Examining Individual and Organizational Factors of School Counselor Burnout

Heather J. Fye, Ryan M. Cook, Eric R. Baltrinic, Andrea Baylin

Burnout is a statistically significant phenomenon for school counselors, correlated with various individual and organizational factors, which have been studied independently. Therefore, we investigated both individual and organizational factors of burnout conceptualized as a multidimensional phenomenon with 227 school counselors. Multidimensional burnout was measured by the five subscales of the Counselor Burnout Inventory, which included Exhaustion, Incompetence, Negative Work Environment, Devaluing Clients, and Deterioration in Personal Life. Using hierarchal regression analyses, we found individual and organizational factors accounted for 66.6% of the variance explained in Negative Work Environment, 38.3% of the variance explained in Deterioration in Personal Life, 36.7% of the variance explained in Incompetence, 35.1% of the variance explained in Exhaustion, and 14.0% of the variance explained in Devaluing Clients. We discuss implications of the findings for school counselors and supervisors. Identifying the multidimensions of burnout and its correlates, addressing self-care and professional vitality goals, communicating defined school counselor roles, providing mentoring opportunities, and increasing advocacy skills may help alleviate burnout.

Keywords: stress, burnout, job satisfaction, coping processes, school counselors


In addition to providing counseling services, school counselors are charged with performing multiple non-counseling duties in their schools (Bardhoshi et al., 2014). These multiple and competing demands place them at risk for experiencing burnout (Mullen et al., 2018). Accordingly, it is important to identify factors that contribute to burnout to promote school counselors’ psychological well-being (Kim & Lambie, 2018), which in turn reinforces school counselors’ ability to support students’ well-being (Holman et al., 2019).

Burnout is a workplace-specific complex construct characterized by feelings of exhaustion, cynicism, and detachment, and a lack of accomplishment and effectiveness (Maslach & Leiter, 2017). Others have conceptualized counselor burnout as a multidimensional construct, featuring the interaction between the individual and work environment (Lee et al., 2007). Given the complex, multidimensional, and interactional nature of burnout, the Counselor Burnout Inventory (CBI) was developed to measure the construct with five subscales: Exhaustion, Incompetence, Negative Work Environment, Devaluing Clients, and Deterioration in Personal Life (Lee et al., 2007). Specific to school counselors, Kim and Lambie (2018) suggested that burnout occurs to varying degrees across individual and organizational factors. Individual factors include perceived stress (Fye et al., 2018; Mullen et al., 2018; Mullen & Gutierrez, 2016; Wilkerson, 2009; Wilkerson & Bellini, 2006) and coping processes (Fye et al., 2018; Wilkerson, 2009; Wilkerson & Bellini, 2006). Organizational factors include perceived job satisfaction (Baggerly & Osborn, 2006; Bryant & Constantine, 2006; Mullen et al., 2018) and role stress (Bardhoshi et al., 2014; Coll & Freeman, 1997; Culbreth et al., 2005).

Researchers of school counselor burnout have studied individual and organizational factors of this phenomenon using a unidimensional structure such as the CBI scale score (Mullen et al., 2018). Other researchers (e.g., Bardhoshi et al., 2014; Moyer, 2011) studied organizational factors, including caseload and administrative (non-counseling) duties, within the multidimensional structure of the CBI (Lee et al., 2007). However, researchers have not yet comprehensively studied these known individual and organizational factors within the context of a multidimensional structure of school counselor burnout. For example, Mullen et al. (2018) investigated the relationships between perceived stress, perceived job satisfaction, and school counselor burnout. However, they did not examine organizational factors such as role stress (e.g., clerical duties), which are also significant to understanding school counselor burnout (Bardhoshi et al., 2014). Thus, we sought to extend the research findings by examining several individual factors (i.e., perceived stress, coping processes) and organizational factors (i.e., perceived job satisfaction, role stress) within a multidimensional structure of school counselor burnout.

