloway, A., & Shwery, C. S. (2004). Rational emotive therapy with children and adolescents: A meta-analysis. Journal of Emotional and Behavioral Disorders, 12, 222–235. doi:10.1177/10634266040120040301
Griffin, D., & Farris, A. (2010). School counselors and collaboration: Finding resources through community asset mapping. Professional School Counseling, 13, 248–256. doi:10.1177/2156759X1001300501
Hamamci, Z., & Bağci, C. (2017). Analyzing the relationship between parent’s irrational beliefs and their children’s behavioral problems and family function. Gaziantep University Journal of Social Sciences, 16, 733–740. doi:10.21547/jss.292722
Hatch, T. (2014). The use of data in school counseling: Hatching results for students, programs, and the profession. Thousand Oaks, CA: Corwin.
Hojjat, S. K., Golmakanie, E., Khalili, M. N., Smaili, H., Hamidi, M., & Akaberi, A. (2016). Personality traits and irrational beliefs in parents of substance-dependent adolescents: A comparative study. Journal of Child & Adolescent Substance Abuse, 25, 340–347. doi:10.1080/1067828X.2015.1012612
Imose, R., & Barber, L. K. (2015). Using undergraduate grade point average as a selection tool: A synthesis of the literature. The Psychologist-Manager Journal, 18, 1–11. doi:10.1037/mgr0000025
Kalenkoski, C. M., & Pabilonia, S. W. (2017). Does high school homework increase academic achievement? Education Economics, 25, 45–59. doi:10.1080/09645292.2016.1178213
Kenney, S. R., Lac, A., Hummer, J. F., Grimaldi, E. M., & LaBrie, J. W. (2015). Pathways of parenting style on adolescents’ college adjustment, academic achievement, and alcohol risk. Journal of College Student Retention: Research, Theory & Practice, 17, 186–203. doi:10.1177/1521025115578232
Kufakunesu, M. (2015). The influence of irrational beliefs on the mathematics achievement of secondary school learners in Zimbabwe. Retrieved from University of South Africa Institutional Repository (http://hdl.handle.net/10500/20072).
LeFevre, A. L., & Shaw, T. V. (2012). Latino parent involvement and school success: Longitudinal effects of formal and informal support. Education and Urban Society, 44, 707–723. doi:10.1177/0013124511406719
Lindner, H., Kirkby, R., Wertheim, E., & Birch, P. (1999). A brief assessment of irrational thinking: The
shortened General Attitude and Belief Scale. Cognitive Therapy and Research, 23, 651–663.
doi:10.1023/A:1018741009293
Maccoby, E. E., & Martin, J. A. (1983). Socialization in the context of the family: Parent-child interaction. In E.
M. Hetherington (Ed.), Mussen Manual of Child Psychology (Vol. 4, 4th ed., pp. 1–102). New York, NY:
Wiley.
Masud, H., Thurasamy, R., & Ahmad, M. S. (2015). Parenting styles and academic achievement of young adolescents: A systematic literature review. Quality & Quantity, 49, 2411–2433.
doi:10.1007/s11135-014-0120-x
McCown, B., Blake, I. K., & Keiser, R. (2012). Content analyses of the beliefs of academic procrastinators.
Journal of Rational-Emotive & Cognitive-Behavior Therapy, 30, 213–222. doi:10.1007/s10942-012-0148-6
Noltemeyer, A. L., Ward, R. M., & Mcloughlin, C. (2015). Relationship between school suspension and student outcomes: A meta-analysis. School Psychology Review, 44, 224–240. doi:10.17105/spr-14-0008.1
Pinquart, M. (2016). Associations of parenting styles and dimensions with academic achievement in children and adolescents: A meta-analysis. Educational Psychology Review, 28, 475–493. doi:10.1007/s10648-015-9338-y
Pinquart, M., & Kauser, R. (2018). Do the associations of parenting styles with behavior problems and academic achievement vary by culture? Results from a meta-analysis. Cultural Diversity and Ethnic Minority Psychology, 24, 75–100. doi:10.1037/cdp0000149
Ray, S. L., Lambie, G., & Curry, J. (2007). Building caring schools: Implications for professional school counselors. Journal of School Counseling, 5(14). Retrieved from http://jsc.montana.edu/articles/v5n14.pdf
Reitman, D., Rhode, P. C., Hupp, S. D. A., & Altobello, C. (2002). Development and validation of the Parental Authority Questionnaire–Revised. Journal of Psychopathology and Behavioral Assessment, 24(2), 119–127. doi:10.1023/A:1015344909518
Roby, D. E. (2004). Research on school attendance and student achievement: A study of Ohio schools. Educational Research Quarterly, 28, 3–14.
Sapp, M. (1996). Irrational beliefs that can lead to academic failure for African American middle school students who are academically at-risk. Journal of Rational-Emotive & Cognitive-Behavior Therapy, 14, 123–134. doi:10.1007/BF02238186
Sapp, M., Farrell, W., & Durand, H. (1995). Cognitive-behavioral therapy: Applications for African American middle school at-risk students. Journal of Instructional Psychology, 22(2), 169–177.
Schwerdt, G., West, M. R., & Winters, M. A. (2017). The effects of test-based retention on student outcomes over time: Regression discontinuity evidence from Florida. Journal of Public Economics, 152, 154–169. doi:10.1016/j.jpubeco.2017.06.004
Sedlacek, W. E. (2017). Measuring noncognitive variables: Improving admissions, success and retention for underrepresented students. Herndon, VA: Stylus.
Snyder, T. D., de Brey, C., & Dillow, S. A. (2018). Digest of Education Statistics 2016 (NCES 2017-094). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
SOAR Learning Inc. (2018). SOAR® Learning & Soft Skills Curriculum for College & Career Readiness. Retrieved from https://studyskills.com
Spengler, M., Brunner, M., Martin, R., & Lüdtke, O. (2016). The role of personality in predicting (change in) students’ academic success across four years of secondary school. European Journal of Psychological Assessment, 32, 95–103. doi:10.1027/1015-5759/a000330
Terjesen, M. D., & Kurasaki, R. (2009). Rational emotive behavior therapy: Applications for working with parents and teachers. Estudos de Psicologia (Campinas), 26, 3–14. doi:10.1590/S0103-166X2009000100001
Warren, J. M. (2017). School consultation for student success: A cognitive behavioral approach. New York, NY: Springer.
Warren, J. M., & Dowden, A. R. (2012). Elementary school teachers’ beliefs and emotions: Implications for school counselors and counselor educators. Journal of School Counseling, 10(19). Retrieved from https://files.eric.ed.gov/fulltext/EJ981200.pdf
Warren, J. M., & Gerler, E. R., Jr. (2013). Effects of school counselors’ cognitive behavioral consultation on irrational and efficacy beliefs of elementary school teachers. The Professional Counselor, 3, 6–15. doi:10.15241/jmw.3.1.6
Warren, J. M., & Hale, R. W. (2016). Fostering non-cognitive development of underrepresented students through rational emotive behavior therapy: Recommendations for school counselor practice. The Professional Counselor, 6, 89–106. doi:10.15241/jw.6.1.89
Warren, J. M., & Hale, R. W. (in press). Predicting grit and resilience: Exploring college students’ academic rational beliefs. Journal of College Counseling.
York, T. T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research & Evaluation, 20(5), 1–20.
Jeffrey M. Warren, NCC, is an associate professor and Chair of the Counseling Department at the University of North Carolina at Pembroke. Leslie A. Locklear is the FATE Director at the University of North Carolina at Pembroke. Nicholas A. Watson is a graduate student at the University of North Carolina at Pembroke. Correspondence can be addressed to Jeffrey Warren, 1 University Drive, Pembroke, NC 28372, jeffrey.warren@uncp.edu.
Nov 10, 2018 | Volume 8 - Issue 4
Zachary D. Bloom, Victoria A. McNeil, Paulina Flasch, Faith Sanders
Empathy plays an integral role in the facilitation of therapeutic relationships and promotion of positive client outcomes. Researchers and scholars agree that some components of empathy might be dispositional in nature and that empathy can be developed through empathy training. However, although empathy is an essential part of the counseling process, literature reviewing the development of counseling students’ empathy is limited. Thus, we examined empathy and sympathy scores in counselors-in-training (CITs) in comparison to students from other academic disciplines (N = 868) to determine if CITs possess greater levels of empathy than their non-counseling academic peers. We conducted a MANOVA and failed to identify differences in levels of empathy or sympathy across participants regardless of academic discipline, potentially indicating that counselor education programs might be missing opportunities to further develop empathy in their CITs. We call for counselor education training programs to promote empathy development in their CITs.
Keywords: empathy, sympathy, counselor education, counselors-in-training, therapeutic relationships
Empathy is considered an essential component of the human experience as it relates to how individuals socially and emotionally connect to one another (Goleman, 1995; Szalavitz & Perry, 2010). Although empathy can be difficult to define (Konrath, O’Brien, & Hsing, 2011; Spreng, McKinnon, Mar, & Levine, 2009), within the counseling profession there is agreement that empathy includes both cognitive and affective components (Clark, 2004; Davis, 1980, 1983). When discussing the difference between affective and cognitive empathy, Vossen, Piotrowski, and Valkenburg (2015) described that “whereas the affective component pertains to the experience of another person’s emotional state, the cognitive component refers to the comprehension of another person’s emotions” (p. 66). Regardless of specific nuances among researchers’ definitions of empathy, most appear to agree that “empathy-related responding is believed to influence whether or not, as well as whom, individuals help or hurt” (Eisenberg, Eggum, & Di Giunta, 2010, p. 144). Furthermore, empathy can be viewed as a motivating factor of altruistic behavior (Batson & Shaw, 1991) and is essential to clients’ experiences of care (Flasch et al., in press). As such, empathy is foundational to interpersonal relationships (Siegel, 2010; Szalavitz & Perry, 2010), including the relationships facilitated in a counseling setting (Norcross, 2011; Rogers, 1957).
Rogers (1957) intuitively understood the necessity of empathy in a counseling relationship, which has been verified by the understanding of the physiology of the brain (Badenoch, 2008; Decety & Ickes, 2009; Siegel, 2010) and validated in the counseling literature (Elliott, Bohart, Watson, & Greenberg, 2011). In a clinical context, empathy can be described as both a personal characteristic and a clinical skill (Clark, 2010; Elliott et al., 2011; Rogers, 1957) that contributes to positive client outcomes (Norcross, 2011; Watson, Steckley, & McMullen, 2014). For example, empathy has been identified as a factor that leads to changes in clients’ attachment styles, treatment of self (Watson et al., 2014), and self-esteem development (McWhirter, Besett-Alesch, Horibata, & Gat, 2002). Moreover, researchers regularly identify empathy as a fundamental component of helpful responses to clients’ experiences (Beder, 2004; Flasch et al., in press; Kirchberg, Neimeyer, & James, 1998).
Although empathy is lauded and encouraged in the counseling profession, empathy development is not necessarily an explicit focus or even a mandated component of clinical training programs. The Council for Accreditation of Counseling and Related Educational Programs (CACREP; 2016) identifies diverse training standards for content knowledge and practice among master’s-level and doctoral-level counselors-in-training (CITs), but does not mention the word empathy in its manual for counseling programs. One of the reasons for this could be that empathy is often understood and taught as a microskill (e.g., reflection of feeling and meaning) rather than as its own construct (Bayne & Jangha, 2016). Yet empathy is more than a component of a skillset, and CITs might benefit from a programmatic development of empathy to enhance their work with future clients (DePue & Lambie, 2014).
The application of empathy, or a counselor’s use of empathy-based responses in a therapeutic relationship, requires skill and practice (Barrett-Lennard, 1986; Truax & Carkhuff, 1967). Clark (2010) cautioned, for example, that counselors’ empathic responses need to be congruent with the client’s experience, and that the misapplication of sympathetic responses as empathic responses can interfere in the counseling relationship. In regard to sympathy, Eisenberg and colleagues (2010) explained, “sympathy, like empathy, involves an understanding of another’s emotion and includes an emotional response, but it consists of feelings of sorrow or concern for the distressed or needy other rather than merely feeling the same emotion” (p. 145). Thus, researchers call for counselor educators to do more than increase CITs’ affective or cognitive understanding of another’s experience, and to assist them in differentiating between empathic responses and sympathetic responses in order to better convey empathic understanding and relating (Bloom & Lambie, in press; Clark, 2010).
With the understanding that a counselor’s misuse of sympathetic responses might interrupt a therapeutic dialogue and that empathy is vital to the therapeutic alliance, researchers call for counselor educators to promote empathy development in CITs (Bloom & Lambie, in press; DePue & Lambie, 2014). Although there is evidence that some aspects of empathy are dispositional in nature (Badenoch, 2008; Konrath et al., 2011), which might make the counseling profession a strong fit for empathic individuals, empathy training in counseling programs can increase students’ levels of empathy (Ivey, 1971). However, the specific empathy-promoting components of empathy training are less understood (Teding van Berkhout & Malouff, 2016). Overall, empathy is an essential component of the counseling relationship, counselor competency, and the promotion of client outcomes (DePue & Lambie, 2014; Norcross, 2011). However, little is known about the training aspect of empathy and whether or not counselor training programs are effective in enhancing empathy or reducing sympathy among CITs. Thus, the following question guided this research investigation: Are CITs’ levels of empathy or sympathy different from their academic peers? Specifically, do CITs possess greater levels of empathy or sympathy than students from other academic majors?
Empathy in Counseling
Researchers have established continuous support for the importance of the therapeutic relationship in the facilitation of positive client outcomes (Lambert & Bergin, 1994; Norcross, 2011; Norcross & Lambert, 2011). In fact, the therapeutic relationship is predictive of positive client outcomes (Connors, Carroll, DiClemente, Longabaugh, & Donovan, 1997; Krupnick et al., 1996), accounting for about 30% of the variance (Lambert & Barley, 2001). That is, clients who perceive the counseling relationship to be meaningful will have more positive treatment outcomes (Bell, Hagedorn, & Robinson, 2016; Norcross & Lambert, 2011). One of the key factors in the establishment of a strong therapeutic relationship is a counselor’s ability to experience and communicate empathy. Researchers estimate that empathy alone may account for as much as 7–10% of overall treatment outcomes (Bohart, Elliott, Greenberg, & Watson, 2002; Sachse & Elliott, 2002), making it an important construct to foster in counselors.
Despite the importance of empathy in the counseling process, much of the literature on empathy training in counseling is outdated. Thus, little is known about the training aspect of empathy; that is, how is empathy taught to and learned by counselors? Nevertheless, early scholars (Barrett-Lennard, 1986; Ivey, 1971; Ivey, Normington, Miller, Morrill, & Haase, 1968; Truax & Carkhuff, 1967) posited that counselor empathy is a clinical skill that may be practiced and learned, and there is supporting evidence that empathy training may be efficacious.
In one seminal study, Truax and Lister (1971) conducted a 40-hour empathy training program with 12 counselor participants and identified statistically significant increases in participants’ levels of empathy. In their investigation, the researchers employed methods in which (a) the facilitator modeled empathy, warmth, and genuineness throughout the training program; (b) therapeutic groups were used to integrate empathy skills with personal values; and (c) researchers coded three of participants’ 4-minute counseling clips using scales of accurate empathy and non-possessive warmth (Truax & Carkhuff, 1967). Despite identifying statistically significant changes in participants’ scores of empathy, it is necessary to note that participants who initially demonstrated low levels of empathy remained lower than participants who initially scored high on the empathy measures. In a later study modeled after the Truax and Lister study, Silva (2001) utilized a combination of didactic, experiential, and practice components in her empathy training program, and found that counselor trainee participants (N = 45) improved their overall empathy scores on Truax’s Accurate Empathy Scale (Truax & Carkhuff, 1967). These findings contribute to the idea that empathy increases as a result of empathy training.
More recent researchers (Lam, Kolomitro, & Alamparambil, 2011; Ridley, Kelly, & Mollen, 2011) have identified the most common methods in empathy training programs as experiential training, didactic (lecture), skills training, and other mixed methods such as role play and reflection. In their meta-analysis, Teding van Berkhout and Malouff (2016) examined the effect of empathy training programs across various populations (e.g., university students, health professionals, patients, other adults, teens, and children) using the training methods identified above. The researchers investigated the effect of cognitive, affective, and behavioral empathy training and found a statistically significant medium effect size overall (g ranged from 0.51 to 0.73). The effect size was larger in health professionals and university students compared to other groups such as teenagers and adult community members. Though empathy increased as a result of empathy training studies, the specific mechanisms that facilitated positive outcomes remain largely unknown.
Although research indicates that empathy training can be effective, specific empathy-fostering skills are still not fully understood. Programmatically, empathy is taught to counselors within basic counseling skills (Bayne & Jangha, 2016), specifically because empathy is believed to lie in the accurate reflection of feeling and meaning (Truax & Carkhuff, 1967). But scholars argue that there is more to empathy than the verbal communication of understanding (Davis, 1980; Vossen et al., 2015). For example, in a more recent study, DePue and Lambie (2014) reported that counselor trainees’ scores on the Empathic Concern subscale of the Interpersonal Reactivity Index (IRI; Davis, 1980) increased as a result of engaging in counseling practicum experience under live supervision in a university-based clinical counseling and research center. In their study, the researchers did not actively engage in empathy training. Rather, they measured counseling students’ pre- and post-scores on an empathy measure as a result of students’ engagement in supervised counseling work to foster general counseling skills. Implications of these findings mirror those described by Teding van Berkhout and Malouff (2016), namely that it is difficult to identify specific empathy-promoting mechanisms. In other words, it appears that empathy training, when employed, produces successful outcomes in CITs. However, counseling students’ empathy also increases in the absence of specific empathy-promoting programs. This begs the question: Are counseling programs successfully training their counselors to be empathic, and is there a difference between CITs’ empathy or sympathy levels compared to students in other academic majors? Thus, the purpose of the present study was to (a) examine differences in empathy (i.e., affective empathy and cognitive empathy) and sympathy levels among emerging adult college students, and (b) determine whether CITs had different levels of empathy and sympathy when compared to their academic peers.
Methods
Participants
We identified master’s-level CITs as the population of interest in this investigation. We intended to compare CITs to other graduate and undergraduate college student populations. Thus, we utilized a convenience sample from a larger data set that included emerging adult college students between the ages of 18 and 29 who were enrolled in at least one undergraduate- or graduate-level course at nine colleges and universities throughout the United States. Participants were included regardless of demographic variables (e.g., gender, race, ethnicity).
Participants were recruited from three sources: online survey distribution (n = 448; 51.6%), face-to-face data collection (n = 361; 41.6%), and email solicitation (n = 34; 3.9%). In total, 10,157 potential participants had access to participate in the investigation by online survey distribution through the psychology department at a large Southeastern university; however, the automated system limited responses to 999 participants. We and our contacts (i.e., faculty at other institutions) distributed an additional 800 physical data collection packets to potential participants, and 105 additional potential participants were solicited by email. Overall, 1,713 data packets were completed, resulting in a sample of 1,598 participants after data cleaning. However, in order to conduct the analyses for this study, it was necessary to limit our sample to groups of approximately equal sizes (Hair, Black, Babin, & Anderson, 2010). Therefore, we were limited to the use of a subsample of 868 participants. Our sample appeared similar to other samples included in investigations exploring empathy with emerging adult college students (e.g., White, heterosexual, female; Konrath et al., 2011).
The participants included in this investigation were enrolled in one of six majors and programs of study, including Athletic Training/Health Sciences (n = 115; 13.2%); Biology/Biomedical Sciences/Preclinical Health Sciences (n = 167; 19.2%); Communication (n = 163; 18.8%); Counseling (n = 153; 17.6%); Nursing (n = 128; 14.7%); and Psychology (n = 142; 16.4%). It is necessary to note that students self-identified their major rather than selecting it from a preexisting prompt. Therefore, the researchers examined responses and categorized similar responses to one uniform title. For example, responses of psych were included with psychology. Further, in order to attain homogeneity among group sizes, we included multiple tracks within one program. For example, counseling included participants enrolled in either clinical mental health counseling (n = 115), marriage and family counseling (n = 24), or school counseling (n = 14) tracks. Table 1 presents additional demographic information (e.g., age, race, ethnicity, graduate-level status). It is necessary to note that, because of the constraints of the dataset, counseling students consisted of master’s-level graduate students, whereas all other groups consisted of undergraduate students.