Individual Factors

Individual factors related to school counselor burnout include psychological constructs and demographic factors (Kim & Lambie, 2018). The two psychological constructs included in the current study were perceived stress (Mullen et al., 2018) and coping processes (Fye et al., 2018). Researchers have previously found contradictory results for the relationship between years of experience and school counselor burnout (Mullen et al., 2018; Wilkerson, 2009). Therefore, the factor of years of experience was included in the current study.

Perceived Stress

Perceived stress is theorized as an individual’s ability to appraise a threatening or challenging event in relation to the availability of coping resources (Lazarus & Folkman, 1984). To that end, a transactional model of stress and coping suggests that stress is a response that occurs when perceived demands exceed one’s coping abilities. For school counselors, perceived stress may occur regularly because of various factors, including non-counseling duties, excessive paperwork and administrative duties, and work overload (Bardhoshi et al., 2014).

Researchers have described a positive relationship between stress and burnout among school counselors (Mullen et al., 2018; Mullen & Gutierrez, 2016). Specifically, higher levels of stress and burnout were related to lower levels of job satisfaction and delivery of direct student services (Mullen et al., 2018; Mullen & Gutierrez, 2016). Others have reported increased emotional responses alongside increased burnout (Wilkerson & Bellini, 2006). For example, school counselors who attempted to deal with stress emotionally may be at greater risk for developing symptoms of burnout including emotional exhaustion, depersonalization, and lower levels of personal accomplishment (Wilkerson, 2009). Additionally, school counselors reported higher levels of emotional exhaustion than other mental health professionals, which can negatively impact their delivery of school counseling services (Bardhoshi et al., 2014). The correlation between stress and burnout further highlights the importance of assessing the components of stress and the ways school counselors are coping with these factors.

Coping Processes

Coping processes are defined as the cognitive and behavioral processes used to manage stressful situations (Folkman & Moskowitz, 2004). There are several coping processes, including problem-focused coping, active-emotional coping, and avoidant-emotional coping (Folkman & Lazarus, 1985). For example, problem-focused coping is defined as an action-oriented approach to stress in which one believes the stressors are controllable by personal action (Lazarus, 1993). Active-emotional coping is an adaptive response to unmanageable stressors and avoidant-emotional coping is described as a maladaptive response to those stressors (Folkman & Lazarus, 1985).

Among school counselors, Fye et al. (2018) studied the relationship between perfectionism, burnout, stress, and coping. These authors found that maladaptive perfectionists engaged more frequently in avoidant-emotional coping and relatedly experienced higher levels of burnout. Moreover, adaptive perfectionists experienced less stress and burnout and reported higher levels of problem-focused coping. Overall, for school counseling professionals, emotional-focused coping is positively related to burnout (Wilkerson, 2009). Given these findings, it is imperative for school counselors to be aware of their coping processes, including the degree to which they are affecting their levels of stress and burnout (Wilkerson, 2009).

Organizational Factors

In addition to individual factors such as stress and coping (Fye et al., 2018; Mullen et al., 2018; Wilkerson, 2009), school counseling researchers noted several organizational factors as contributing to school counselor burnout (Holman et al., 2019; Kim & Lambie, 2018). Accordingly, researchers in the current study examined organizational factors, including perceived job satisfaction and role stress (i.e., role ambiguity, role incongruity, and role conflict; Culbreth et al., 2005). Additionally, because previous researchers found a relationship between the organizational factor of school district (e.g., urban setting) and burnout (Butler & Constantine, 2005), this variable was included in the present study.

Perceived Job Satisfaction

Perceived job satisfaction refers to the degree of affective or attitudinal reactions one experiences relative to their job (Spector, 1985). Understanding the extent of school counselors’ perceived job satisfaction may be one way to buffer the effects of stress and burnout. This is because, according to Bryant and Constantine (2006), job satisfaction predicted life satisfaction for school counselors.