Table 1
Participants’ Demographic Characteristics
Characteristic |
|
n
|
Total %
|
|
Age |
18–19 |
460
|
52.4
|
|
|
20–21 |
155
|
17.9
|
|
|
22–23 |
130
|
15.0
|
|
|
24–25 |
58
|
6.7
|
|
|
26–27 |
36
|
4.1
|
|
|
28–29 |
27
|
3.1
|
|
Gender |
Female |
692
|
79.7
|
|
|
Male |
167
|
19.2
|
|
|
Other |
8
|
0.9
|
|
Racial |
Caucasian |
624
|
71.9
|
|
Background |
African American/African/Black |
101
|
11.6
|
|
|
Biracial/Multiracial |
65
|
7.5
|
|
|
Asian/Asian American |
40
|
4.6
|
|
|
Native American |
3
|
0.3
|
|
|
Other |
25
|
2.9
|
|
Ethnicity |
Hispanic |
172
|
19.8
|
|
|
Non-Hispanic |
689
|
79.4
|
|
Academic |
Undergraduate |
709
|
81.7
|
|
Enrollment |
Graduate |
152
|
17.5
|
|
|
Other |
5
|
0.6
|
|
Academic Major |
Athletic Training/Health Sciences |
115
|
13.2
|
|
|
Biology/Biomedical Sciences/Preclinical Health Sciences |
167
|
19.2
|
|
|
Counseling |
153
|
17.6
|
|
|
Communication |
163
|
18.8
|
|
|
Nursing |
128
|
14.7
|
|
|
Psychology |
142
|
16.4
|
|
Note. N
= 868.
Procedure
The data utilized in this study were collected as part of a larger study that was approved by the authors’ institutional review board (IRB) as well as additional university IRBs where data was collected, as requested. We followed the Tailored Design Method (Dillman, Smyth, & Christian, 2009), a series of recommendations for conducting survey research to increase participant motivation and decrease attrition, throughout the data collection process for both web-based survey and face-to-face administration. Participants received informed consent, assuring potential participants that their responses would be confidential and their anonymity would be protected. We also made the survey convenient and accessible to potential participants by making it available either in person or online, and by avoiding the use of technical language (Dillman et al., 2009).
We received approval from the authors of the Adolescent Measure of Empathy and Sympathy (AMES; Vossen et al., 2015; personal communication with H. G. M. Vossen, July 10, 2015) to use the instrument and converted the data collection packet (e.g., demographic questionnaire, AMES) into Qualtrics (2013) for survey distribution. We solicited feedback from 10 colleagues regarding the legibility and parsimony of the physical data collection packets and the accuracy of the survey links. We implemented all recommendations and changes (e.g., clarifying directions on the demographic questionnaire) prior to data collection.
All completed data collection packets were assigned a unique ID, and we entered the data into the IBM SPSS software package for Windows, Version 22. No identifying information was collected (e.g., participants’ names). Having collected data both in person and online via web-based survey, we applied rigorous data collection procedures to increase response rates, reduce attrition, and to mitigate the potential influence of external confounding factors that might contribute to measurement error.
Data Instrumentation
Demographics profile. We included a general demographic questionnaire to facilitate a comprehensive understanding of the participants in our study. We included items related to various demographic variables (e.g., age, race, ethnicity). Regarding participants’ identified academic program, participants were prompted to respond to an open-ended question asking “What is your major area of study?”
AMES. Multiple assessments exist to measure empathy (e.g., the IRI, Davis, 1980, 1983; The Basic Empathy Scale [BES], Jolliffe & Farrington, 2006), but each is limited by several shortcomings (Carré, Stefaniak, D’Ambrosio, Bensalah, & Besche-Richard, 2013). First, many scales measure empathy as a single construct without distinguishing cognitive empathy from affective empathy (Vossen et al., 2015). Moreover, the wording used in most scales is ambiguous, such as items from other assessments that use words like “swept up” or “touched by” (Vossen et al., 2015), and few scales differentiate empathy from sympathy. Therefore, Vossen and colleagues designed the AMES as an empathy assessment that addresses problems related to ambiguous wording and differentiates empathy from sympathy.
The AMES is a 12-item empathy assessment with three factors: (a) Cognitive Empathy, (b) Affective Empathy, and (c) Sympathy. Each factor consists of four items rated on a 5-point Likert scale with ratings of 1 (never), 2 (almost never), 3 (sometimes), 4 (often), and 5 (always). Higher AMES scores indicate greater levels of cognitive empathy (e.g., “I can tell when someone acts happy, when they actually are not”), affective empathy (e.g., “When my friend is sad, I become sad too”), and sympathy (e.g., “I feel concerned for other people who are sick”). The AMES was developed in two studies with Dutch adolescents (Vossen et al., 2015). The researchers identified a 3-factor model with acceptable to good internal consistency per factor: (a) Cognitive Empathy (α = 0.86), (b) Affective Empathy (α = 0.75), and (c) Sympathy (α = 0.76). Further, Vossen et al. (2015) established evidence of strong test-retest reliability, construct validity, and discriminant validity when using the AMES to measure scores of empathy and sympathy with their samples. Despite being normed with samples of Dutch adolescents, Vossen and colleagues suggested the AMES might be an effective measure of empathy and sympathy with alternate samples as well.
Bloom and Lambie (in press) examined the factor structure and internal consistency of the AMES with a sample of emerging adult college students in the United States (N = 1,598) and identified a 3-factor model fitted to nine items that demonstrated strong psychometric properties and accounted for over 60% of the variance explained (Hair et al., 2010). The modified 3-factor model included the same three factors as the original AMES. Therefore, we followed Bloom and Lambie’s modifications for our use of the instrument.
Data Screening
Before running the main analysis on the variables of interest, we assessed the data for meeting the assumptions necessary to conduct a one-way between-subjects MANOVA. First, we conducted a series of tests to evaluate the presence of patterns in missing data and determined that data were missing completely at random (MCAR) and ignorable (e.g., < 5%; Kline, 2011). Because of the robust size of these data (e.g., > 20 observations per cell) and the minimal amount of missing data, we determined listwise deletion to be best practice to conduct a MANOVA and to maintain fidelity to the data (Hair et al., 2010; Osborne, 2013).
Next, we utilized histograms, Q-Q plots, and boxplots to assess for normality and identified non-normal data patterns. However, MANOVA is considered “robust” to violations of normality with a sample size of at least 20 in each cell (Tabachnick & Fidell, 2013). Thus, with our smallest cell size possessing a sample size of 115, we considered our data robust to this violation. Following this, we assumed our data violated the assumption for multivariate normality. However, Hair et al. (2010) stated “violations of this assumption have little impact with larger sample sizes” (p. 366) and cautioned that our data might have problems achieving a non-significant score for Box’s M Test. Indeed, our data violated the assumption of homogeneity of variance-covariance matrices (p < .01). However, this was not a concern with these data because “a violation of this assumption has minimal impact if the groups are of approximately equal size (i.e., largest group size ÷ smallest group size < 1.5)” (Hair et al., 2010, p. 365).
It is necessary to note that MANOVA is sensitive to outlier values. To mitigate against the negative effects of extreme scores, we removed values (n = 3) with standardized z-scores greater than +4 or less than -4 (Hair et al., 2010). This resulted in a final sample size of 868 participants.
We also utilized scatterplots to detect the patterns of non-linear relationships between the dependent variables and failed to identify evidence of non-linearity. Therefore, we proceeded with the assumption that our data shared linear relationships. We also evaluated the data for multicollinearity. Participants’ scores of Affective Empathy shared statistically significant and appropriate relationships with their scores of Cognitive Empathy (r = .24) and Sympathy (r = .43). Similarly, participants’ scores of Cognitive Empathy were appropriately related to their scores of Sympathy (r = .36; p < .01). Overall, we determined these data to be appropriate to conduct a MANOVA. Table 2 presents participants’ scores by academic discipline.
Table 2
AMES Scores by Academic Major
Scale
|
Mean (M)
|
SD
|
Range
|
Athletic Training |
|
|
|
Affective Empathy
|
3.20
|
0.80
|
4.00 |
Cognitive Empathy
|
3.80
|
0.62
|
3.33 |
Sympathy
|
4.34
|
0.55
|
2.67 |
Biomedical Sciences |
|
|
|
Affective Empathy
|
3.12
|
0.76
|
4.00 |
Cognitive Empathy
|
3.66
|
0.59
|
3.00 |
Sympathy
|
4.30
|
0.61
|
2.00 |
Communication |
|
|
|
Affective Empathy
|
3.18
|
0.87
|
4.00 |
Cognitive Empathy
|
3.80
|
0.62
|
2.67 |
Sympathy
|
4.27
|
0.69
|
3.00 |
Counseling |
|
|
|
Affective Empathy
|
3.32
|
0.60
|
3.33 |
Cognitive Empathy
|
3.83
|
0.48
|
4.00 |
Sympathy
|
4.32
|
0.54
|
2.00 |
Nursing |
|
|
|
Affective Empathy
|
3.37
|
0.71
|
3.67 |
Cognitive Empathy
|
3.80
|
0.59
|
2.67 |
Sympathy
|
4.46
|
0.49
|
2.00 |
Psychology |
|
|
|
Affective Empathy
|
3.28
|
0.78
|
4.00 |
Cognitive Empathy
|
3.86
|
0.59
|
2.67 |
Sympathy
|
4.35
|
0.65
|
2.67 |
Note. N
= 868.
Results
Participants’ scores on the AMES were used to measure participants’ levels of empathy and sympathy. Descriptive statistics were used to compare empathy and sympathy levels between counseling students and emerging college students from other disciplines. CITs recorded the second highest levels of affective empathy (M = 3.32, SD = .60) and cognitive empathy (M = 3.83, SD = 0.48), and the fourth highest levels of sympathy (M = 4.32, SD = 0.54) when compared to students from other disciplines. Nursing students demonstrated the highest levels of affective empathy (M = 3.37, SD = .71) and sympathy (M = 4.46, SD = .49), and psychology students recorded the highest levels of cognitive empathy (M = 3.86, SD = 0.59) when compared to students from other disciplines. The internal consistency values for each empathy and sympathy subscale on the AMES were as follows: Cognitive Empathy (α = 0.86), Affective Empathy (α = 0.75), and Sympathy (α = 0.76).
We performed a MANOVA to examine differences in empathy and sympathy in emerging adult college students by academic major, including counseling. Three dependent variables were included: affective empathy, cognitive empathy, and sympathy. The predictor for the MANOVA was the 6-level categorical “academic major” variable. The criterion variables for the MANOVA were the levels of affective empathy (M = 3.24, SD = .76), cognitive empathy (M = 3.80, SD = .58), and sympathy
(M = 4.34, SD = .60), respectively. The multivariate effect of major was statistically non-significant:
p = .062, Wilks’s lambda = .972, F (15, 2374.483) = 1.615, η2 = .009. Furthermore, the univariate F scores for affective empathy (p = .139), cognitive empathy (p = .074), and sympathy (p = .113) were statistically non-significant. That is, there was no difference in levels of affective empathy, cognitive empathy, or sympathy based on academic major, including counseling. Thus, these data indicated that CITs were no more empathic or sympathetic than students in other majors, as measured by the AMES.
We also examined these data for differences in affective empathy, cognitive empathy, and sympathy based on data collection method and educational level. However, we failed to identify a statistically significant difference between groups in empathy or sympathy based on data collection method
(e.g., online survey distribution, face-to-face data collection, email solicitation) or by educational level (e.g., master’s level or undergraduate status). Thus, these data indicate that data collection methods and participants’ educational level did not influence our results.
Discussion
The purpose of the present study was to (a) examine differences in empathy (i.e., affective empathy and cognitive empathy) and sympathy levels among emerging adult college students, and (b) determine whether CITs demonstrate different levels of empathy and sympathy when compared to their academic peers. We hypothesized that CITs would record greater levels of empathy and lower levels of sympathy when compared to their non-counseling peers, because of either their clinical training from their counselor education program or the possibility that the counseling profession might attract individuals with strong levels of dispositional empathy. Participants’ scores on the AMES were used to measure participants’ levels of empathy and sympathy. We conducted a MANOVA to determine if participants’ levels of empathy and sympathy differed when grouped by academic majors. CITs did not exhibit statistically significant differences in levels of empathy or sympathy when compared to students from other academic programs. In fact, CITs recorded levels of empathy that appeared comparable to students from other academic disciplines. This finding is consistent with literature indicating that even if empathy training is effective, counselor education programs might not be emphasizing empathy development in CITs or employing empathy training sufficiently. We also failed to identify statistically significant differences in participants’ AMES scores when grouping data by collection method or participants’ educational level. Thus, we believe our results were not influenced by our data collection method or by participants’ educational level.
Implications for Counselor Educators
The results from this investigation indicated that there was not a statistically significant difference in participants’ levels of cognitive or affective empathy or sympathy regardless of academic program, suggesting that CITs do not possess more or less empathy or sympathy than their academic peers. This was true for students in all majors under investigation (i.e., athletic training/health sciences, biology/biomedical sciences/preclinical health sciences, communication, counseling, nursing, and psychology), regardless of age and whether or not they belonged to professions considered helping professions (i.e., counseling, nursing, psychology). Although students in helping professions tended to have higher scores on the AMES than their peers, these differences were not statistically significant.
One might hypothesize that students in helping professions (especially in professions in which individuals have direct contact with clients or patients, such as counseling) would have significantly higher levels of empathy. However, counseling programs may not attract individuals who possess greater levels of trait empathy, or training programs might not be as effective in training their students as previously thought. Although microskills are taught in counselor preparation programs (e.g., reflection of content, reflection of feeling), microskill training might not overlap with material that is taught as part of an empathy training or enhance such training. Thus, microskill training might not be any more impactful for CITs’ development of empathy and sympathy than material included in training programs of other academic disciplines (e.g., athletic training, nursing).
Another potential reason for the lack of recorded differences between CITs and their non-counseling peers could be that counseling students are inherently anxious, skill-focused, self-focused, or have limited self-other awareness (Stoltenberg, 1981; Stoltenberg & McNeill, 2010). We wonder if CITs might not be focused on utilizing relationship-building approaches as much as they are on doing work that promotes introspection and reflection. Another inquiry for consideration is whether CITs potentially possess a greater understanding of empathy as a construct that inadvertently leads CITs to rate themselves lower in empathy than their non-counseling peers. Further, it is possible that CITs potentially minimize their own levels of empathy in an effort to demonstrate modesty, a phenomenon related to altruism and understood as the modesty bias (McGuire, 2003). Future research would be helpful to better understand various mitigating factors. Nevertheless, we suggest that counseling programs might be able to do more to foster empathy-facilitating experiences in counselors by being more proactive and effective in promoting empathy development in CITs. Through a review of the literature, we found support that empathy training is possible, and we wonder if there is a missed opportunity to effectively train counselors if counselor education programs do not intentionally facilitate empathy development in their CITs.
Counselor training programs are not charged to develop empathy in CITs; however, given the importance of empathy in the formation and maintenance of a therapeutic relationship, we propose that counseling training programs consider ways in which empathy is or is not being developed in their specific program. As such, we urge counselor educators to consider strategies to emphasize empathy development in their CITs. For example, reviewing developmental aspects of empathy in children, adolescents, and adults might fit well in a human development course, and the subject can be used to facilitate a conversation with CITs regarding their experiences of empathy development.
Similarly, because empathy consists of cognitive and affective components, CITs might benefit from work that assists them in gaining insight into areas of strengths and limitations in regard to both cognitive and affective aspects of empathy. Students who appear stronger in one area of empathy might benefit from practicing skills related to the other aspect of empathy. For example, if a student has a strong awareness of a client’s experience (i.e., cognitive empathy) but appears to have limitations in their felt sense of a client’s experience (i.e., affective empathy), a counselor educator might utilize live supervision opportunities to assist the student in recognizing present emotions or sensations in their body when working with the client or in a role play. Alternatively, to assist a student with developing a greater intellectual understanding of their client’s experience, a counselor educator might employ interpersonal process recall when reviewing their clinical work to help the student identify what their client might be experiencing as a result of their lived experience. To echo recommendations made by Bayne and Jangha (2016), we encourage counselor educators to move away from an exclusive focus on microskills for teaching empathy and to provide opportunities to teach CITs how to foster a connecting experience through creative means (e.g., improvisational skills).
Furthermore, the results from this study indicated that CITs possess higher levels of sympathy than of both cognitive and affective components of empathy. We recommend that counselor educators facilitate CITs’ understanding of the differences between empathy and sympathy and bring awareness to their use of sympathetic responses rather than empathic responses. It is our hope that CITs will possess a strong enough understanding between empathy and sympathy to be able to choose to use either response as it fits within a counseling context (Clark, 2010). We also encourage counselor educators to consider recommendations made by Bloom and Lambie (in press) to employ the AMES with CITs. The AMES could be a valuable and accessible tool to assist counselor educators in evaluating CITs’ levels of empathy and sympathy in regard to course assignments, in response to clinical situations, or as a wholesale measure of empathy development. As Bloom and Lambie encouraged, clinical training programs might benefit from using the AMES as a tool to programmatically measure CITs’ levels of empathy throughout their experience in their training program (i.e., transition points) as a way to collect programmatic data.
Limitations
Although this study produced important findings, some limitations exist. It is noted that the majority of participants from this study attended universities located within the Southeastern United States. As a result, the sample might not be representative of students nationwide. Similarly, demographic characteristics of the present study including the race, age, and gender composition of the sample limit the generalizability of the findings.
This study also is limited in that the instrument used to assess empathy and sympathy was a self-report measure. Although self-report measures have been shown to be reliable and are widely used within research, these measures might result in the under- or over-reporting of the variables of interest (Gall, Gall, & Borg, 2007). It is necessary to note that we employed the AMES, which was normed with adolescents and not undergraduate or graduate students. Although we recognize that inherent differences exist between adolescent and emerging adult populations, we believed the AMES was an effective choice to measure empathy because of Vossen and colleagues’ (2015) intentional development of the instrument to address existing weaknesses in other empathy assessment instruments. Nonetheless, it is necessary to interpret our results with caution.
Recommendations for Future Research
We recommend future researchers address some of the limitations of this study. Specifically, we recommend continuing to compare CITs’ levels of empathy with students from other academic disciplines, but to include a more diverse array of academic backgrounds. Similarly, we suggest future researchers not limit themselves to an emerging adult population, as both undergraduate and graduate populations include individuals over the age of 29. Further, researchers should aim to collect data from students across the country and to include a more demographically diverse sample in their research designs.
Additionally, it is necessary to note that limitations exist to using self-report measures (Gall et al., 2007), and measures of empathy are vulnerable to a myriad of complications (Bloom & Lambie, in press; Vossen et al., 2015). Thus, we encourage future researchers to consider using different measures of empathy that move away from a self-report format (e.g., clients’ perceptions of cognitive and affective empathy within a therapeutic relationship; Flasch et al., in press). Another area for future research is to track counseling students’ levels of empathy as they enter the counseling profession after graduation. It is possible that as they become more comfortable and competent as counselors, and as anxiety and self-focus decrease, their ability to empathize increases.
There is agreement in the counseling profession that empathy is an important characteristic for counselors to embody in order to facilitate positive client outcomes and to meet counselor competency standards (DePue & Lambie, 2014). Yet scholars have grappled with how to identify the necessary skills to foster empathy in counselor trainees and remain torn on which approaches to use. Although empathy training programs seem effective, little is known about which aspects of such programs are the effective ingredients that promote empathy-building, and we lack understanding about whether such programs are more effective than simply engaging in clinical work or having life experiences. Thus, we encourage researchers to explore if counseling programs are effective at teaching empathy to CITs and to further explore mechanisms that may or may not be valuable in empathy development.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
Badenoch, B. (2008). Being a brain-wise therapist: A practical guide to interpersonal neurobiology. New York, NY:
W. W. Norton & Company, Inc.
Barrett-Lennard, G. T. (1986). The Relationship Inventory now: Issues and advances in theory, method, and use. In L. S. Greenberg & W. M. Pinsof (Eds.), The psychotherapeutic process: A research handbook (pp. 439–476). New York, NY: Guilford Press.
Batson, C. D., & Shaw, L. L. (1991). Evidence for altruism: Toward a pluralism of prosocial motives. Psychological Inquiry, 2(2), 107–122. doi:10.1207/s15327965pli0202_1
Bayne, H. B., & Jangha, A. (2016). Utilizing improvisation to teach empathy skills in counselor education. Counselor Education and Supervision, 55(4), 250–262. doi:10.1002/ceas.12052
Beder, J. (2004). Lessons about bereavement. Journal of Loss & Trauma, 9, 383–387. doi:10.1080/15325020490491014
Bell, H., Hagedorn, W. B., & Robinson, E. H. M. (2016). An exploration of supervisory and therapeutic relationships and client outcomes. Counselor Education and Supervision, 55(3), 182–197. doi:10.1002/ceas.12044
Bloom, Z. D., & Lambie, G. W. (in press). The Adolescent Measure of Empathy and Sympathy in a sample of emerging adults. Measurement and Evaluation in Counseling and Development.
Bohart, A. C., Elliott, R., Greenberg, L. S., & Watson, J. C. (2002). Empathy. In J. C. Norcross (Ed.), Psychotherapy relationships that work: Therapist contributions and responsiveness to patients (pp. 89–108). New York, NY: Oxford University Press.
Carré, A., Stefaniak, N., D’Ambrosio, F., Bensalah, L., & Besche-Richard, C. (2013). The Basic Empathy Scale in Adults (BES-A): Factor structure of a revised form. Psychological Assessment, 25, 679–691.
doi:10.1037/a0032297
Clark, A. J. (2004). Empathy: Implications of three ways of knowing in counseling. The Journal of Humanistic Counseling, Education & Development, 43, 141–151. doi:10.1002/j.2164-490X.2004.tb00014.x
Clark, A. J. (2010). Empathy and sympathy: Therapeutic distinctions in counseling. Journal of Mental Health Counseling, 32(2), 95–101. doi:10.17744/mehc.32.2.228n116thw397504
Connors, G. J., Carroll, K. M., DiClemente, C. C., Longabaugh, R., & Donovan, D. M. (1997). The therapeutic alliance and its relationship to alcoholism treatment participation and outcome. Journal of Consulting and Clinical Psychology, 65, 588–598. doi:10.1037/0022-006X.65.4.588
Council for Accreditation of Counseling and Related Educational Programs. (2016). 2016 Standards. Alexandria,
VA: Author.
Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology, 10, 85.
Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional approach. Journal of Personality and Social Psychology, 44, 113–126. doi:10.1037/0022-3514.44.1.113
Decety, J., & Ickes, W. (Eds.). (2009). The social neuroscience of empathy. Cambridge, MA: MIT Press.
DePue, M. K., & Lambie, G. W. (2014). Impact of a university-based practicum experience on counseling students’ levels of empathy and assessed counseling competences. Counseling Outcome Research and Evaluation, 5(2), 89–101. doi:10.1177/2150137814548509
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method (3rd ed.). Hoboken, NJ: Wiley.
Eisenberg, N., Eggum, N. D., & Di Giunta, L. (2010). Empathy-related responding: Associations with prosocial behavior, aggression, and intergroup relations. Social Issues and Policy Review, 4, 143–180.
doi:10.1111/j.1751-2409.2010.01020.x
Elliott, R., Bohart, A. C., Watson, J. C., & Greenberg, L. S. (2011). Empathy. Psychotherapy, 48, 43–49.
doi:10.1037/a0022187
Flasch, P. S., Limberg-Ohrt, D., Fox, J., Ohrt, J., Crunk, E., & Robinson, E. (in press). Experiences of altruistic caring by clients and their counselors in the counseling session. Counseling & Values Journal.
Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8th ed.). Boston, MA: Allyn & Bacon.
Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ for character, health and lifelong achievement. New York, NY: Bantam Books.
Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Ivey, A. E. (1971). Microcounseling: Innovations in interviewing training. Oxford, England: Charles C. Thomas.
Ivey, A. E., Normington, C. J., Miller, C. D., Morrill, W. H., & Haase, R. F. (1968). Microcounseling and attending behavior: An approach to prepracticum counselor training. Journal of Counseling Psychology, 15(5), 1–12.
Jolliffe, D., & Farrington, D. P. (2006). Development and validation of the Basic Empathy Scale. Journal of Adolescence, 29, 589–611. doi:10.1016/j.adolescence.205.08.010
Kirchberg, T. M., Neimeyer, R. A., & James, R. K. (1998). Beginning counselors’ death concerns and empathic responses to client situations involving death and grief. Death Studies, 22(2), 99–120.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
Konrath, S. H., O’Brien, E. H., & Hsing, C. (2011). Changes in dispositional empathy in American college
students over time: A meta-analysis. Personality and Social Psychology Review, 15, 180–198. doi:10.1177/1088868310377395
Krupnick, J. L., Sotsky, S. M., Simmens, S., Moyer, J., Elkin, I., Watkins, J., & Pilkonis, P. A. (1996). The role of a therapeutic alliance in psychotherapy and pharmacotherapy outcome: Findings in the National Institute of Mental Health Treatment of Depression Collaborative Research Program. Journal of Consulting and Clinical Psychology, 64, 532–539.
Lam, T. C. M., Kolomitro, K., & Alamparambil, F. C. (2011). Empathy training: Methods, evaluation practices, and validity. Journal of Multidisciplinary Evaluation, 7(16), 162–200.
Lambert, M. J., & Barley, D. E. (2001). Research summary on the therapeutic relationship and psychotherapy outcome. Psychotherapy: Theory, Research, Practice, Training, 38, 357–361. doi:10.1037/0033-3204.38.4.357
Lambert, M. J., & Bergin, A. E. (1994). The effectiveness of psychotherapy. In A. E. Bergin & S. L. Garfield (Eds.), Handbook of psychotherapy and behavior change (4th ed.; pp. 143–189). Oxford, England: John Wiley & Sons.
McGuire, A. M. (2003). “It was nothing”: Extending evolutionary models of altruism by two social cognitive biases in judgment of the costs and benefits of helping. Social Cognition, 21, 363–394.
doi:10.1521/soco.21.5.363.28685
McWhirter, B. T., Besett-Alesch, T. M., Horibata, J., & Gat, I. (2002). Loneliness in high risk adolescents: The role of coping, self-esteem, and empathy. Journal of Youth Studies, 5, 69–84. doi:10.1080/13676260120111779
Norcross, J. C. (Ed.). (2011). Psychotherapy relationships that work: Evidence-based responsiveness (2nd ed.). New York, NY: Oxford University Press.
Norcross, J. C., & Lambert, M. J. (2011). Psychotherapy relationships that work II. Psychotherapy: Theory, Research, Practice, Training, 48, 4–8.
Osborne, J. W. (2013). Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data. Thousand Oaks, CA: Sage.
Qualtrics. (2013). Qualtrics software (Version 37,892) [Computer software]. Provo, UT: Qualtrics Research Suite.
Ridley, C. R., Kelly, S. M., & Mollen, D. (2011). Microskills training: Evolution, reexamination, and call for reform. The Counseling Psychologist, 39, 800–824. doi:10.1177/0011000010378438
Rogers, C. R. (1957). The necessary and sufficient conditions of therapeutic personality change. Journal of Consulting Psychology, 21, 95–103. doi:10.1037/h0045357
Sachse, R., & Elliott, R. (2002). Process–outcome research on humanistic therapy variables. In D. J. Cain & J. Seeman (Eds.), Humanistic psychotherapies: Handbook of research and practice (pp. 83–115). Washington, DC: American Psychological Association.
Siegel, D. J. (2010). Mindsight: The new science of personal transformation. New York: Bantam.
Silva, N. W. (2001). Effect of empathy training on masters-level counseling students. (Unpublished doctoral dissertation). University of Florida, Gainesville, FL.
Spreng, R. N., McKinnon, M. C., Mar, R. A., & Levine, B. (2009). The Toronto Empathy Questionnaire: Scale development and initial validation of a factor-analytic solution to multiple empathy measures. Journal of Personality Assessment, 91, 62–71. doi:10.1080/00223890802484381
Stoltenberg, C. (1981). Approaching supervision from a developmental perspective: The counselor complexity model. Journal of Counseling Psychology, 28, 59–65. doi:10.1037/0022-0167.28.1.59
Stoltenberg, C. D., & McNeill, B. W. (2010). IDM supervision: An integrative developmental model for supervising counselors & therapists (3rd ed.). New York, NY: Routledge.
Szalavitz, M., & Perry, B. D. (2010). Born for love: Why empathy is essential—and endangered. New York, NY: Harper Collins.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed). Upper Saddle River, NJ: Pearson.
Teding van Berkhout, E., & Malouff, J. M. (2016). The efficacy of empathy training: A meta-analysis of randomized controlled trials. Journal of Counseling Psychology, 63, 32–41. doi:10.1037/cou0000093
Truax, C. B., & Carkhuff, R. R. (1967). Toward effective counseling and psychotherapy: Training and practice. Chicago, IL: Aldine Publishing.
Truax, C. B., & Lister, J. L. (1971). Effects of short-term training upon accurate empathy and non-possessive warmth. Counselor Education and Supervision, 10(2), 120–125. doi:10.1002/j.1556-6978.1971.tb01430.x
Vossen, H. G. M., Piotrowski, J. T., & Valkenburg, P. M. (2015). Development of the Adolescent Measure of Empathy and Sympathy (AMES). Personality and Individual Differences, 74, 66–71.
doi:10.1016/j.paid.2014.09.040
Watson, J. C., Steckley, P. L., & McMullen, E. J. (2014). The role of empathy in promoting change. Psychotherapy Research, 24, 286–298. doi:10.1080/10503307.2013.802823
Zachary D. Bloom is an assistant professor at Northeastern Illinois University. Victoria A. McNeil is a doctoral candidate at the University of Florida. Paulina Flasch is an assistant professor at Texas State University. Faith Sanders is a mental health counselor at Neuropeace Wellness Counseling in Orlando, Florida. Correspondence can be addressed to Zachary Bloom, 5500 North St. Louis Avenue, Chicago, IL 60625, z-bloom@neiu.edu.
Nov 9, 2018 | Volume 8 - Issue 4
Jennifer Barrow, Stanley B. Baker, Lance D. Fusarelli
The purpose of this grounded theory study was to understand and explain how training and work setting experiences influence readiness of professional school counselors for serving gang members in schools. A purposeful sample consisted of secondary school counselors (n = 5) and school leaders (n = 7) in a southeastern metropolitan school district. Blended themes from the counselors and leaders were: (a) professional development attitudes, (b) actual and potential roles when working with students in gangs, and (c) counselors’ collaborative role in discipline process. The Collaborative C.A.R.E. theory that emerged from the thematic analysis highlighted the absence of collaboration between school counselors and leaders. Specific findings identified reasons for the lack of collaboration and led to recommendations for practice and further research.
Keywords: gang members, school counselors, grounded theory, Collaborative C.A.R.E, discipline
On a daily basis, professional school counselors (PSCs) are expected to engage in a variety of functions in order to enhance the academic, career, personal, and social development of all students (American School Counselor Association [ASCA], 2012b, 2014). Serving all students can be very challenging given the disproportionate number of PSCs to students in the United States and the number of non-counseling functions often imposed on PSCs (Lambie & Williamson, 2004). ASCA (2012a) recommends a counselor-to-student ratio of 1:250. Despite this recommendation, findings have indicated that the accurate ratio is closer to 1:491 (ASCA, n.d.). Responding to the “serve all students” expectation can be even more challenging when attempting to serve gang members, who are considered members of marginalized populations that are excluded from the social, economic, cultural, and political mainstream (McCluskey, Baker, & McCluskey, 2005).
Research on the PSC’s role was conducted in the late 1990s and early 2000s, and much of the research is generalized to include the role of the PSC (both perceived and actual) with little consideration for the contextual differences in jobs (e.g., elementary, middle, high school; Brott & Myers, 1999; Lambie & Williamson, 2004). A paucity of data exists in recent research examining the role of PSCs with specific groups of students based on cultural and environmental contexts, and their role since the introduction of the ASCA National Model. Gang members are students with norms related to language, rituals, and membership (Gibbs, 2000). The presence of gangs in schools reflects a need to examine the role of the PSC in serving this culturally marginalized population.
Gang members are often viewed as outsiders associated with “outlaw organizations” engaged in deviant behaviors (Gibbs, 2000, p. 73). On the other hand, from the inside, members find structure, ritual, and norms specific to their gang structure. This study was designed to attempt to fill these gaps by examining the role of the PSC with a contemporary, marginalized population.
According to the National Gang Intelligence Center (2011), there are approximately 1.4 million active gang members representing more than 33,000 gangs in the United States. This represents a 40% increase compared to data collected in 2009. The data support an assumption that there is an increasing presence of gangs in both rural and urban communities (Brinson, Kottler, & Fisher, 2004). Unfortunately, there are several negative outcomes associated with the presence of gang members in the schools, including harassment, vandalism, aggressive recruitment of new members, irregular attendance, decreased motivation to succeed in school, and criminal activities. Consequently, gang presence can adversely affect the school environment, lower levels of academic achievement, and negatively influence perceptions of safety (Brinson et al., 2004). In and of itself, gang membership is not a crime, and gang members who are enrolled in public schools are eligible for all of the services that other students are receiving, including those offered by PSCs (Kizer, 2012).
As gang membership increases nationally, the presence of gang members will continue to expand in the schools and surrounding communities (Coggeshall & Kingery, 2001; Kingery, Coggeshall, & Alford, 1998). A recent survey of 12- to 18-year-old students indicated that 18% stated there were gangs in their schools (Robers, Kemp, Truman, & Snyder, 2013). This phenomenon will increase the exposure of PSCs to gang activity (Gündüz, 2012; Skovholt & McCarthy, 1988). Because of their training, PSCs appear to be in a unique position conceptually to offer services to gang members and to the schools where gang members are present. Potential resources include individual and group counseling competencies; core curriculum programming knowledge and skills; availability for providing helpful consultations; and the overlying quest to enhance the academic, career, personal, and social development of all students (ASCA, 2012b, 2014).
The first author’s exposure to gangs increased in her role as a PSC. Perceived lack of training and preparation to work with gang members and an absence of professional literature on the role of PSCs with gangs motivated the first author to conduct a preliminary investigation. Participants in the pilot study were PSCs in a southeastern urban public school setting. The pilot study consisted of two phases of inquiry consistent with the grounded theory methodology. Grounded theory generates data based on “participant experiences” (Hays & Singh, 2012, p. 288).
The first phase of the pilot study was a focus group of PSC participants with data being transcribed by the researcher, hand-coded, and analyzed. The second phase consisted of individual interviews completed at the respective job sites of three practicing PSCs. The interviews and observations from the second phase provided further evidence, more variation, and a greater understanding of the role of the PSC working with students in gangs across elementary, middle, and high school settings.
The preliminary investigation suggested further research in the school counseling domain. The participating PSCs appeared to experience ambiguity and lack of decision-making authority related to working with students who are gang members. Decisions on professional development opportunities and the PSC’s role were influenced by school-based leaders, such as principals, whose views tended to focus on disciplinary issues rather than academic, career, personal, and social development with regard to gang members. Consequently, the pilot study revealed a need to further explore the PSC’s role in working with gang members based on perceived and ideal roles, their professional development needs, and the influence of their educational administrators and supervisors.
Although uniquely positioned to offer something of value, there are impediments to fulfilling that role. Developing and defining the role for PSCs continues to be a challenge for PSCs, their school leaders (SLs), and national professional organizations that offer recommended roles for PSCs (Foxx, Baker, & Gerler, 2017; Griffin & Farris, 2010; Shoffner & Williamson, 2000). Some SLs determine the tasks that define the role of their PSCs with little to no input from counselors (Zalaquett & Chatters, 2012). These decisions are not aligned with ASCA’s PSC role recommendations and indicate misunderstandings about how their counselors were trained and failure to collaborate on PSC role definitions (Kirchner & Setchfield, 2005). Collaboration between PSCs and SLs is essential in the development of comprehensive counseling programs designed to support the academic goals of the school (Armstrong, MacDonald, & Stillo, 2010; Foxx et al., 2017; Zalaquett & Chatters, 2012).
An additional challenge is a lack of professional development related to working with gang members after one’s graduate training program. Caldarella, Sharpnack, Loosli, and Merrell (1996) found that many PSCs do not feel adequately trained or equipped to deal with gang activity and gang members in their schools, and almost half of the sample had no training related to gangs. Relatedly, our preliminary investigation found PSCs were trained to recognize the presence of gangs yet knew very little about how to engage with gang members and offer their services. Believing that one is unprepared and not competent to deliver counseling services to gang members may cause feelings of helplessness, apathy, and little or no desire to serve them (Ibrahim, Helms, & Thompson, 1983). As a marginalized population, students in gangs compound the unique challenges PSCs face, including role ambiguity (Burnham & Jackson, 2000), constant changes in student and school characteristics and needs (Reising & Daniels, 1983), and disconnects between training and practice (Brott & Myers, 1999; Lambie & Williamson, 2004).
The purpose of the present grounded theory study was to further understand and explain how training, perceived roles, and work setting experiences (e.g., professional development, working with students in gangs) influenced the readiness of PSCs in a large urban school district to serve gang members. Given the challenges PSCs experience related to serving gang members, the following research questions were derived in order to attempt to explain a conceptual linkage via a grounded theory based on understanding perspectives of a sample of PSCs and SLs via the interplay of context, conditions, and the PSC’s role (Hays & Singh, 2012): How do PSCs and SLs describe perceived and actual roles of PSCs regarding services to gang members? How do PSCs and SLs describe previous training related to working with gang members? and How do PSCs and SLs describe circumstances that influence opportunities PSCs have for serving gang members?
Method
Participants
A total of 12 participants were included in this study. Five participants were PSCs and seven were SLs. Of the PSCs, four were female and one was male; four were White and one was African American. All of the PSCs had master’s degrees and school counseling licenses. The mean age of the PSCs was 52 (SD = 8.57), and the mean years of counseling experience was 14.8 (SD = 7.69). All of the seven SLs were male. Six were White and one was African American. Four had master’s degrees in educational leadership, one had a bachelor’s degree in science, and two had doctoral degrees in education. Two of the seven SLs were based in the school district’s central office. The mean age of the SLs was 42 (SD = 7.23), and the average years of experience was 10.4 (SD = 3.26). Each participant is represented by a pseudonym in the findings.
Consistent with grounded theory, stratified purposeful sampling was used to identify PSCs and SLs serving at the same school to voluntarily participate (Corbin & Strauss, 2008; Hays & Singh, 2012). PSCs possessed state professional licenses in their fields and were employed in secondary school settings. Specific criteria for the SLs were that they be assistant principals, principals, or central office staff members. SLs possessed state professional licenses in their fields. An additional advantage of this approach was being able to triangulate data sources by acquiring data from different perspectives, including central school office members.
Instrumentation
Demographic questionnaire. Information related to age, ethnicity, education, and experience was collected from PSC and SL participants via a brief demographic questionnaire that asked identical questions.
Interview questions for participants. Two sets of open-ended questions were developed for semi-structured individual interviews. Topical areas addressed in the current study included role perception, professional development, and barriers to serving students in gangs. The following questions were presented to the PSC participants: (a) What factors determine the role you play in your school? (b) Who is involved in determining your professional role? (c) In your opinion, what role do professional school counselors currently play in identifying gang presence and providing intervention in your school? (d) Tell me what role you think counselors may play in identifying and providing interventions for students currently involved in a gang or considering gang membership. (e) What role has the school or school district played in providing professional school counselors with training specific to gang activity in the schools? (f) During your graduate school training, were you provided any opportunities to learn about gangs in schools? (g) Since graduate school, have you been provided or sought out opportunities to learn about gangs in schools? (h) In your own words, describe your work with students in gangs. (i) What barriers exist impacting your effectiveness in working with students in gangs? (j) In what ways do you seek out information to inform your work as a professional school counselor? (k) How might the ASCA National Model support your efforts to prevent or intervene with students in gangs? and (i) Is there anything you care to add?
The following questions were presented to the SL participants: (a) What factors determine the role school counselors play in your school? (b) Who is involved in determining their professional role? (c) In your opinion, what role do professional school counselors currently play in identifying gang presence and providing intervention in your school? (d) Tell me what role you think counselors may play in identifying and providing interventions for students currently involved in a gang or considering gang membership. (e) What role has the school or school district played in providing professional school counselors and school faculty with training specific to gang activity in the schools? (f) During your graduate school training, were you provided any opportunities to learn about the role of the school counselor? (g) Since graduate school, has your perception of the role of the school counselor changed? How so? (h) In your own words, describe your work with students in gangs. (i) How might the ASCA National Model support your school’s efforts to prevent or intervene with students in gangs? and (j) Is there anything you care to add?