Perceived job satisfaction and its relationship with stress and burnout have received increased attention in the school counseling literature (Mullen et al., 2018). Among the contributing factors, higher levels of role balance and increased perceived job satisfaction resulted in greater overall life satisfaction (Bryant & Constantine, 2006). Higher perceived job satisfaction has been aligned with school counselors engaging in appropriate roles. For example, Baggerly and Osborn (2006) found that school counselors who frequently performed roles aligned with comprehensive school counseling programs were more satisfied and more committed to their careers. Similarly, higher perceived job satisfaction was directly related to the school counselor’s ability to provide direct student services within their schools (Kolodinsky et al., 2009). Conversely, school counselors who did not intend to return to their jobs the following year reported higher levels of demand and stress because of non-counseling duties, such as excessive paperwork and administrative disruptions (McCarthy et al., 2010). As a result, those who are not satisfied are at risk for disengagement (Mullen et al., 2018), while school counselors who are satisfied with their jobs may have increased student connections (Kolodinsky et al., 2009).

Role Stress

    Role stress refers to the levels of role incongruity, role conflict, and role ambiguity experienced by school counselors (Culbreth et al., 2005; Freeman & Coll, 1997). Role incongruity may occur when there are structural conflicts, including inadequate resources for school counselors and engagement in ineffective tasks (Freeman & Coll, 1997). Several authors noted that inappropriate or non-counseling duties contributed to burnout, including excessive paperwork, administrative duties, and testing coordinator roles (Bardhoshi et al., 2014; Moyer, 2011, Wilkerson, 2009). Moyer (2011) found that school counselors who engaged in increased non-counseling duties also had increased feelings of exhaustion and incompetence, had decreased feelings toward work environment, and were less likely to show empathy toward students. Furthermore, school counselors who were assigned inappropriate roles reported higher levels of frustration and resentment toward the school system. Overall, authors emphasized the importance of educating administrators on the appropriate and inappropriate roles for school counselors to decrease burnout (Bardhoshi et al, 2014; Cervoni & DeLucia-Waack, 2011; Moyer, 2011).

Role conflict occurs when school counselors experience multiple external demands from different stakeholders (Holman et al., 2019). Role conflict examples for school counselors include: (a) whether school counselors should focus on the education goals or mental health needs of students first (Paisley & McMahon, 2001) and (b) whether a school counselor should engage in an actual role given by an administration or supervisor (e.g., testing coordinator) or preferred role (e.g., classroom guidance activity; Wilkerson, 2009). As such, school counselors can feel overwhelmed and often engage in inappropriate duties, according to the American School Counselor Association (ASCA) National Model (2019). In turn, school counselors experience stress and burnout (Mullen et al., 2018).

Role ambiguity is the discrepancy between actual and preferred counseling duties (Scarborough & Culbreth, 2008). Role ambiguity has been linked to burnout because of school counselors’ stress from lacking an understanding of their professional roles and being misinformed about the realities of the job (Culbreth et al., 2005). For example, school counselors face challenges of navigating mixed messages about role expectations across stakeholders (Coll & Freeman, 1997). This confusion may lead to school counselors experiencing role ambiguity (Scarborough & Culbreth, 2008). When school counselors interact with stakeholders who have conflicting ideas about their roles, it creates stress. It is especially difficult for school counselors when stakeholders’ conceptualization of their roles clashes with what school counselors learned during graduate training (Culbreth et al., 2005). When school counselors are assigned duties that conflict with their own understandings of their roles, they are not able to operate in alignment with their professional mandates (Holman et al., 2019). Overall, school counselors experiencing role ambiguity also report higher levels of stress, both of which have been linked to burnout (Kim & Lambie, 2018).