Interviewer/Investigator. The first author was an insider who worked for the school district as a PSC. At the time of the study, she was working full-time and was a doctoral student in a counselor education program accredited by the Council for Accreditation of Counseling and Related Educational Programs. She was a 37-year-old White female with nine years of school counseling experience. She is a licensed school counselor, licensed professional counselor, and a National Certified Counselor (NCC). She had access to data that would not be available to an outsider. An advantage was her familiarity with the participants.
Subjectivity statement. On the one hand, the first author lacked personal gang awareness and was sensitive to the participants’ lack of knowledge (Corbin & Strauss, 2008). On the other hand, she had observed disruptive incidents created by gang members in her schools and was conflicted about how to deal with gang members as a professional. This led to a preliminary literature review that suggested ideas about how PSCs may serve gang members in their schools via both responsive services and core curriculum responses. The potential biases were role ambiguity and professional development. These biases were addressed during the data collection and analysis through the use of a journal to record immediate reactions to completed interviews.
Reflectivity during data collection is a valuable tool and is “considered essential to the research process” (Corbin & Strauss, 2008, p. 31). A journal housed field notes after each interview regarding participants’ body language, physical environment, and interviewer’s immediate thoughts and impressions. Journaling allowed for the constant comparison of data, looking for more data, and initial coding of collected data (Corbin & Strauss, 2008).
Procedure
Data collection. Established university research policies for the protection of human subjects and the research policies of the school district were followed in order to gain access to schools and participants. After receiving institutional review board approval from the first author’s affiliated university and the school district’s research department, data collection was completed via interviewing participants, journaling, and reviewing documents. The primary source of data was individual semi-structured interviews using an open-ended questions approach and an interview guide (Patton, 2002). Observations of the school setting, participants, and reflections of each interview were noted by the first author/researcher in her journal (Corbin & Strauss, 2008). In addition to journaling, policy manuals and public relations documents were accessed from the school district’s website for the triangulation process (Patton, 2002). The school district’s documents informed the researcher of existing procedures and policies and potential access to related training opportunities.
Participants were provided the interview questions in the moments immediately preceding the beginning of the interviews, giving them the opportunity to view questions and consider answers or emerging thoughts as needed. They were offered an opportunity to answer all questions. In order to enhance the analysis of the role of the PSC, interviews were conducted with SLs and PSCs working at the same schools. The interviews were conducted at the jobsites of the PSCs and SLs or at mutually agreeable locations. A digital voice recorder was used to record all interviews.
Data analysis. The recorded interviews were played and reviewed immediately after face-to-face interviews, allowing for constant comparisons (Schwandt, 2001). Each individual audio-recorded interview was transcribed by a professional transcriptionist. Following transcription, the interviews were read twice by the first author before themes were highlighted and noted in the margins. Interview data were individually read for all PSCs with themes noted in the margins. Then, interview data were individually read for all SLs with themes noted in the journal. Finally, interview data were reviewed for each PSC and their corresponding SL with themes of each pairing noted by the researcher. Hand-coding was used to analyze data gathered from transcribed interviews with a focus on capturing essential concepts (Bogdan & Biklen, 2007). The process of hand-coding involved deriving codes and the emerging themes to be organized into discrete categories leading to theory development (Corbin & Strauss, 2008). In the first or open coding stage, large general conceptual domains were identified in the reflective journal. Then, the researcher searched for relationships among the domains during the axial coding stage. Finally, the selective coding stage involved: (a) explaining story lines, (b) relating subsidiary categories around the core categories by means of paradigms, (c) relating categories at the dimensional levels, (d) validating the relationships against the raw data, and (e) filling in the categories that may need further development (Corbin & Strauss, 2008).
Triangulation was used as a means to increase the trustworthiness in the present study (Creswell & Miller, 2000; Patton, 2002). Four data sources were used to inform theory development: interviews with PSCs, interviews with SLs, a reflective journal, and related school district documents (e.g., discipline policies, in-service training programs). Grounded theory is built upon the cyclical and constant analysis of data (Hays & Singh, 2012). The use of multiple data sources in this study enhanced the development of codes, categories, and theory, and strengthened the trustworthiness of the study’s findings (Merriam, 2002). The transcribed interviews were reviewed by the researcher to ensure that professional jargon was accurate. A reflective research journal was kept throughout the entire study. Each participant was offered an opportunity to member check the transcribed data (Creswell & Miller, 2000). In addition, an audit was conducted to attempt to reduce the potential for personal biases influencing the data analysis. The auditor was a White female with a doctorate in educational leadership and previous work experience as a PSC. The auditing process consisted of quality control: (a) assuring ethical concerns were addressed, including the use of pseudonyms to protect participants; (b) reviewing the data to insure the study proposed and conducted matched data reported; and (c) proofreading, including clarifying professional jargon. Data saturation was achieved after the eighth interview; however, to affirm category development, complete interview pairings, and ensure triangulation of data sources, the interviews continued through 12 participants. As stated in the introduction, the purpose of the present study was to construct a grounded theory based on the data.
Findings
Grounded theory study data analyses provide central categories that bring all of the codes together (Corbin & Strauss, 2008). The central thematic categories in the present study were: (a) professional development attitudes, (b) actual and potential roles when working with students in gangs, and (c) PSCs’ collaborative role in the discipline process. An integration of the three central categories caused a Collaborative C.A.R.E. theory to emerge. Collaboration was the category both present and notably absent in the stories of the PSCs and the SLs. The C.A.R.E. acronym emerged out of the categories that developed during the axial coding process. The categories revealed a lack, or the presence, of communication with community stakeholders. The data suggested a need for PSCs working in secondary school settings to advocate for policies, procedures, programming, and educational opportunities to clarify their role in providing responsive services for students in gangs. What follows are excerpts of the data in the voices of the participants presented via the three central themes.
Professional Development Attitudes
PSCs are increasingly overwhelmed by their day-to-day responsibilities, leading them often to not engage in professional development that may take them away from campus. In addition, the interview data revealed that PSCs were not engaging in professional development related to working with gang members because of a lack of interest in working with this population, a concern for personal safety, unclear counseling roles, and the cost of professional development.
Beth (PSC) noted in her time as a PSC that different initiatives drive the training offered in the local district. She recalled a “push” four or five years previously to identify the presence of gangs at her school, but since that training she noted, “It’s not an interest of mine” and she will look to other staff members to “handle that stuff.” Beth’s response demonstrated a lack of engagement as a result of a lack of interest.
As noted, Beth expected other staff members, primarily SLs, to address the needs of students in gangs. In contrast, Sasha’s (PSC) gang awareness training at the school level had occurred in other counties. She noted that the school district in the present study “maybe has had something,” but “I don’t think the school has provided anything.” She went on to say, “I don’t think I’ve done anything in this district.” Sasha added that possessing knowledge of gangs in schools is “just not the highest on the list of priorities.”
Sasha’s supervising SL, Joe, noted the training from the district is “probably limited, to be honest.” Joe stated as an SL: “I don’t receive training for gangs or gang-related activity. Most of what I know is either self-taught or stuff that we pick up along the way because we’re placed into that position as administrators.” Joe elaborated that much of what he had picked up was reactive: “Unfortunately it’s reactive, but that’s also predicated upon the levels that we deal with here, which is not very much . . . so some of that [training] is from our SRO [school resource officer].”
Beyond having experienced awareness training, the PSCs expressed repeated concerns about their lack of intervention tools. Sasha said she was in need of “strategies” to work with gangs. She asked, “Are you working on trying to get them out of a gang or are you working on how do you cope with being part of a gang?” She followed with an insight: “it’s . . . how it’s affecting them in the school and so, generally, it leads to academics and attendance and if there are discipline issues or . . . . But it still has to have the school slant to . . . work with them.” Judy (PSC) concurred that training had “been mostly awareness and information,” and a lack of urgency to learn more left her deficient in skills and techniques to intervene.
Although awareness training appeared to be somewhat useful, specific prevention and intervention strategies were lacking in any of the training in which PSCs had previously participated. Stacey (PSC) stated that the limited training she received had been “one or two instances” consisting of “signs or signals.” Sasha noted she had not been trained to intervene, and she believed part of the problem was the nature of gangs because they may be “generational, and I don’t think anybody really knows how exactly [to] intervene. ” When speaking about the role of training, Judy quite frankly stated, “If you’re going to provide . . . training, does that imply that I then own the problem . . . if you’re training me, you’re giving me the problem and how am I supposed to solve it?”
Actual and Potential Roles When Working With Students in Gangs
The perceived and actual role of working PSCs has been studied extensively. Recommendations for serving students representing specific populations may vary (e.g., different ethnic groups, various exceptional populations, sexual minorities). On the other hand, ASCA (2014) is explicit in its petitioning provision of services to all students to address long-term goals and “demonstrate personal safety skills” (p. 2). The findings in this study suggest a possible actual role and provide ideas for a potential role for serving gang members.
Beth’s SL, Stan, said, “I would say they [PSCs] don’t really have a specific role in identifying gang presence” and “it wouldn’t be something that I would put under their job description.” Beth also noted that interactions with students in gangs were limited to an awareness that students may be involved with a gang because any intervention or interaction was something “that the assistant principals work with.” Stan’s comments mirrored those of his PSC. He stated, “If it’s a discipline issue, then it [the student issue] would stick with the administration.” Stan’s PSCs would be involved if the student needed “more of a counseling-type component where the student needs assistance or is seeking help from . . . the school.”
Sasha said she worked with students in gangs, but their gang affiliation was “not what we’re working on.” Beth agreed: “The thing is . . . if a kid is coming to you with a specific problem, you help them with that specific problem whether he’s a gang member or not.” Beth stated her actual role as a PSC limited her ability to interact because in her opinion, “if a kid was deeply entrenched in a gang, we’re not going to be able to get them out of that gang.” Derek (SL) agreed that the degree of involvement complicates the intervention because “once they reach a certain point, it is going to be very difficult—I’m not going to say impossible—but it’s going to be very difficult to get [them] back.”
Because the immediate need for a student to seek a PSC’s assistance was rarely, if ever, gang-related, Beth noted her form of intervention was about helping the students obtain their diplomas. Beth went on to say, “If he is here and attempting to get an education, behaving himself and not fighting . . . then my role would be to help him get what he needs from the school system as long as he is playing by our rules.” Her view of services for gang members seemed focused primarily on academic counseling.
PSCs’ Collaborative Role in Discipline Processes
Jake (SL) identified collaboration as a function in the PSC’s role when working with students in gangs, although he noted that the level of collaboration would be limited by the degree of the student’s gang involvement and its impact on the school environment. Jake stated, “I don’t know that they play a role in identifying gang issues unless somebody comes to them with a situation.”
Stacey, a PSC at Jake’s school, concurred with his assessment when she noted, “We don’t do a lot in identifying the gang presence . . . administration and the resource officer tend to be the ones dealing with that.” Stacey went on to say that addressing students in gangs was handled by administrators, and there was no communication with the PSCs about those students that may be involved in gangs. Communications related to students in gangs among SLs, PSCs, and teachers did not exist at Stacey’s school. She explained, “I can’t remember anyone here ever talking about making that kind of referral.”
Like Stacey, Trevor (PSC) did not expect referrals related to gang membership coming to him from teachers. The PSC participants reported that those students violating school policy were referred to administrators. Most referrals for confirmed concerns related to gang members based on attire or language were directed to the administrative teams if they came to the counseling office first. As a counterpoint, Trevor’s SL, Frank, stated, “I can’t say I’ve ever met a counselor I would trust to even give me that type of information.” He went on to say, “So I’m not very trusting of that [information coming from PSCs] at this point. I don’t think they’re [PSCs] involved.”
The degree of collaboration in the actual role of PSCs was mentioned frequently. There seemed to be a lack of collaboration and shortage of referrals from SLs to PSCs, especially when the student gang members had committed infractions leading to disciplinary consequences. When SLs disciplined gang members, there often was no follow-up with PSCs. The SLs in this sample seemed not to view PSCs as contributors to their disciplinary and safety maintenance functions. Because of their focus on safety and discipline issues when thinking about gang members, it seemed not to occur to the SLs that PSCs could contribute to the academic, career, personal, and social development of gang members via their traditional professional functions.
Limitations
Given the impact of the school calendar and its restricted timeline on data collection, it is possible the researcher was dependent upon acquiring participants from a limited population of busy professionals. Rather than relying on power analyses to determine the sample, qualitative researchers rely on evidence of data saturation, which may not have occurred in this study, to ensure sample sizes are sufficient. Further, qualitative researchers continue interviewing if repeated themes or codes are not present in the interviewing and follow emerging themes (Corbin & Strauss, 2008; Creswell & Miller, 2000; Marshall, Cardon, Poddar, & Fontenot, 2013). In the present study, the sample size was smaller than some sources recommend for grounded theory studies. Fortunately, obvious signs of saturation were noted after the eighth participant was interviewed.
Racial diversity was limited to one African American in each sub-sample. Gender diversity was not achieved in the SL sub-sample. Consequently, the voice of a female SL’s perspective was not present in this study because there were only five female site-based SLs in a district with 25 high schools. The lack of diversity might have impacted the lens by which they led or worked with marginalized populations. Meeting the age diversity selection criterion also was a challenge. The average age of the PSCs indicated that the views and experiences of younger professionals were understated. The extent of the participating PSCs’ exposure to the ASCA National Model (2012a) was not assessed in the demographic questionnaire. Consequently, recommendations promoted in the National Model such as serving all students; offering comprehensive school counseling programs; enhancing the academic, career, and personal/social development of students; and collaboration with stakeholders, may have been limited, therefore impacting their perceived and actual roles accordingly. Participants may have self-censored responses as a result of being interviewed by a school system colleague or by knowing that a colleague in their school with more power was also being interviewed. Utilizing a researcher without ties to the school district might have enhanced the responses. Having colleagues from the same school participate was an important component of the study, a limitation that had to be accepted in addition to the population and sample being limited to one school district.
Discussion
The perceived and ideal role of PSCs has been extensively studied; however, a search of the professional literature demonstrated a paucity of research on the role of PSCs with specific, marginalized student populations (e.g., exceptional children, homeless), and the present study was designed to address the work of PSCs with one such group (i.e., students in gangs). The researcher attempted to understand the participants’ perspectives related to how participating PSCs and SLs described their actual roles, their previous training, and opportunities for further training with regard to serving gang members.
Consistent with previous research (Burnham & Jackson, 2000; Ibrahim et al., 1983; Lambie & Williamson, 2004), the findings revealed a perceived role for the PSCs’ work with students in gangs as academically focused and reactive. PSC participants noted not knowing what counseling strategies to employ in order to assist students in gangs, implying there is no ideal role for PSCs within that domain. A lack of engagement in professional development, concerns for personal safety, unclear or absent roles for working with students in gangs, and, notably, a limited role imposed by SLs, negatively impacted their potential for working with gang members constructively.
Insights Based on the Circumstances That Led to the Study
As stated in the introduction, motivation to conduct the study was based on the first author’s limited previous professional experience with gang members, suggestions from a literature search, and results of a pilot study. The first author reported having observed the influence of disruptive gang members in her schools, leading to conflicted thoughts about how to serve them. Consistent with previous literature on role confusion (Burnham & Jackson, 2000; Lambie & Williamson, 2004), PSCs in the present study also seemed conflicted about serving student gang members, and SLs seemed to consider the role of PSCs from a limited perspective. The perspectives of the two sets of professionals were somewhat different because their respective broad professional goals differed. Although SLs were more likely to focus on maintaining order and ensuring safety for all students, PSCs were more likely to focus primarily on their own safety and secondarily on providing limited responsive services to gang members (Sindhi, 2013).
Consequently, when SLs considered the role of PSCs, their perspectives were narrowly focused on safety and disciplinary issues, and PSCs were not viewed as being expected or able to contribute to those goals. They were not prompted to consider the PSC’s role from a broader professional perspective, nor did they think of it (Cobb, 2014). It seemed as if most of the PSCs also responded from a safety perspective, feeling unprepared and unwilling to be involved in that kind of role, especially if it would involve discipline or attempting to get students to leave their gangs. Two PSCs (Sasha and Beth) mentioned providing limited responsive services if requested (i.e., personal issues and academic counseling) and if the students were behaving themselves. This finding also mirrored those of the pilot investigation that prompted this study.
Related contributions to the professional literature indicated dissonance about the perceived and actual roles of PSCs (Brott & Myers, 1999; Burnham & Jackson, 2000; Ibrahim et al., 1983; Lambie & Williamson, 2004). The findings in the present study were quite similar to those of Caldarella et al. (1996) almost two decades ago—that is, the PSCs did not feel adequately trained to work with gang members. And the attitudes expressed by the PSC participants in the findings mirrored the apathy about and disinterest in serving gang members reported by Ibrahim et al. (1983) over 30 years ago. Unfortunately, the findings highlighted apparently limited potential for PSCs to address the academic, career, personal, and social development needs of students in gangs in the targeted school district because of their current settings and frames of mind.
Implications for Professional School Counseling
Limited range of counselor services. Implementation of the ASCA National Model (2012b)throughout the school system represented in the present study apparently had little influence on the role PSCs played in serving gang members. Considerable interview content from PSCs and SLs seemed focused on safety and discipline issues rather than on the academic, career, personal, and social development of student gang members. Mention of providing academic services came from two of the PSCs. Limiting counseling services to academics alone does not fit into the proactive, “serve all students” framework supported by ASCA (2012b). The perception that PSCs are solely academic counselors may cause them to feel boxed in professionally, therefore limiting their ability to advocate for counseling services for students in gangs and causing them to determine over the course of their professional careers that their role is fixed and rigidly academically focused (Lambie & Williamson, 2004).
Insufficient training. Three of the PSCs reported lacking sufficient training as a barrier to their working with students in gangs. Four of the five PSCs had not received training related to working with students in gangs during their master’s degree programs. Two of the five reported attending workshops after graduate school, and the remaining three had not sought training. Training provided by the school district on gangs in schools was limited to enhancing awareness, and there was no coverage of counseling-based techniques designed to reach students in gangs. A significant obstacle to training was time away from work and the cost of attending training. Although obstacles to training were reported, there seemed to be an underlying sense of frustration about the training that had been offered. The training from the district and from professional conferences was designed to make school personnel aware of the presence of gangs in the schools. This perceived lack of training designed to intervene and engage in counseling services for students in gangs is consistent with Brott and Myers’ (1999) work noting the need for experiential learning to enhance professional autonomy. The participants reported not knowing what to do with students in gangs and wondering what the goals of the counseling relationships would be if students were involved with gangs. This limited response model appears to have negatively impacted the way that PSCs viewed gang members. Neither the PSCs nor the SLs wanted PSCs involved in a discipline-focused mode.
PSC collaboration and advocacy. The Collaborative C.A.R.E. grounded theory presented at the beginning of the findings section suggests that PSCs respond to the challenges presented above via collaborating with others in their educational communities to advocate for policies, procedures, programs, and educational opportunities designed to clarify their role in providing responsive services to students in gangs. Although PSCs will benefit from more informed policies and richer educational opportunities, they also have advocacy competencies acquired in their training programs that should be of value when serving all students, including gang members. It appears as if the best way to serve students in gangs is through targeted responsive services designed to remove barriers and promote equitable access to counseling services (Trusty & Brown, 2005). Fortunately, most PSCs will not have to work differently in order to work with students in gangs via these approaches. Therefore, it appears as if the major changes needed are attitudinal. Believing that students in gangs deserve their services and advocacy efforts and can be served through existing services and competencies is essential. Overcoming safety concerns seems to be a very important goal. Students in gangs are members of a unique cultural group and equally worthy of positive regard and empathy. Becoming familiar with the nuances of this culture also seems to be an important goal for PSCs.
PSCs are challenged to be able to approach counseling sessions with student gang members in the same way as any other student client. Sasha noted she had not been trained to intervene with gang members; however, she likely is capable of building empathic relationships and aiding in goal setting and future planning for all student clients. The challenge might be to accept gang members as they are and attempt to help them focus on something of value that they want to be in their future and attempt to help them achieve those goals.
Recommendations for Practice and Research
Training preparation recommendations. The role of the PSC is continuously evolving via numerous influences, such as changing school policies and new initiatives at the local, state, and federal levels. Over their professional careers, PSCs may see a shift in the issues their students bring to the counseling relationship. For example, 15 years ago, PSCs were not dealing with cyberbullying. Cultural and economic shifts lead to changes in the issues students are forced to address, and changes in the lives of the students challenge PSCs to expand their expertise in order to be more effective practitioners. PSCs should be offered and encouraged to attend training based on a variety of issues impacting their work with 21st-century students, including enhancing the academic, career, personal, and social development of gang members.
As PSCs prepare to respond to evolving issues and shifting demographics, graduate training programs are challenged to provide instruction to prepare future PSCs for the realities of school settings and the diverse populations served. By no means can graduate training programs prepare graduate-level students for all of the nuances of practicing in a school; however, a careful review of the populations being served in 21st-century schools may guide the development of training modules designed to work with unique populations, including students in gangs. A training module of this type also can be developed and implemented in school districts in order to provide professional development for practicing PSCs.
Research recommendations. The paucity of research related to students in gangs and school counseling provides rich opportunities for future studies that might include examining the professional development needs of PSCs, addressing personal safety concerns, and exploring the impact of school-based stakeholders on the self-efficacy of PSCs. Until PSCs feel secure in the role they were trained to fill, they may continue to accept the non-counseling roles often expected by SLs and experience low levels of self-efficacy in working with diverse populations, including students in gangs (Dahir, Burnham, & Stone, 2009).
Responsive services address the immediate needs and concerns of students and incorporate both direct and indirect service modes (ASCA, 2012b). Further research involving responsive services may address the following questions: How is role development impacted by existing procedures and policies? How is the role of PSCs different in districts with procedures for addressing the needs of students in gangs versus districts lacking the same procedures? How effective are PSCs who collaborate with their communities when working with gang members?
Of all the research needs regarding students in gangs, knowledge acquired from the gang member’s perspective seems most needed. Without gang members as participants, the voice of students in gangs will continue to be silent. Studying students in gangs in order to understand how school staff can enhance their development may provide valuable information for both responsive and core curriculum services that can be provided by PSCs.
Conclusion
ASCA’s National Model (2012b) advocates for comprehensive school counseling programs designed to serve all students. Gang members are a unique student culture to be included within the “all students” framework and can benefit from school-based counseling services designed to enhance their academic, career, personal, and social development. Unfortunately, the findings in the present study revealed that there are impediments preventing PSCs from serving gang members. It seems as if the PSCs in the present study lacked role clarity in working with students in gangs, and there was a lack of intervention-based professional development. Not serving students in gangs led PSCs to believe they have nothing to offer those students through traditional counseling services, and this lack of efficacy may impact their role as advocates. Although this study was limited to one school district, the experiences and perceptions of PSCs and SLs in this study might not be unique. PSCs are uniquely trained and strategically located in school settings to provide valuable services to gang members that can help them feel accepted for who they are at the moment, while also helping them to focus on finding a meaningful pathway to their futures.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
American School Counselor Association. (2012a). Position statement: The school counselor and comprehensive school
counseling programs. Alexandria, VA: Author.
American School Counselor Association. (2012b). The ASCA National Model: A framework for school counseling
programs (3rd ed.). Alexandria, VA: Author.
American School Counselor Association. (2014). Mindsets and behaviors for student success: K-12 college- and
career-readiness standards for every student. Alexandria, VA: Author.
American School Counselor Association. (n.d.). Student-to-school-counselor ratio 2015-2016. Retrieved
December 18, 2018, from https://www.schoolcounselor.org/asca/media/asca/home/Ratios15-16.pdf
Armstrong, S. A., MacDonald, J. H., & Stillo, S. (2010). School counselors and principals: Different perceptions
of relationship, leadership, and training. Journal of School Counseling, 8(15), 27.
Bogdan, R. C., & Biklen, S. K. (2007). Qualitative research for education: An introduction to theory and methods (5th
ed.). New York: Allyn and Bacon.
Brinson, J. A., Kottler, J. A., & Fisher, T. A. (2004). Cross-cultural conflict resolution in the schools: Some
practical intervention strategies for counselors. Journal of Counseling & Development, 82, 294–301.
doi:10.1002/j.1556-6678.2004.tb00313.x
Brott, P. E., & Myers, J. E. (1999). Development of professional school counselor identity: A grounded theory.
Professional School Counseling, 2, 339–348.
Burnham, J. J., & Jackson, C. M. (2000). School counselor roles: Discrepancies between actual practice and
existing models. Professional School Counseling, 4, 41–49.
Caldarella, P., Sharpnack, J., Loosli, T., & Merrell, K. W. (1996). The spread of youth gangs into rural areas: A
survey of school counselors. Rural Special Education Quarterly, 15(4), 18–27.
doi:10.1177/875687059601500404
Cobb, N. (2014). Climate, culture and collaboration: The key to creating safe and supportive schools.
Techniques: Connecting Education & Careers, 89(7), 14–19.
Coggeshall, M. B., & Kingery, P. M. (2001). Cross-survey analysis of school violence and disorder. Psychology in
the Schools, 38(2), 107–116.
Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded
theory (3rd ed.). Thousand Oaks, CA: Sage.
Creswell, J. W., & Miller, D. L. (2000). Determining validity in qualitative inquiry. Theory Into Practice, 39, 124–
130. doi:10.1207/s15430421tip3903_2
Dahir, C. A., Burnham, J. J., & Stone, C. (2009). Listen to the voices: School counselors and comprehensive
school counseling programs. Professional School Counseling, 12, 182–192.
doi:10.1177/2156759X0901200304
Foxx, S. P., Baker, S. B., & Gerler, E. R., Jr. (2017). School counseling for the twenty-first century (6th ed.). New
York, NY: Routledge/Taylor & Francis Group.
Gibbs, J. T. (2000). Gangs as alternative transitional structures: Adaptations to racial and social marginality in
Los Angeles and London. Journal of Multicultural Social Work, 8(1/2), 71–99. doi:10.1300/J285v08n01_04
Griffin, D., & Farris, A. (2010). School counselors and collaboration: Finding resources through community
asset mapping. Professional School Counseling, 13, 248–256. doi:10.1177/2156759X1001300501
Gündüz, B. (2012). Self-efficacy and burnout in professional school counselors. Educational Sciences: Theory &
Practice, 12, 1761–1767.
Hays, D. G., & Singh, A. A. (2012). Qualitative inquiry in clinical and educational settings. New York, NY: Guilford.
Ibrahim, F. A., Helms, B. J., & Thompson, D. L. (1983). Counselor role and function: An appraisal by consumers
and counselors. Personnel and Guidance Journal, 61, 597–601. doi:10.1111/j.2164-4918.1983.tb00004.X
Kingery, P. M., Coggeshall, M. B., & Alford, A. A. (1998). Violence at school: Recent evidence from four
national surveys. Psychology in the Schools, 35, 247–258.
doi:10.1002/(SICI)1520-6807(199807)35:3<247::AID-PITS5>3.0.CO;2-K
Kirchner, G. L., & Setchfield, M. S. (2005). School counselors’ and school principals’ perceptions of the school
counselor’s role. Education, 126, 10–16.
Kizer, K. (2012). Behind the guise of gang membership: Ending the unjust criminalization. DePaul Journal for
Social Justice, 5, 333–366.
Lambie, G. W., & Williamson, L. L. (2004). The challenge to change from guidance counseling to professional
school counseling: A historical proposition. Professional School Counseling, 8(2), 124–131.
Marshall, B., Cardon, P., Poddar, A., & Fontenot, R. (2013). Does sample size matter in qualitative research? A
review of qualitative interviews in IS research. Journal of Computer Information Systems, 54, 11–22.
doi:10.1080/08874417.2013.11645667
McCluskey, K. W., Baker, P. A., & McCluskey, A. L. A. (2005). Creative problem solving with marginalized
populations: Reclaiming lost prizes through in-the-trenches interventions. Gifted Child Quarterly, 49,
330–341. doi:10.1177/001698620504900406}
Merriam, S. B. (2002). Assessing and evaluating qualitative research. In S. B. Merriam (Ed.), Qualitative research
in practice: Examples for discussion and analysis (pp. 18–33). San Francisco: Jossey-Bass.
National Gang Intelligence Center. (2011). National gang threat assessment: Emerging trends. Retrieved from
https://www.fbi.gov/file-repository/stats-services-publications-2011-national-gang-threat-
assessment-2011%20national%20gang%20threat%20assessment%20%20emerging%20trends.pdf/view
Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks, CA: Sage.
Reising, G. N., & Daniels, M. H. (1983). A study of Hogan’s model of counselor development and supervision.
Journal of Counseling Psychology, 30, 235–244. doi:10.1037/0022-0167.30.2.235
Robers, S., Kemp, J., Truman, J., & Snyder, T. D. (2013). Indicators of school crime and safety: 2012 (NCES No.
2013-036/NCJ No. 241446). Washington, DC: National Center for Education Statistics, U.S. Department
of Education, and Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice.
Schwandt, T. A. (2001). Dictionary of qualitative inquiry (2nd ed.). Thousand Oaks, CA: Sage.
Shoffner, M. F., & Williamson, R. D. (2000). Engaging preservice school counselors and principals in dialogue
and collaboration. Counselor Education and Supervision, 40(2), 128–140.
doi:10.1002/j.1556-6978.2000.tb01244.x
Sindhi, S. A. (2013). Creating safe school environment: Role of school principals. Tibet Journal, 38(1–2), 77–89.
Skovholt, T. M., & McCarthy, P. R. (1988). Critical incidents: Catalysts for counselor development. Journal of
Counseling & Development, 67, 69–72. doi:10.1002/j.1556-6676.1988.tb02016.X
Trusty, J., & Brown, D. (2005). Advocacy competencies for professional school counselors. Professional School
Counseling, 8, 259–265.
Zalaquett, C. P., & Chatters, S. J. (2012). Middle school principals’ perceptions of middle school counselors’
roles and functions. American Secondary Education, 40(2), 89–103.
Jennifer Barrow, NCC, is an assistant professor at North Carolina Central University. Stanley B. Baker is a professor at North Carolina State University. Lance D. Fusarelli is a professor at North Carolina State University. Correspondence can be addressed to Jennifer Barrow, 700 Cecil Street, Durham NC 27707, jbarrow4@nccu.edu.
Oct 21, 2018 | Volume 8 - Issue 4
Vernell Deslonde, Michael Becerra
This study utilized a qualitative dominant crossover mixed analysis that examined why school counselors (N = 38) choose or do not choose to use Naviance—an online college, career, and financial planning tool. The study further explored whether school counselors’ acceptance and use of Naviance enhances counseling practices, job productivity, and efficiency. The Technology Acceptance Model (TAM) was used for the theoretical framework. TAM is comprised of four constructs: perceived ease of use, perceived usefulness, attitudes, and actual behaviors. Bandwidth, training, and connectivity influenced some counselors’ attitudes toward usage and productivity; however, overall attitudes toward Naviance were positive. Future research should explore the connection between counselor usage and the number of hours trained on Naviance.
Keywords: school counselors, Technology Acceptance Model, TAM, Naviance, qualitative dominant crossover mixed analysis
New technologies are pervasive in the counseling profession. School counselors are experiencing a growing field of technologies that include virtual counseling platforms, smartphone applications, and learning management systems that provide the ability to see students face-to-face, quickly access information through an application, and offer high school students resources and information, ultimately assisting in the school-to-work transition. Additionally, the value of integrating new technologies into practice to support counselor growth as well as student outcomes is recognized in the education field. Many researchers believe that online technologies are effective educational tools (Serdyukov, 2017; Sung, Chang, & Liu, 2016; Tarhini, Hone, & Liu, 2015; Teo, 2011).
According to the Condition of Education 2017 report, in 2013–2014, K–12 schools spent $634 billion integrating technology to support academic achievement (National Center for Education Statistics, 2017). The bulk of the cost has been on purchasing equipment, integrating hardware and software, and training staff personnel. Despite the promise and financials spent, the lack of user acceptance is a barrier to the success of integrating new technologies (Blanchard, Prior, Barton, & Dawson, 2016; Davis, 1993; Tarhini et al., 2015; Teo, 2011). Without user acceptance, the value of the technology diminishes. Alternatively, increased technology acceptance can enable educators, including school counselors, to become effective with transferring knowledge, preparing and advancing student outcomes (Hu, Clark, & Ma, 2003), and enhancing counseling practices (Hayden, Poynton, & Sabella, 2008; Steele, Jacokes, & Stone, 2014).
Numerous theoretical models have been developed to investigate users’ acceptance of new technologies. The most widely researched model on user acceptance that investigates why a user chooses to use or not to use technology is the Technology Acceptance Model (TAM; Davis, 1993; Nair & Das, 2011; Tarhini et al., 2015; Teo, 2011). TAM predicts the level of technology acceptance and usage. Few studies exist on TAM within the context of K–12 schools and even fewer on the school counseling profession (Tri Anni, Sunawan, & Haryono, 2018). Utilizing TAM as a guiding framework, this research extends and advances knowledge on factors that influence school counselors’ acceptance and use of technologies, specifically Naviance, an online college, career, and financial planning counseling platform.
School Counselors’ Technology Acceptance
Perceived Ease of Use
Research has indicated that individuals are more likely to accept and use new technology if they perceive the technology as easy to use (Davis, 1993; Nair & Das, 2011; Saade & Bahli, 2005). Perceived ease of use is determined when a user believes that using a system is free of effort (Nair & Das, 2011;
Tarhini et al., 2015). Previous studies reveal common themes of perceived ease of use of certain technologies in the school counseling profession. For example, many school counselors perceive that sending email communication, creating multimedia presentations and webpages, developing newsletters, and retrieving information from schools’ student information systems are relatively easy functions (Carey & Dimmitt, 2004; Carlson, Portman, & Bartlett, 2006; Kozlowski, Mikesina, & Genova, 2015; Loague, Alexander, & Reynolds, 2010; Steele et al., 2014; Van Horn & Myrick, 2001). Today, many school counselors find it easy to retrieve counseling-related information from the internet and create targeted presentations for students. Further, school counselors perceive that delivering counseling curriculum, disseminating information, and administering needs and career assessments require minimal effort (Hayden et al., 2008; Holcomb-McCoy, Gonzalez, & Johnston, 2009; Loague et al., 2010; Millsom & Bryant, 2006; Steele et al., 2014).
School counselors have found certain types of technology easier to use. For example, in a quantitative study, Carlson et al. (2006) investigated how school counselors use technology and their comfort level. The results indicated that counselors felt comfortable or somewhat comfortable (92.7%) utilizing certain types of technology and software, such as desktop computers, VCRs and monitors, overhead projectors to create visual presentations, and Microsoft Word and Microsoft PowerPoint, as additional resources. However, most school counselors (76.9%) experienced low comfort levels and felt anxious or somewhat anxious using new software.
Perceived Technology Usefulness
Technology acceptance also is influenced by perceived usefulness. Perceived usefulness is determined by a user’s belief that a type of technology enhances job performance (Tarhini et al., 2015). Although a
reasonable amount of literature exists on how school counselors use technology in the counseling profession (Carlson et al., 2006; Hayden et al., 2008; Steele et al., 2014), little exists on perceived usefulness (Tri Anni et al., 2018). Tri Anni et al. (2018) surveyed school counselors in Indonesia and found that counselors who perceived that technology was easy to use were more likely to determine that the technology was useful. However, Tri Anni et al.’s study did not focus on a specific type of technology such as Naviance to determine whether such a tool enhances job effectiveness.
In another study, Steele et al. (2014) surveyed school counselors and found that many (45%) remained neutral when asked whether the advantages of online communication in their counseling practice outweighed the disadvantages. Furthermore, 61% felt slightly or not comfortable at all using online technology to perform counseling duties. When asked specifically about using Skype and other synchronous online communication technologies, researchers found a positive correlation among counselors’ level of training and comfort.
Attitudes Toward Technology Use
Guzman and Nussbaum (2009) argued that merely acquiring the hardware or software is insufficient to integrate technologies and therefore stressed the importance of the user’s attitude. The more positive the attitude about technology, the higher the actual usage (Teo, 2011). Several researchers have found school counselors’ attitudes toward the use of technology to be mostly positive, but lower when new technologies are introduced (Carlson et al., 2006; Rainey, Mcglothlin, & Miller, 2008; Steele et al., 2014).
It is important to note that there are external forces that shape a person’s perceived ease of use and usefulness of technology, and these forces may negatively affect attitudes. Such barriers include limited training on new software, age of the user, bandwidth challenges, slow data access, time delays in downloading content, and limited equipment (Carlson et al., 2006; Guzman & Nussbaum, 2009; Hu et al., 2003; Lederer, Maupin, Sena, & Zhuang, 2000; Steele et al., 2014). Moreover, large counselor caseloads might be a barrier to perceived ease of use and usefulness. For example, counselors working in states with higher caseloads may perceive that learning new technological software while managing higher caseloads and trying to capture large amounts of student information can be difficult.
Naviance: An Online College Career and Financial Planning Tool
Although many school counselors and students have used Naviance for more than a decade, a Google Scholar search revealed only one study in which the authors explored the relationship between the number of times that students visit Naviance and increased college application rates (Christian, Lawrence, & Dampman, 2017). Naviance is an online college and career readiness tool developed by Hobsons (Hobsons, 2017). According to Hobsons’ website, “more than 10 million students rely on Naviance to achieve key readiness milestones and answer critical questions such as: Who am I? What do I want to be? How will I get there? and Will I be successful?” (Hobsons, 2017). From a college and career counseling perspective, Naviance is used by middle and high school counselors and personnel to support and track student progress. Some of the features in Naviance include course planning; postsecondary planning; career inventories; career and college searches; college majors; college applications; test preparation (SAT, ACT, and Advanced Placement); college enrollment; and 28 curriculum lessons in college, career, and financial planning.
TAM
Technology acceptance and adoption is well documented in the literature. Although several factors influence the acceptance and use of technologies, TAM, grounded in Fishbein and Ajzen’s (1975) research on beliefs, attitudes, and behaviors, indicates that perceived usefulness and perceived ease of use predict attitudes and actual behaviors (Davis, 1993; Nair & Das, 2011). Essentially, TAM captures the user’s overall attitude toward online technologies.
Davis (1993) hypothesized that one’s attitude toward using technology is a function of two beliefs: perceived ease of use and perceived usefulness. Perceived ease of use is the degree to which a person believes that using the system would require minimal effort, whereas perceived usefulness is the extent to which the information system enhances job performance (Lederer et al., 2000). Two other constructs of TAM are a person’s attitude toward the use of the system (which is the user’s desire to employ the system) and behavioral intention (which is the likelihood that a person will use the system; Davis, 1993; Lederer et al., 2000). Scholars have argued that perceived ease of use of the technology and perceived usefulness determines one’s attitude toward a new technology (Davis, 1993; Padmavathi, 2016; Teo, 2011), such as Naviance.
Purpose of the Study
The purpose of this study was two-fold. First we sought to investigate if school counselors utilized Naviance. Second, we examined how Naviance usage enhances middle and high school counselors’ practices, productivity, and efficiency. Although many school counselors integrate technology into their practice (Kozlowski et al., 2015; Reljic, Harper, & Crethar, 2013; Steele et al., 2014), few studies address whether school counselors accept new technologies, as well as examine attitudes and actual usage. TAM provides the theoretical framework to understand school counselors’ acceptance and use of Naviance. To shed light onto the phenomenon, the following research questions guided this study: (a) Do school counselors choose to use or not choose to use Naviance; and (b) how does Naviance acceptance and usage enhance school counseling practices in terms of productivity and efficiency?
Methods
Data sources collected for this qualitative dominant crossover mixed analysis study included a survey questionnaire, face-to-face semi-structured interviews, and Naviance staff usage and engagement reports. According to Onwuegbuzie and Teddlie (2003), the benefits of a crossover mixed analysis include the ability to compare, correlate, and integrate quantitative and qualitative findings to describe the phenomenon. This type of qualitative dominant crossover mixed analysis takes into consideration a qualitative stance with quantitative data that provides additional detail to the study (Frels & Onwuegbuzie, 2013; Onwuegbuzie, Leech, & Collins, 2011). Ross and Onwuegbuzie (2010) grouped quantitative analyses according to difficulty, starting at the basic, descriptive level 1, and reaching as high as level 8, which includes multidirectional and multilevel analyses like multilevel structural equation modeling. In this study, the researchers used a level 1 quantitative analysis, which includes descriptive data taken from usage and engagement reports, and percentages from the questionnaire to determine productivity and efficiency.
Participants
A purposeful and convenience sample was utilized for this study. Purposeful sampling is used to identify and select individuals who are knowledgeable about a phenomenon (Palinkas et al., 2015), whereas convenience sampling is beneficial when participants are easily accessible and in close geographic proximity (Etikan, Musa, & Alkassim, 2016). The first researcher purposefully sought out middle and high school counselors who worked in close proximity and use Naviance in their role, from 14 public schools within the southwestern part of the United States. The first researcher sent an email to 48 potential participants. Of the 48 participants contacted, 38 school counselors agreed to participate, of which 10 were male and 28 were female. Twelve counselors worked at the middle school level and 26 at the high school level. All participants held a master’s degree and Pupil Personnel Service credential. Counselors ranged in age from 25 to 51. The age range for 55% of the school counselors was 25–44 years, whereas the remaining 45% age range was 45–51 years.
School District and Research Team
The school district implemented Naviance in 2014. The Naviance technology was given a low to medium priority, with the expectation that school counselors would at least minimally use the technology. The Naviance implementation occurred over a 3-year period. In the first year, two middle and two high schools implemented Naviance. In the second year, three additional high schools, two alternative high schools, and two additional middle schools launched Naviance, and during the final year, the remaining three middle schools rolled out the technology tool. Also in the third year, all Advanced Placement (AP) teachers were trained on Naviance AP test prep at each high school. Counselors and select school personnel received two full-day trainings on Naviance during each implementation year and Webex trainings were offered quarterly to those who needed a refresher on Naviance features and functionalities. In addition, professional development was offered to counselor groups upon request.
The first researcher works at the district office and provides monthly professional development to school counselors; however, the first researcher does not supervise the school counselors. Further, there are multiple layers of supervision that remove the first researcher from the day-to-day interactions of school counselors; the first researcher does not sign the performance evaluations of counselors, thereby preventing the first researcher from being able to use knowledge obtained from this study to negatively affect the participants. The second researcher works at a university in Texas as an adjunct faculty member. The first researcher identifies as African American and the second as Afro-Latino, with a mean age of 45. The first researcher is female and the second is male. Neither researcher has received financial assistance to conduct this study from Hobsons or its affiliates.
Instruments
Survey questionnaire. The TAM electronic questionnaire, first developed by Davis (1993) and validated in different contexts by several researchers (Nair & Das, 2011), consisted of 17 questions, of which 13 were on a 5-point Likert-type scale questionnaire, with the scale ranging from 1 (strongly agree) to 5 (strongly disagree). Also included in the survey questionnaire was demographic information (questions 1–3). To explore the research question, survey questions 4–15 asked about the extent to which Naviance was easy to use (4 questions); whether Naviance enhanced middle and high school counselors’ counseling practices, job productivity, and efficiency (4 questions); if Naviance was useful (2 questions); and attitudes toward using Naviance (2 questions). Question 16 was open-ended and regarded counselors’ overall attitude toward using Naviance, and the last question asked participants to indicate the frequency that they use Naviance (1 = daily, 2 = weekly, 3 = monthly, 4 = at least every other month, or 5 = not at all). Validation of the survey questions was established through a school counseling professional, who is a researcher, university faculty, and a retired school counselor of 30 years. Both researchers had combined experience of more than 30 years in counseling.
Interviews. Face-to-face, semi-structured interviews were another source of data for this study to help answer both research questions. The researchers used TAM and the survey questionnaire to construct 10 interview questions. The 10 interview questions centered on usefulness, ease of use, attitudes, and whether Naviance helped to enhance school counseling practices, job productivity, and efficiency. To ascertain ease of use, the first two interview questions focused on which of the functionalities in Naviance were the easiest to navigate and which data visualization features were easy to decipher. Questions 3 and 4 investigated how Naviance enhanced the role of school counselors and the benefits of using Naviance to engage multiple stakeholders. Interview questions 5–8 examined the ways that Naviance increases job effectiveness, efficiency, and productivity. The remaining questions explored whether Naviance was worthwhile and integration challenges.
Validation of the interview questions were by an expert panel of doctoral-level professionals in the fields of education and school counseling. Two members of the panel have been school principals and district personnel for more than 20 years combined. The third expert panelist is a university faculty member and retired school counselor. The first researcher sent the interview questions to the expert panel via email and requested feedback. One of the experts suggested that the researchers add a definition for perceived ease of use and perceived usefulness for the participants as part of interview questions two and three, which the first researcher subsequently incorporated. The second expert suggested that the researchers incorporate the language middle and high school counselor as part of the purpose of the study in the interview script rather than school counselor, which the first researcher included. The third expert did not offer additional suggestions.
Archival materials. To further help address the second research question, the researchers used the Naviance staff usage and engagement reports as a secondary data source. Specifically, the staff usage report showed the number of times that school counselors had accessed Naviance since implementation. In addition, the engagement reports showed the features in Naviance school counselors use to support the academic, college, and career development of students.
Procedure
The first researcher sent an email invitation along with a Qualtrics link for the TAM questionnaire to 48 middle and high school counselors to participate in this study. The survey remained open for 10 business days. Within that timeframe, 38 middle and high school counselors consented to participate in this study. After the survey closed, the first researcher sent an email to all 48 counselors inviting those who completed the survey to participate in face-to-face interviews. Of the 38 counselors who completed the study, 10 consented (three middle and seven high school counselors) to participate in the face-to-face interviews. The first researcher told participants that the interviews would be digitally recorded, they could withdraw any time, and their demographic information and personal identities would remain confidential. The first researcher conducted 10 separate interviews, which lasted on average 33 minutes.
After transcription of the interviews by rev.com, an online transcription company, each participant received a copy of the transcript to review and offer feedback within five business days. At the close of the five business days and with no changes suggested from participants, the first researcher deleted information that could identify participants and emailed the interview and Naviance staff and engagement data, which was retrieved at the district level, to the second researcher. The use of video conference calls as a virtual workspace was useful in collectively reading over transcripts, developing and comparing coding, and discussing themes.
Trustworthiness Procedures
To ensure trustworthiness and credibility of the study, the researchers used the process of triangulation and member checking to strengthen construct validity during the data collection process. The selection of triangulation allowed the researchers to collect data using a combination of sources to incorporate multiple perspectives on technology use and integration. Although archival materials (e.g., school counselor usage and engagement reports) did not require insight from the participants to increase the researchers’ understanding because of their pre-existing nature (Yin, 2014), the materials were instrumental in authenticating information from the interviews and were determined to be a valued data source. Another method used to strengthen trustworthiness was member checking. The first researcher separately emailed each participant, asking them to review the interview transcriptions to check for accuracy and offer feedback. Each participant replied within the 5-day timeframe indicating no corrections or feedback were necessary.
Data Analysis
The process of thematic analysis guided this study, which involved identifying patterns, insights, or concepts in the data that help to explain why those patterns are there (Bernard & Ryan, 2010). Both researchers used the process of open and axial coding, which involved breaking apart each data source, and deductive coding, which uses a top-down approach making connections and categorizing themes under TAM (i.e., perceived ease of use, perceived usefulness, attitudes, and actual usage). After reviewing themes from both researchers, there was absolute agreement about themes and codes.
The researchers followed the six phases of thematic analysis described by Clarke and Braun (2013), which included (a) familiarization of the data; (b) generation of initial codes; (c) identification of themes; (d) review themes; (e) define and name themes; and (f) produce the report. First, the researchers read through each line of the transcript several times to become familiar with content and understand perceptions regarding the usefulness, ease of use in using Naviance, and attitudes. Second, the researchers generated initial codes. Open coding allowed the researchers to break apart and group the data, and axial coding allowed the researchers to make connections to the data once it was categorized (Bernard & Ryan, 2010).
Next, the researchers categorized themes according to TAM from the transcribed interviews. TAM served as a priori themes, which related to the research questions as well. Themes capture important data about the research questions (Clarke & Braun, 2013) and explore patterns (Alhojailan, 2012). To help sort through the data to identify potential themes and the relationship between the codes, the first researcher established a codebook to assist in analyzing the data. Then, the researchers defined and named the themes based on TAM. Next, the researchers connected the narrative to the themes, named each theme according to the model, and generated themes. The last step of the data analysis process was to produce a concise, non-repetitive account of the story related to the research questions (Clarke & Braun, 2013).
Results
Perceived Ease of Use
Drawing from the survey questionnaire, 79% of the middle and high school counselors (n = 30) strongly or somewhat agreed that Naviance has a friendly interface for students and counselors, requires minimal effort, and was easy to use, while 5% (n = 2) neither agreed nor disagreed and 16% (n = 6) somewhat disagreed. Similarly, when asked whether Naviance was clear and understandable, 79% (n = 30) strongly or somewhat agreed, while 3% (n = 1) neither agreed nor disagreed, and 18% (n = 7) somewhat or strongly disagreed.
During the interviews, the counselors reported that the Naviance data platform layout made it easy to view and use all the pertinent data required for advising students on academic performance, college readiness, and social and emotional development. Specifically, some of the layout features discussed by counselors included Quick Links (i.e., application manager, transcript manager, journal dashboard, curriculum, and test prep) and counseling tabs (i.e., students, planner to help assign tasks and discuss goals, course planner, scholarships, colleges, careers, and a new feature, analytics). Other areas described by counselors that contributed to the ease of use of Naviance was data visualization of college applications submitted by students on the home page, and outcome images (i.e., overall percentage of students that applied and were accepted to at least one college and overall percentage that applied to and were accepted to a 4-year college).
Another feature reported by middle and high school counselors that they believed was easy to use was the reports and analytics functionality. At the middle school level, counselors indicated that they were able to run reports on whether students completed their career inventories or curriculum assignments. If a student failed to complete an assignment, counselors mentioned that sending an electronic reminder to their student via Naviance was seamless. One middle school counselor stated, “I run various queries in Naviance, which are extremely helpful. I like the feature where it allows me to automatically generate a weekly status report on all of my students.”
One high school counselor described Naviance’s academic, college, and career online resources: “Naviance is the best setup I’ve seen in my 20-plus years of being a counselor. It’s a one-stop shop and really simple to use.” Two other high school counselors described the ability to cross-share information with other Naviance counselors nationwide. For instance, a male high school counselor stated, “I no longer need to create student surveys! Other counselors who use Naviance in other states have created a battery of surveys across entire grade levels that I can export and electronically use with my students.”
Overall, most of the middle and high school counselors reported that Naviance was easy to use; however, some school counselors somewhat disagreed. For example, one high school counselor mentioned, “When Naviance is working correctly and the students can complete the activities, Naviance is easy to use. As a counselor, Naviance feels like busy work [record keeping, student follow-up, having groups of students logging in to a system], especially when there are issues with connectivity.” Another counselor reported, “Naviance is not user-friendly at the high school level. It’s too cumbersome and time consuming.”
Perceived Usefulness
On the survey questionnaire, when asked whether Naviance increases job-related effectiveness and productivity, in both instances most school counselors (79%, n = 30) strongly or somewhat agreed, while some were neutral (5%, n = 2) or somewhat disagreed (16%, n = 6). When asked whether Naviance enhances counseling practices, 84% of school counselors (n = 32) strongly or somewhat agreed, while 16% somewhat disagreed (n = 6). When asked whether Naviance was useful 92% of school counselors agreed (n = 35), while 8% (n = 3) somewhat or strongly disagreed.
During the interviews, eight of the 10 middle and high school counselors reported that the Naviance system is a comprehensive counseling solution that allows for the collection and quick retrieval of information that shows measurable results of their work, which increases their job effectiveness and productivity. For instance, school counselors identified the ability to retrieve overall assessment results, graduation status, academic progress, individual and small group tracking, pre- and post-outcomes, analysis on college application and acceptance rates (i.e., 2- and 4-year acceptances), field trip numbers, PSAT/SAT/ACT historical data, and more. The collection, analysis, and reporting of data from Naviance was perceived by school counselors as a useful strategy that supported their effort in becoming more data-driven, with data needed for school counselors to establish credibility in their role, evaluate their impact, and demonstrate program accountability that promotes student outcomes. The perception by many middle and high school counselors was that the Naviance system facilitated evidence-based practices. One high school counselor put it this way, “administrators understand data, and if we want to demonstrate our value to stakeholders, we must show how our work impacts student outcomes.” A middle school counselor stated, “Presenting survey data and responses from students after each presentation or field trip shows teachers, administrators, and parents the effect of our efforts.”
When asked whether Naviance enhances their counseling practice, one middle school counselor stated, “I think that Naviance makes our jobs a lot easier. . . . Naviance has helped to streamline the college, career, and academic process and make it very clear. Everything about our job as counselors is more fluid.” Another middle school counselor stated, “I think Naviance is very beneficial to my role. I can track student progress, communicate to teachers about relevant meetings, quickly deliver services, and actively engage to find digital resources to address needs.” A counselor at the high school level stated, “The more I used Naviance, the more I saw the many benefits, possibilities, and connections to the work that I do every day. Naviance has become a really important tool in my arsenal.” A high school counselor commented that Naviance helps capture whether students are on or off track to graduate and is a source to share electronic resources for students needing Tier 2 supports. Another high school counselor reported that Naviance was helpful in saving time when completing tasks and gathering student information. She stated, “Using Naviance makes me a better counselor; I’m more productive throughout my day, and I can tackle other more pressing issues students might have instead of working late to update my Excel spreadsheet.”
Although there were more counselors who found Naviance useful in their role, one middle school counselor and one high school counselor did not agree that Naviance enhanced their counseling practice. The high school counselor stated, “Naviance is yet again another system to use to support students that might go away when there is no more funding, so why learn it.” The same counselor went on to add that she has students who are “dealing with anger, drug addiction, pregnancy, suicide, and anxiety, and Naviance does not offer curriculum on those topics.” She further stated, “I can upload resources into Naviance, but it’s not useful because my role also includes helping students in the areas of social and emotional development.”
The middle school counselor described her experience using Naviance and added, “Naviance is good for kids, but I honestly do not see how it makes me a better counselor or my job more efficient or productive.” The same counselor added, “My job is about building trust, establishing relationships, advocating, and guiding students through middle school. Naviance is a tool that can help facilitate that process, but it does not enhance my counseling skills.”
Attitudes
When asked whether counselors like using Naviance and whether they have a generally favorable attitude toward it, in both instances the results were mixed. Twenty-eight (72%) of the 38 school counselors strongly or somewhat agreed that they liked using Naviance, four counselors (10%) neither agreed nor disagreed, and seven (18%) somewhat disagreed or strongly disagreed. When asked about having a favorable attitude toward Naviance, 23 (61%) strongly or somewhat agreed, 5 (13%) neither agreed nor disagreed, and 10 (26%) disagreed or strongly disagreed. Twenty-three school counselors (61%) reported on the open-ended survey question that Naviance was desirable to use for academic and related counseling purposes. Several counselors indicated that multiple training opportunities contributed to comfort level and positive attitudes. However, one high school counselor whose attitude was less than positive stated, “I would prefer to use Californiacolleges.edu, which is a free program that essentially offers the same activities for our students instead of Naviance. Plus, the system specifically caters to counselors and students in California, unlike Naviance.”
Two challenges identified by several school counselors that interfered with having a positive attitude about Naviance related to bandwidth issues and access to schools’ computer labs. Counselors expressed frustration by the slow internet connection at their schools, which they reported was due to limited bandwidth capacity. One counselor commented, “due to bandwidth limitations, Naviance does not always work.” Another challenge identified that interfered with overall satisfaction of Naviance was limited access to computer labs. One high school counselor stated, “Computer labs are scarce and accessibility to use Naviance with students is difficult.”
Actual Usage
Drawing from the Naviance usage and engagement reports, actual Naviance usage and engagement among school counselors was high. Since the implementation of Naviance, school counselor usage has increased each year (see Table 1). Counselor-supported engagement within Naviance is highest among high school counselors (see Table 2).
Table 1
Actual Usage of Naviance Since Implementation
Descriptors |
Year 1
(2014–2015)
|
Year 2
(2015–2016)
|
Year 3
(2016–2017)
|
Middle and High School |
1,295
|
3,277
|
5,574
|
Note.
Number of times school counselors used or accessed Naviance from 2014–2017.
Table 2
Counselor Engagement Support Provided to Students
Descriptors |
Naviance Guidance Curriculum
|
ACT/SAT/ AP Study Plans
|
College Planning
|
Career Planning
|
Academic Planning
|
Middle School |
12,887
|
0
|
599
|
10,735
|
32
|
High School |
22,366
|
153,000
|
11,623
|
508
|
497
|
Note.
Number of times Naviance was used to engage students in 2016–2017.
On the survey, middle and high school counselors were asked the frequency of Naviance usage. Most school counselors used Naviance daily, followed by weekly usage. Sixty-six percent (n = 25) reported using Naviance daily, whereas 24% (n = 9) indicated using Naviance weekly, and 5% (n = 2) reported monthly use. Finally, 5% (n = 2) reported not using Naviance at all. Table 3 shows the frequency of Naviance usage.
Table 3
Naviance Frequency of Use by School Counselors
Descriptors
|
Daily
|
Weekly
|
Monthly
|
At Least Every Other Month
|
Not At All
|
Middle School |
10
|
2
|
0
|
0
|
1
|
High School |
15
|
7
|
2
|
0
|
1
|
Note.
Frequency in which school counselors used Naviance during the
2016–2017 academic year.
Discussion
Implementing technology in school counseling is a call to action from past counseling researchers (Casey, Bloom, & Moan, 1994; Creamer, 2000; Dahir, 2009; Granello, 2000) to move the profession into the future (Dahir, 2009). When school counselors adopt and integrate technology into their practices, they can be effective in their role (Hu et al., 2003). The first research question, whether school counselors choose to use or not use Naviance, was answered by most of the counselors, who indicated that the ease of use and the overall usefulness influenced their decision to use the Naviance platform or not. Barriers identified that interfered with ease of use and usefulness were bandwidth issues within schools and school counselors’ ability to connect to the resource tool.
The second research question, how Naviance acceptance and usage enhance school counseling practices, productivity, and efficiency, was answered by most of the school counselors in this study, who stated that the use of Naviance positively enhanced their job productivity, efficiency, and counseling practices. Particularly, the ability to introduce college-related material to help students develop individual education plans, identify courses, provide social and emotional resources, and advise on graduation status and college eligibility, was positive. In addition, more school counselors used Naviance as a vehicle to share information with teachers, administrators, and parents.
Limitations
There were several limitations. The results of this study indicated that school counselors had positive attitudes toward the integration and usage of Naviance; however, the findings were limited to middle and high school counselors who work in a specific public school district located in the southwestern part of the United States, which prevented the inclusion of experiences and expertise of other public and private school counselors throughout the country. The addition of other Naviance users in small public and private schools might have produced other results. Another limitation was that the first researcher has used Naviance for the past 10 years in various roles as a district administrator. To prevent bias, the first researcher did not make assumptions based on what participants chose to share or attempt to present answers. In contrast, the second researcher has never used Naviance, which allowed for an unbiased viewpoint when writing the analysis. Further, a school counselor educator, familiar with Naviance, reviewed and read over this study prior to publication to minimize researcher technology bias.
Finally, Naviance generally provides district offices and schools with reports on engagement activities and staff and student usage. Although researchers used the Naviance engagement reports to speak to overall usage in subcategories such as college planning, career planning, guidance curriculum, and test preparation, multiple school engagement reports were combined to differentiate middle and high school engagement activities. In addition, Naviance provides reports on staff usage; therefore, the first researcher retrieved data at the school site level to determine counselor usage rather than usage by staff, such as teachers and administrators, during data analysis.
Implications for Counselors
One of the benefits of using an online platform such as Naviance is that it can bring value to the practices of school counselors when helping to introduce and prepare students for college. For instance, such a tool can support dissemination of critical student-related information, data collection, tracking and analysis, customization of 4-year graduation plans, and communication between multiple stakeholders, to name a few.
The knowledge generated from this study is useful to school counselors in several ways. First, understanding the intricacies and impact of Naviance could offer school counselors additional ways to support their students’ academic development, college preparedness, and readiness efforts, and to share and provide social and emotional resources to students. Second, knowing which features in Naviance influence career and college-related outcomes at the middle and high school level can improve engagement and communication efforts between school counselors, parents, and teachers. Third, exposing students early to the numerous college readiness features and functionalities in Naviance can increase graduation and college application rates of high school students, which is consistent with literature findings. Fourth, capturing college- and career-related data can help school counselors communicate, gather, analyze, and synthesize information required to meet state accountability standards and evaluate the effectiveness of counseling programs.
Recommendations and Future Research
Given the benefits of integrating Naviance into the daily practice of school counselors, two recommendations for future practice include leveraging the reports and analytic features to emphasize programmatic effectiveness and student outcomes, and infusing the college-related curriculum into subject matter classes. Although the high school counselor is the primary interpreter of the college preparation, application, and enrollment sources, incorporating college-related information into classroom instruction could be used as a springboard to deliver information on college and career readiness and support the understanding of the relationship between academic performance and college eligibility. This practice could free up time for the high school counselor to have more meaningful and deliberate conversations with students to support their understanding of college norms and expectations and effectively facilitate the college enrollment process.
The findings indicate a need to extend TAM by exploring other external factors that influence user acceptance of Naviance. For example, future research could explore the connection between counselor usage and the number of hours trained on Naviance. Low counselor usage could be the result of insufficient training or differences in age. In addition, as many schools, particularly those located in urban settings, focus on increasing college eligibility, future studies should be conducted on Naviance test prep (i.e., ACT, SAT, AP) and student outcomes.
Conclusion
Research into school counselors’ technology integration and usage has been a focus in the counseling profession since the 1980s and continues to be an important area for investigation today. Most school counselors suggested that Naviance was useful in their role as a school counselor in providing academic, career, college, and personal counseling to students and that actual usage enhanced their job performance, productivity, and proficiency. In addition, many expressed that Naviance was a tool that required minimal effort, if usage was ongoing. Lastly, perceived usefulness and perceived ease of use was connected to school counselors’ positive attitude regarding Naviance.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
Alhojailan, M. I. (2012). Thematic analysis: A critical review of its process and evaluation. Retrieved from https://fac.ksu.edu.sa/sites/default/files/ta_thematic_analysis_dr_mohammed_alhojailan.pdf
Bernard, H. R., & Ryan, G. W. (2010). Analyzing qualitative data: Systematic approaches (1st ed.). Los Angeles, CA: Sage.
Blanchard, A., Prior, F., Barton, J., & Dawson, T. (2016). Barriers in new health and social care technology implementation in the United Kingdom, a case study. Retrieved from https://www.computer.org/csdl/proceedings/dese/2016/5487/00/07930625.pdf
Carey, J., & Dimmitt, C. (2004). The web and school counseling. Interdisciplinary Journal of Practice, Theory, and Applied Research, 21(3–4), 69–79. doi:10.1300/J025v21n03_08
Carlson, L., Portman, T., & Bartlett, J. (2006). Professional school counselors’ approaches to technology. Professional School Psychology, 9, 252–256. doi:10.5330/prsc.9.3.9162536405845454
Casey, J. A., Bloom, J. W., & Moan, E. R. (1994). Use of technology in counselor supervision. Retrieved from https://www.counseling.org/resources/library/ERIC%20Digests/94-25.pdf
Christian, D., Lawrence, A., & Dampman, N. (2017). Increasing college access through the implementation of Naviance: An exploratory study. Journal of College Access, 3(2), 28–44.
Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120–123.
Creamer, M. B. (2000). Technology utilization in the field of school counseling: An action research study. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.502.8338&rep=rep1&type=pdf
Dahir, C. A. (2009). School counseling in the 21st century: Where lies the future? Introduction to the special section. Journal of Counseling & Development, 87, 3–5. doi:10.1002/j.1556-6678.2009.tb00542.x
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Retrieved from https://deepblue.lib.umich.edu/bitstream/handle/2027.42/30954/0000626.pdf?sequence=1
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5, 1–4. doi:10.11648/j.ajtas.20160501.11
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Frels, R. K., & Onwuegbuzie, A. J. (2013). Administering quantitative instruments with qualitative interviews: A mixed research approach. Journal of Counseling & Development, 91, 184–194.
doi:10.1002/j.1556-6676.2013.00085.x
Granello, P. F. (2000). Historical context: The relationship of computer technologies and counseling. Retrieved from https://www.ericdigests.org/2001-3/context.htm
Guzman, A., & Nussbaum, M. (2009). Teaching competencies for technology integration in the classroom. Journal of Computer Assisted Learning, 25, 453–469. doi:10.1111/j.1365-2729.2009.00322.x
Hayden, L., Poynton, T., & Sabella, R. A. (2008). School counselors’ use of technology within the ASCA national model’s delivery system. Journal of Technology in Counseling 5(1). Retrieved from https://www.researchgat
e.net/publication/287417135_School_counselors’_use_of_technology_with_in_the_ASCA_national_mod
el’s_delivery_system
Hobsons. (2017). Connecting learning to life. Retrieved from https://www.naviance.com/solutions
Holcomb-McCoy, C., Gonzalez, I., & Johnston, G. (2009). School counselor dispositions as predictors of data usage. Professional School Counseling, 12(5). doi:10.1177/2156759X0901200504
Hu, P. J.-H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information & Management, 41, 227–241. doi:10.1016/S0378-7206(03)00050-8
Kozlowski, K. A., Mikesina, L., & Genova, A. (2015). Incorporating technology into a school counseling body image group. Retrieved from https://www.counseling.org/docs/default-source/vistas/incorporating-technology-into-a-school-counseling-body-image-group.pdf?sfvrsn=8
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29, 269–282. doi:10.1016/S0167-9236(00)00076-2
Loague, A. M., Alexander, J. J., & Reynolds, G. P. (2010). The school counselor web page. Retrieved from https://www.counseling.org/docs/default-source/vistas/vistas_2010_article_62.pdf?sfvrsn=b665e8d_11
Millsom, A., & Bryant, J. (2006). School counseling departmental websites: What message do we send? Professional School Counseling, 10(2).
Nair, I., & Das, V. M. (2011). Analysis of recent studies undertaken for assessing acceptance of technology among teachers using TAM. International Journal of Computer Applications, 32(8), 38–46.
National Center for Education Statistics (2017). The condition of education 2017. Retrieved from https://nces.ed.gov/pubs2017/2017144.pdf
Onwuegbuzie, A. J., Leech, N. L., & Collins, K. M. T. (2011). Toward a new era for conducting mixed analyses: The role of quantitative dominant and qualitative dominant crossover mixed analyses. In M. Williams & W. P. Vogt (Eds.), The Sage handbook of innovation in social research methods (pp. 353–384). Thousand Oaks, CA: Sage.
Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A.
Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (1st ed., pp.
351–383). Thousand Oaks, CA: Sage.
Padmavathi, M. (2016). A study of student-teachers’ readiness to use computers in teaching: An empirical study. I-manager’s Journal on School Educational Technology, 11(3), 29–39.
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42, 533–544.
doi:10.1007/s10488-013-0528-y
Rainey, S., Mcglothlin, J., & Miller, L. (2008). Technology: School counselor attitudes, experiences and competency. Journal of Technology in Counseling, 5(1).
Reljic, R., Harper, A., & Crethar, H. (2013). Using technology creatively to empower diverse populations in counseling. Retrieved from https://www.counseling.org/docs/default-source/vistas/using-technology-creatively-to-empower-diverse-populations-in-counseling.pdf?sfvrsn=57116101_12
Ross, A. A., & Onwuegbuzie, A. J. (2010). Mixed methods research design: A comparison of prevalence in JRME and AERJ. International Journal of Multiple Research Approaches, 4, 233–245.
doi:10.5172/mra.2010.4.3.233
Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the Technology Acceptance Model. Information & Management, 42, 317–327. doi:10.1016/j.im.2003.12.013
Serdyukov, P. (2017). Innovation in education: What works, what doesn’t, and what to do about it? Journal of Research in Innovative Teaching and Learning, 10, 4–33. doi:10.1108/JRIT-10-2016-0007
Steele, T. M., Jacokes, D. E., & Stone, C. B. (2014). An examination of the role of online technology in school counseling. Professional School Counseling, 18, 125–135. doi:10.1177/2156759X0001800118
Sung, Y.-T., Chang, K.-E., & Liu, T.-C. (2016). The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis. Computers & Education, 94, 252–275. doi:10.1016/j.compedu.2015.11.008
Tarhini, A., Hone, K., & Liu, X. (2015). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46, 739–755. doi:10.1111/bjet.12169
Teo, T. (Ed.). (2011). Technology acceptance in education: Research and issues. Retrieved from http://users.ugent.be/~wduyck/articles/PynooDevolderTondeurVanBraakDuyckDuyck2011b.pdf
Tri Anni, C., Sunawan, S., & Haryono, H. (2018). School counselors’ intention to use technology: The technology acceptance model. Retrieved from https://www.researchgate.net/publication/324438702_School_Counselors’_Intention_to_Use_Technology_The_Technology_Acceptance_Model
Van Horn, S., & Myrick, R. (2001). Computer technology and the 21st century school counselor. Retrieved from https://www.researchgate.net/publication/234687258_Computer_Technology_and_the_21st_Century_School_Counselor
Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Thousand Oaks, CA: Sage.
Vernell Deslonde is a director at Fontana Unified School District. Michael Becerra is an adjunct instructor at the University of North Texas at Dallas. Correspondence can be addressed to Vernell Deslonde, 9680 Citrus Ave., Fontana, CA 92334, deslonde08@gmail.com.
Sep 28, 2018 | Volume 8 - Issue 3
Anthony Hartman, Hope Schuermann, Jovanna Kenney
Despite efforts to boost mental health treatment-seeking behaviors by combat veterans, rates have improved relatively little since 2004. Previous work suggests that trust and confidence in the mental health community may be a significant factor. This study explored how professional titles may impact trust and confidence among active-duty U.S. Army soldiers (n = 32). Consistent with previous research, eight vignettes were used to solicit ordinal (ranked) trust and confidence scores for mental health professionals. Highest confidence and trust were seen in clinical psychologists and licensed professional counselors, followed by psychiatrists, licensed clinical social workers, and marriage and family therapists; however, deviations were seen for each individual vignette and the manifested symptoms depicted. Scores for trust and confidence were strongly correlated and both appear to impact soldiers’ treatment-seeking decisions.
Keywords: soldiers, mental health professionals, licensed professional counselors, trust, confidence
The U.S. Army Medical Command’s Department of Behavioral Health provides the following vision: “Our efforts in education, prevention, and early treatment are unprecedented. Our goal is to ensure that every deployed and returning soldier receives the health care they need” (U.S. Army Medical Department, 2016). In 2004, a landmark study by Hoge and colleagues found that only 13–27% of soldiers meeting screening criteria for mental health disorders sought treatment from a mental health professional in the previous year. The researchers concluded that the primary reason for such underutilization was perhaps “concern about how a soldier will be perceived by peers and by the leadership” (Hoge et al., 2004, p. 20). Subsequently, the Army has taken significant actions to reduce negative perceptions toward mental health care and increase confidentiality for those seeking treatment.
Despite substantial efforts to reduce negative stigmas, the number of soldiers seeking mental health care seems to remain significantly low. In a population of soldiers with probable post-traumatic stress disorder (PTSD) or major depression, Schell and Marshall (2008) found that “only 30 percent had received any type of minimally adequate treatment” (p. 101). Specifically, only 18% received minimally adequate talk therapy treatment. Of a sample population of National Guard and Reserve service members reporting psychological problems, Britt et al. (2011) found that only 42% had sought treatment. Most recently, Britt, Jennings, Cheung, Pury, and Zinzow (2015) found that only 40% of soldiers who acknowledged having a mental health issue sought treatment in the last year. Although the percentages of soldiers seeking treatment seem to be improving, the current literature continues to show less than half of those in need seek even a first visit with a mental health care provider. Thus, other significant deterrents to seeking treatment remain beyond the perceptions of leadership and peers.
Research studies indicate that one possible reason for this underutilization of mental health care services could be soldiers’ lack of trust or confidence in the quality of their providers or treatments. When surveyed, one in four soldiers recently returning from deployment indicated a lack of trust in mental health care practitioners (Kim, Britt, Klocko, Riviere, & Adler, 2011). Similarly, in a different sample of soldiers and Marines screening positively for mental health disorders, 38% indicated a lack of trust in mental health providers, while one in four of the same sample indicated a belief that mental health treatments were not effective (Hoge et al., 2004). Further hinting at a lack of trust for mental health care professionals and confidence in treatment, many soldiers would prefer to address their mental health issues with family, friends, or clergy (Schell & Marshall, 2008). Recently, the statement “Marines don’t trust mental health professionals” was rated as one of the top perceptions that mark barriers to care by a sample of enlisted Marine Corps leaders (VanSickle et al., 2016, p. 1022). Ultimately, there seems to be a trend of distrust and a lack of confidence in mental health care treatments and professionals among military populations.
Mental Health Practitioners and Military Treatment
Considering that there are numerous types of mental health professionals (e.g., psychiatrists, mental health counselors), it is possible that soldiers’ perceptions and knowledge of mental health professionals may vary depending on the specific type of provider. This study aims to distinguish soldiers’ perceptions between distinct mental health professionals: psychiatrists, clinical psychologists, licensed clinical social workers (LCSWs), licensed marriage and family therapists (LMFTs), and licensed professional counselors (LPCs). Psychiatrists are distinct in that they must have earned a doctorate in medicine (i.e., MD or DO) and have the nearly exclusive privilege of prescribing pharmaceutical medications for the treatment of mental disorders. Clinical psychologists also must be educated at the doctoral level (i.e., PhD or PsyD) and maintain a licensure in order to practice, but they cannot prescribe medications in most states. LCSWs, LMFTs, and LPCs are educated at least at the master’s level by an institution accredited for their respective field, and must complete respective licensing requirements that include supervised clinical experience following degree completion.
While the educational experience and licensing protocol can easily be distinguished, the mental health professions also have evolved somewhat distinct professional identities in terms of their approaches to mental health treatment. While psychiatrists are trained in various psychotherapeutic modalities, trends indicate the majority of current and future psychiatrists plan to rely more heavily on pharmacological treatments than on talk therapies (Clemens, Plakun, Lazar, & Mellman, 2014; Zisook et al., 2011). As for clinical psychologists, a review of 50 years of literature surrounding this occupation revealed trends around specializing in one particular aspect of the field (i.e., psychotherapy, assessment, research) and one or two treatment modalities (e.g., psychodynamic therapy, cognitive-behavioral therapy), and a prevalence of cognitive therapies (Norcross & Karpiak, 2012). Generally speaking, LCSWs are likely to conduct therapy from a holistic approach that heavily considers the social impacts on a person while pursing social justice and equality agendas, such as helping underprivileged groups (Bradley, Maschi, O’Brien, Morgen, & Ward, 2012). While LMFTs are often exposed to or trained in a wide variety of therapeutic paradigms and techniques, they are likely to emphasize a collective rather than individual treatment approach, often marked by working with families and couples to identify and improve systemic or transactional issues between the members (Imber-Black, 2014). As LPCs’ professional identity continues to develop and stabilize (Mellin, Hunt, & Nichols, 2011; Reiner, Dobmeier, & Hernández, 2013), professional counselors train in a variety of treatment modalities and provide a variety of services in the mental health field, including “the diagnosis and treatment of mental and emotional disorders, including addictive disorders; psychoeducational techniques aimed at the prevention of such disorders; consultation to individuals, couples, families, groups, and organizations; and research into more effective therapeutic treatment modalities” (American Counseling Association, 2011, para. 4).
Although the average client may not know or fully understand the distinctions between mental health professionals, the literature suggests clients do exhibit some bias when selecting mental health professionals. Over the past 30 years, researchers have shown a consistent trend of professional titles or education levels impacting perceptions of mental health professionals. Warner and Bradley (1991) and Wollersheim and Walsh (1993) established that both perceptions of and confidence in mental health therapies were impacted by the title and education level of the mental health professional; generally, participants in these studies indicated a lack of confidence and knowledge about clinical psychologists and a preference for treatment from counselors. In a study examining public confidence in mental health professionals, Fall, Levitov, Jennings, and Eberts (2000) found significant differences in confidence based upon their title as well as their education level (i.e., master’s vs. doctoral level); participants mostly favored doctoral-level education and preferred counselors, except when presented with “serious psychiatric disorders” (p. 122). This study was repeated in 2005 with an African American sample that provided similar findings (Fall, Levitov, Anderson, & Clay, 2005). While specific attitudes and perceptions may have changed or evolved over the past three decades, these studies show that distinct perceptions or even biases toward professional titles do exist among civilian populations. This led the researchers to question if similar trends exist in military populations, which may be influencing the treatment-seeking decisions of service members.
To summarize, soldiers’ confidence in treatment for and trust in mental health professionals might be significantly impacting treatment-seeking decisions. In multiple studies, service members have repeatedly indicated relatively low levels of trust and confidence in mental health providers and treatments. Also, researchers have consistently shown that a professional title can impact patient or public perceptions with respect to general confidence in the professional’s abilities. To date, no known research is published on military members’ perceptions and levels of confidence or trust with differing mental health professionals. Thus, the purpose of this study was to explore soldiers’ relative levels of trust for and confidence in mental health professionals based solely upon their title and a presenting issue, in an effort to better understand what may be influencing treatment-seeking decisions among U.S. Army soldiers.
Method
The researchers for this study received approval from the Institutional Review Board of their university, and the survey was approved for distribution to active-duty soldiers by Army public affairs representatives. Sample size was determined by following similar confidence in mental health professional studies that used Friedman non-parametric tests (e.g., Fall et al., 2000; Fall et al., 2005). Participants were surveyed via the online metrics program Qualtrics, ensuring anonymity.
Participants
Active-duty soldiers serving in the U.S. Army were recruited using snowball sampling initiated by public affairs representatives at various Army installations. Each potential participant received a generalized email invitation that included an information sheet about the research and a link to complete an online survey. Participants were encouraged to forward the invitation to others who also met the inclusion criteria, which limited participation to those currently serving on active duty in the U.S. Army with more than 2 years of active-duty service or the National Guard/Army Reserve equivalent. Upon completion of the survey, participants were offered the opportunity to enroll in a raffle drawing to win one of two prizes: a $100 or a $50 gift card.
The sample included 32 active-duty soldiers, 26 males and six females, between the ages of 25 and 50 years (M = 33.3, SD = 7.0). Ethnic identities included 25 non-Hispanic Whites, two Hispanic or Latinos, one African American, one Filipino, one Native American, one White/Korean, and one White/Hispanic. Most of the participants (26) were married, while three were divorced and three had never married. Nearly two-thirds of the sample indicated current responsibility for children in their homes; there was an average of 1.85 children (SD = 1.5) reported by these 20 participants. Thirteen of the soldiers had seen at least one mental health professional (MHP) prior to completing the survey; respondents had seen all five MHPs included in this study. Participants were allowed to list multiple MHPs if applicable, and the MHPs were identified as follows: clinical psychologist, seven times; psychiatrist, five times; LPC, four times; LCSW, three times; LMFT, three times; and “other” or “unsure,”five times.
Regarding military experience, the sample included 18 officers, 11 non-commissioned officers, and three junior-enlisted (i.e., rank of E1–E4) soldiers. Twenty participants had a military occupational specialty (MOS) considered as Combat Arms in the U.S. Army. In the military, not all service members are equally likely to fight in combat; certain MOSs are combat-related while others are supportive in nature (e.g., administrative personnel, mechanics, logisticians). Of our 32 qualifying participants, we had a good mix of combat and non-combat MOSs. To the reader, this may seem to be either irrelevant or not particularly noteworthy information; however, this data can be quite important when forming conclusions about the study. On average, military service was 11.4 years (SD = 7.2), with 17 months (SD = 11.5) deployed to either Iraq or Afghanistan; only two participants had not been deployed to these countries. Seventy-five percent of the sample reported direct exposure to combat, and 59% reported having never seen an MHP for even one visit throughout their life.
Materials
Demographic questionnaire. In order to provide some description of the sample population, a demographics survey of 15 questions regarding age, sex, ethnicity, marital background, parental status, military rank, deployment and combat experience, and previous experience with mental health care providers was collected from participants. Most questions were multiple-choice but offered the options to not respond or provide a unique response if desired. The remaining questions were free-response.
Vignettes. Brief vignettes were used to depict the selected mental health diagnoses or mental health issues of eight fictional soldiers recently returning from a combat deployment. The vignettes were limited in length to approximately half of a standard printed page and were written with the goal of depicting diagnostic criteria in a manner that one might see them manifested by the soldier in the vignette. Authors specifically avoided using the exact clinical terms that an MHP may use while ensuring that enough diagnostic criteria were included to suggest the intended diagnoses may be warranted.
Each vignette was followed immediately by two questions. These questions asked the participant to rank the five MHPs in order according to the participant’s preference for (1) confidence in the MHPs in providing treatment for the soldier in the vignette, and (2) their own personal trust for the professionals if they were experiencing the symptoms described in the vignette. Because both questions were worded similarly, keywords such as trust and confident were bolded or underlined in order to highlight the intent of the question.
Development and validation of the vignettes.
The vignettes and questions were originally drafted by the lead researcher to explore how soldiers may rank MHPs under the two stated conditions (i.e., confidence and trust questions). The four mental health diagnoses selected were PTSD, anxiety disorders, depression, and substance use disorders, as these were identified by Seal, Bertenthal, Miner, Sen, and Marmar (2007) to be the most prevalent for soldiers returning from Iraq. The four common issues were suicide, marital problems, parenting difficulties, and sleep problems; these were selected from the Military Health System’s “After Deployment” (2015) website because they were depicted as common problems faced by soldiers and contributed to the breadth of issues explored in the study. Vignettes were modeled after previous studies using similar metrics to measure populations’ trust of MHPs (e.g., Fall et al., 2000; Fall et al., 2005).
After review and editing within the research team, faculty with extensive clinical and teaching expertise in the area of diagnosis reviewed the vignettes. Based on their recommendations, specific diagnostic labels, such as PTSD and depression, were removed in order to reduce the impact of these labels on participants’ responses, and the keywords trust and confidence were included and bolded in the survey questions. Their input also resulted in the refining of the vignettes to more accurately depict the intended issues based upon their clinical experience and expertise.
Procedures
From January to June of 2017, surveys were administered via Qualtrics software on an electronic device of the participant’s choosing. Respondents were requested to complete the surveys at a location and time presenting minimal distractions. After being provided information about the study and consenting to continue, participants were presented with the demographics survey followed by the vignettes. The survey would not advance to the next page unless a response was recorded to all questions on the previous page. Upon completion of the demographics portion, participants advanced to the vignettes depicting soldiers facing issues upon returning from a combat deployment.
During the vignette portion of the survey, respondents ranked the list of mental health practitioners for both the confidence and trust conditions; see the Appendix for the vignettes presented to participants. The survey would not allow duplicate ranks (i.e., MHPs could not “tie”) for either condition. The vignettes were randomized, with both the trust and confidence questions presented together on the same screen, and the listed order of the MHPs was randomized for each vignette as well.
Analysis
Data analysis focused on three main themes: the mean ranks for trust of the MHPs across the vignettes, the mean ranks for confidence in the MHPs across the vignettes, and potential correlation between trust and confidence. Consistent with the Fall et al. (2005) analysis, Friedman non-parametric tests and Wilcoxon matched-pairs tests were used to determine significant findings in the mean ranks for MHPs in each vignette with respect to both the confidence and trust conditions separately. These tests were completed 16 times—once for each of the eight vignettes for both the trust and confidence questions. Afterward, the data was aggregated separately for both the trust and confidence questions to allow an overall assessment of the mean ranks for each MHP without concern for the particular vignette presented. Both the Friedman and Wilcoxon tests were completed again on the aggregated data. Finally, a Goodman and Kruskall’s gamma test was used to determine the correlation between trust and confidence ranking for each MHP.
Results
For all eight vignettes, significant differences (n = 32, df = 4, p <= .002) were found for mean rankings in both confidence and trust conditions. Subsequently, Wilcoxon matched-pairs tests identified statistically significant differences within groups for each of the 16 conditions; see Table 1 for specific results. Figures 1 and 2 display inverted mean rankings for each MHP by vignette for the confidence and trust questions respectively; higher scores indicate a more favorable ranking.
In both the confidence and trust conditions, the data from each vignette allowed for the separation of the five MHPs into either two or three distinct groups in terms of their rankings. In some instances, some MHPs could be grouped with both the higher- and lower-ranking adjacent MHP; in this case, the MHP was placed in both groups. For example, in Table 1 under the Aggregate Rank column for the confidence condition, there was no significant difference between LPCs and psychiatrists (N = 256, p = .202), or LPCs and psychologists (N = 256, p = .336), but there was a significant difference between psychologists and psychiatrists (N = 256, p = .011).
Lastly, scores from all eight vignettes were aggregated for each MHP to allow an overall measure of the MHP’s ranking for both confidence and trust. Table 1 includes the associated statistically significant grouping, and Figure 3 depicts the aggregated inverted mean ranking for both conditions for each MHP. Using a Goodman and Kruskall’s gamma test on the aggregated data, a strong positive correlation was found between confidence and trust ratings for all five MHPs with G values ranging from 0.72 to 0.88 (N = 256, p < .0005).

Figure 1. Inverted Mean Ranks for Confidence Question Plotted by Type of Mental Health Professional and Vignette. Higher mean rank equates to higher confidence.


Figure 2. Inverted Mean Ranks for Trust Question Plotted by Type of Mental Health Professional and Vignette. Higher mean rank equates to higher trust.

Figure 3. Aggregated Inverted Mean Ranks for Mental Health Professionals for Confidence and Trust Questions. Higher mean rank equates to higher confidence or trust. Error bars indicate standard error based on standard deviation from the mean; they do not indicate statistical significance.
Discussion
This study was designed to explore active-duty Army soldiers’ perceptions toward various mental health care providers with respect to trust and confidence in the MHP. Overall, the sample population of soldiers appears to have the highest confidence and trust in clinical psychologists and LPCs, while LCSWs and LMFTs are significantly less preferred (as seen in Table 1). Psychiatrists seem to be somewhere between each of these two groups, as they appear in both the highest and second-highest preferred groups depending on the condition (i.e., confidence or trust). The statistically significant stratification into these groups suggests that the title of available MHPs may influence a soldier’s decision to seek services. Undoubtedly, other factors are involved, but the title, and perhaps the certifications of the available professional, is likely impacting treatment-seeking behaviors in military communities.
At the heart of this study is the notion that each of the MHPs included could treat any of the soldiers in the vignettes; however, the results suggest that soldiers would seek out different professionals based on the context of the presenting symptoms rather than the type or potential efficacy of the treatment to be received. For example, the marital problems vignette (see Appendix) could arguably have been treated more effectively by a psychiatrist than an LMFT; perhaps the declining relationship was itself a symptom of biochemical issues such as vitamin or neurotransmitter deficiencies, which may be more aptly treated with medicine. Or, it also is possible that an experienced LPC or LCSW could have effectively brought to the surface some other underlying issue in the course of individual therapy rather than the marriage, couple, and family-oriented approach taken by an LMFT. Similar arguments could be made for each of the other vignettes, but the results suggest that soldiers are likely making treatment decisions based upon professional title and presumably the associated reputation. If the Army’s goal is to boost rates of treatment-seeking behaviors, professional titles and perceptions of trust and confidence should not be ignored.
Results also show a strong correlation between trust and confidence across all of the vignettes. This can best be seen by comparing the LMFTs’ rankings for the marital problems and parenting issues vignettes with their consistently lower scores on the other vignettes. The jump in scores was consistent across both conditions, demonstrating that trust and confidence for MHPs are strongly linked. Although less likely, it also is possible that the respondents might have been biased or influenced to provide similar ranks for each professional across both conditions because the survey design allowed them to see their scores for the confidence question while completing the trust question. Regardless of whether trust influences confidence or vice versa, the two should be considered in the quest to boost treatment-seeking rates among soldiers.
Implications for Service Provision
With further validating and corroborating research, the Army may be able to improve treatment-seeking rates among those in need of mental health care by adjusting services based on the perceptions of soldiers. Although LPCs were consistently favored more than LCSWs, the Army currently allows LCSWs to serve as commissioned officers in behavioral health clinics providing individual therapy to soldiers, while the LPC license does not qualify an MHP to commission and serve as an officer (U.S. Department of the Army, 2007). This means soldiers have fewer chances of seeing an LPC without some type of insurance referral because the uniformed personnel initially available will not be LPCs. This study provides evidence that LPCs may be more appropriate and effective in this role by boosting treatment-seeking rates, so it could be beneficial to make treatment with LPCs more accessible to soldiers. Likewise, incorporating the services of LMFTs following deployments could help military families, as they had the highest average trust and confidence ratings of any professional in any vignette in the study when they were the preferred MHP. Perhaps they could advocate for temporary positions following deployments or increased advertisement of their services in military communities with units returning from overseas.
Limitations and Future Research
Future research is certainly needed to further confirm the results of this study. Investigators could explore what drives trust and confidence perceptions in military communities and how prior personal experiences influence the soldiers’ views of MHPs. Studies like this one could be conducted with other branches of the military and include National Guard and Reserve forces. Exploratory qualitative research could seek to identify specific factors that build trust and confidence in the mental health community as a whole. Future studies also should continue to update the disorders or issues selected to accurately represent the issues faced by targeted populations at the time.
Limitations to this study include the sample size, delivery of the survey, and lack of consideration for gender biases. While 32 respondents can provide initial insights, a much larger sample should be surveyed before any significant policy decisions are considered. The research team also recommends administering the surveys in person rather than online with the belief that many soldiers—and people in general—may not complete the digital surveys as earnestly as a paper version following a personal interaction with the research team or a recruiter. With regards to gender, it was not considered how the names of the soldiers in the vignettes may influence the respondents’ rankings; it is possible that the scores could have varied if the soldier in the vignette was of a specific gender.
Future researchers should be cautious to ensure that voluntary participation is not influenced by environmental pressures. In military communities, the researchers recommend seeking a sample population that includes personnel from multiple units, locations, and MOSs, as culture and attitudes can be vastly different among these variables.
Although this study has limitations, the researchers believe it highlights one of the key reasons that soldiers may not seek mental health services when in need: lack of trust and confidence in the resources available. Although the military has significantly addressed other identified issues, such as the associated stigma or impact to a service member’s career, treatment-seeking rates for those in need have changed very little, which indicates other issues are contributing to the decision not to visit with an MHP. The researchers hope the results of this study are built upon and examined for alternative approaches to boost treatment-seeking rates among the military.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
After Deployment. (2015). After deployment: Wellness resources for the military community. Retrieved from
http://afterdeployment.dcoe.mil
American Counseling Association. (2011). Who are licensed professional counselors. Retrieved from
https://www.counseling.org/PublicPolicy/WhoAreLPCs.pdf
Bradley, C., Maschi, T., O’Brien, H., Morgen, K., & Ward, K. (2012). Faithful but different: Clinical social workers speak out about career motivation and professional values. Journal of Social Work Education, 48, 459–477. doi:10.5175/JSWE.2012.201000043
Britt, T. W., Bennett, E. A., Crabtree, M., Haugh, C., Oliver, K., McFadden, A., & Pury, C. L. S. (2011). The theory of planned behavior and reserve component veteran treatment seeking. Military Psychology, 23, 82–96. doi:10.1080/08995605.2011.534417
Britt, T. W., Jennings, K. S., Cheung, J. H., Pury, C. L. S., & Zinzow, H. M. (2015). The role of different stigma perceptions in treatment seeking and dropout among active duty military personnel. Psychiatric Rehabilitation Journal, 38, 142–149. doi:10.1037/prj0000120
Clemens, N. A., Plakun, E. M., Lazar, S. G., & Mellman, L. (2014). Obstacles to early career psychiatrists practicing psychotherapy. Psychodynamic Psychiatry, 42, 479–495. doi:10.1521/pdps.2014.42.3.479
Fall, K. A., Levitov, J. E., Anderson, L., & Clay, H. (2005). African Americans’ perception of mental health professions. International Journal for the Advancement of Counselling, 27, 47–56.
doi:10.1007/s10447-005-2246-y
Fall, K. A., Levitov, J. E., Jennings, M., & Eberts, S. (2000). The public perception of mental health professions: An empirical examination. Journal of Mental Health Counseling, 22, 122–134.
Hoge, C. W., Castro, C. A., Messer, S. C., McGurk, D., Cotting, D. I., & Koffman, R. L. (2004). Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. New England Journal of Medicine, 351, 13–22. doi:10.1056/NEJMoa040603
Imber-Black, E. (2014). Eschewing certainties: The creation of family therapists in the 21st century. Family Process, 53, 371–379. doi:10.1111/famp.12091
Kim, P. Y., Britt, T. W., Klocko, R. P., Riviere, L. A., & Adler, A. B. (2011). Stigma, negative attitudes about treatment, and utilization of mental health care among soldiers. Military Psychology, 23, 65–81.
doi:10.1080/08995605.2011.534415
Mellin, E. A., Hunt, B., & Nichols, L. M. (2011). Counselor professional identity: Findings and implications for counseling and interprofessional collaboration. Journal of Counseling & Development, 89, 140–147. doi:10.1002/j.1556-6678.2011.tb00071.x
Norcross, J. C., & Karpiak, C. P. (2012). Clinical psychologists in the 2010s: 50 Years of the APA Division of Clinical Psychology. Clinical Psychology: Science and Practice, 19, 1–12.
doi:10.1111/j.1468-2850.2012.01269.x
Reiner, S. M., Dobmeier, R. A., & Hernández, T. J. (2013). Perceived impact of professional counselor identity: An exploratory study. Journal of Counseling & Development, 91, 174–183.
doi:10.1002/j.1556-6676.2013.00084.x
Schell, T. L., & Marshall, G. N. (2008). Survey of individuals previously deployed for OEF/OIF. In T. Tanielian & L. H. Jaycox (Eds.), Invisible wounds of war: Psychological and cognitive injuries, their consequences, and services to assist recovery (pp. 87–115). Santa Monica, CA: RAND Health Center for Military Health Policy Research.
Seal, K. H., Bertenthal, D., Miner, C. R., Sen, S., & Marmar, C. (2007). Bringing the war back home: Mental health disorders among 103,788 US veterans returning from Iraq and Afghanistan seen at Department of Veterans Affairs facilities. Archives of Internal Medicine, 167, 476–482.
U.S. Army Medical Department. (2016). Behavioral health. Retrieved from https://armymedicine.health.mil/Behavioral-Health
U.S. Department of the Army. (2007). Army Medical Department Officer Development and Career Management: Department of the Army Pamphlet 600-4. Retrieved from http://www.apd.army.mil/epubs/DR_pubs/DR_a/pdf/web/p600_4.pdf
VanSickle, M., Werbel, A., Perera, K., Pak, K., DeYoung, K., & Ghahramanlou-Holloway, M. (2016). Perceived barriers to seeking mental health care among United States Marine Corps noncommissioned officers serving as gatekeepers for suicide prevention. Psychological Assessment, 28, 1020–1025.
doi:10.1037/pas0000212
Warner, D. L., & Bradley, J. R. (1991). Undergraduate psychology students’ views of counselors, psychiatrists, and psychologists: A challenge to academic psychologists. Professional Psychology: Research and Practice, 22, 138–140.
Wollersheim, D. M., & Walsh, J. A. (1993). Clinical psychologists: Professionals without a role? Professional Psychology: Research and Practice, 24, 171–175.
Zisook, S., McQuaid, J. R., Sciolla, A., Lanouette, N., Calabrese, C., & Dunn, L. B. (2011). Psychiatric residents’ interest in psychotherapy and training stage: A multi-site survey. American Journal of Psychotherapy, 65, 47–59.
Appendix
Vignettes Used to Depict Mental Health Diagnoses and Issues
Post-Traumatic Stress Disorder Vignette
Joe returned from deployment to Afghanistan 4 months ago. He was personally involved in combat with enemy insurgents on multiple occasions and was exposed to disfigured, dead bodies of both enemy combatants and fellow soldiers as well. He has often mentioned bad dreams about one of these times in particular and seems obviously distressed (e.g., fidgeting, faster breathing, and sometimes even sweating) whenever he speaks about it. However, when his fellow soldiers from the deployment bring up the event, he seems unwilling to participate in the conversation and has on a few occasions become angry about it. Based on these behaviors, you believe he may be struggling with traumatic experiences.
Anxiety Disorder Vignette
John returned from a combat deployment 9 months ago. Since returning, his family and coworkers have noticed changes in his behavior. He is often restless (or “on edge”), irritable, or physically tense in common, everyday situations. Plus, he has claimed poor or unsatisfying sleep for several months. These symptoms seem to be impairing his work performance and damaging personal relationships with loved ones. When asked, he hasn’t mentioned any particular traumatic events or worries that are bothering him. He simply seems much more anxious and it is affecting his well-being.
Depression Vignette
Jane returned from a combat deployment 3 months ago and has generally seemed a little bit down since coming home. Nearly every day over the past 2 weeks she has seemed to be sad or gloomy throughout the day and has shown very little interest in doing things she used to enjoy. She is clearly tired throughout the day and has mentioned feeling worthless to those around her. It seems like she is suffering greatly based on her unhappy and sad moods.
Substance Abuse Disorder Vignette
Jim returned from a combat deployment 12 months ago. Upon returning, he seemed to seamlessly reintegrate with his family, friends, and former social life. However, he soon began drinking alcohol more heavily than ever before, often binge drinking until passing out on weekdays and weekends. Although never caught in the act, he has even gone to work intoxicated and driven while drunk on multiple occasions. On two distinct occasions, he attempted to reduce his alcohol consumption but failed after only a week or two. Alcohol abuse is beginning to disrupt his work performance, family life, and physical well-being.
Sleep Problems Vignette
Joan returned from a combat deployment 4 months ago. She seems to have reintegrated very successfully into her family, social, and work environments. However, her sleep patterns have become very irregular and unsatisfactory. She rarely gets more than 4 hours of sleep consecutively and often uses her weekends to recover from a week of sleepless nights. Although her family and coworkers haven’t noticed anything wrong, Joan fears her sleep problems will soon begin disrupting her life.
Suicide Risk Vignette
James returned from a combat deployment 6 months ago. Since returning, he has outwardly seemed to have successfully reintegrated into his family, work, and social life. Although he appears to have been changed by his combat experiences, he does not seem to be generally troubled in any way (e.g., depressed, anxious, abusing drugs). However, he has jokingly mentioned “blowing his brains out” to colleagues at work and mentioned a specific plan to take his own life with his pistol. During a conversation with two friends, he has mentioned “ending it all” because he is feeling hopeless. You think James may be at risk for suicide.
Marital Problems Vignette
Jon returned from a combat deployment 5 months ago. He has rejoined his wife of 6 years, but their relationship has changed. While they used to feel very close and connected, they now both feel very distant. They do not enjoy activities together which they used to, such as hiking and dancing. They rarely hold good conversations with each other and are also less physically intimate. Jon and his wife both want their marriage to work but fear that they are nearing divorce. They are facing the most significant period of marital problems they have ever experienced.
Parenting Issues Vignette
Jerry returned from a combat deployment 10 months ago. He rejoined his wife of 16 years, their 13-year-old daughter, and their 5-year-old son. Since returning, Jerry has experienced some difficulty reassuming his role as a parent. His daughter seems to want very little to do with him. Although he thinks this is typical of a 13-year-old, it still causes him distress and he complains that he doesn’t feel like he has any influence in her life. With their son, Jerry often disagrees with his wife on discipline issues, and he can’t seem to find ways to connect with the 5-year-old. His son seems to have little interest in playing anything besides video games and always runs to his mother when Jerry attempts to discipline him. These parenting issues are significantly affecting Jerry’s mental and emotional well-being.
Anthony Hartman is a medical student at UT-Health San Antonio. Hope Schuermann is a clinical assistant professor at the University of Florida. Jovanna Kenney is a therapist at Genesis Psychiatric Center in San Antonio, TX. Correspondence can be addressed to Anthony Hartman, 7703 Floyd Curl Drive, San Antonio, TX 78229, hartmanaj@livemail.uthscsa.edu.