Purpose of the Present Study
Despite prevalence in the school counseling burnout literature regarding individual and organizational factors of burnout, we were unable to locate a study that holistically researched these variables. To align our findings with a theoretical understanding of school counselor burnout, we examined these phenomena as a multidimensional construct. Additionally, we controlled for years of experience (Mullen et al., 2018; Wilkerson, 2009; Wilkerson & Bellini, 2006) and school district (Butler & Constantine, 2005). Therefore, we answered the research question: What is the relationship between individual (i.e., perceived job stress, problem-focused coping, avoidant-emotional coping, and active-emotional coping) and organizational (i.e., perceived job satisfaction, role incongruity, role conflict, and role ambiguity) factors after controlling for years of experience and school district, with the subscales of school counselor burnout: (1) Exhaustion, (2) Incompetence, (3) Negative Work Environment, (4) Devaluing Clients, and (5) Deterioration in Personal Life?



A total of 227 school counselors participated in the study. Ages ranged from 26 to 69 (M = 46.21; SD = 10.26; four declined to answer). The sex of participants included females (n = 166, 73.1%) and males (n = 61, 26.9%). The race and ethnicity of participants included White (n = 185, 81.5%), African American/Black (n = 20, 8.8%), Hispanic (n = 7, 3.1%), Asian/Pacific Islander (n = 3, 1.3%), American Indian/Alaskan Native (n = 1, 0.4%), and Biracial/Multiracial (n = 9, 4.0%), and two participants (0.9%) declined to answer. Participants held a master’s degree in school counseling (n = 175, 77.1%), a PhD or EdD (n = 33, 14.5%), or a master’s degree in another counseling or mental health specialty area (n = 19, 8.4%). The years of experience ranged from 2 to 41 years (M = 13.68, SD = 7.49). Participants reported working in suburban (n = 97, 42.7%), rural (n = 76, 33.5%), and urban (n = 54, 23.8%) settings. Regarding level of practice, participants worked in an elementary school (i.e., grades K–6; n = 80, 35.2%), middle school (i.e., grades 7–8; n = 14, 6.2%), high school (i.e., grades 9–12; n = 59, 26.0%), or multiple grade levels (e.g., K–8, K–12, etc.; n = 74, 32.6%). A power analysis was completed in G*Power 3.1 before beginning the study (Faul et al., 2009). The necessary sample size was determined to be at least 200, with a power of .80, assuming a moderate effect size of .15 in the multiple regression analyses, and with an error probability or alpha of .05 (J. Cohen, 1992).


Institutional Review Board approval was obtained prior to beginning the study. The first author sent recruitment emails to 4,000 school counselors who were professional members of the ASCA online membership directory. Specifically, approximately 20% of school counselors in each of the 50 states and District of Columbia were chosen from the membership directory to receive the recruitment emails. The emails included a brief introduction to the study and an anonymous link that took potential participants to the online survey portal in Qualtrics. Potential participants first reviewed the informed consent. Once they consented to the survey, participants completed the demographics questionnaire and instruments. A convenience sample was obtained based upon voluntary responses to the survey (Dimitrov, 2009).


The first author constructed a brief demographics survey to gather information about the participants (e.g., age, sex, race and ethnicity, degree, and years of experience) and their work environment (e.g., school district, grade level). The Perceived Stress Scale (PSS; S. Cohen et al., 1983) and Brief COPE (Carver, 1997) were used to measure individual factors. The Job Satisfaction Survey (JSS; Spector, 1985) and Role Questionnaire (RQ; Rizzo et al., 1970) were used to measure organizational factors. The CBI (Lee et al., 2007) was used to measure the dimensions of school counselor burnout.

Perceived Stress Scale (PSS)

The PSS (S. Cohen et al., 1983) is a 14-item inventory designed to measure an individual’s perceived stress within the past month. In the present study, we used the PSS-4, which is a subset of items from the original 14-item scale. The PSS was normed on a large sample of individuals from across the United States (S. Cohen et al., 1983). Participants responded to a 5-point Likert-type scale ranging from 0 (never) to 4 (very often). Scores on the PSS-4 ranged from 0 to 20. An example question of the PSS-4 is: “In the past month, how often have you felt difficulties were piling up so high that you could not overcome them?” The PSS-4 was determined to be a suitable brief measure of stress perceptions, based upon adequate factor structure and predictive validity (S. Cohen & Williamson, 1988). Reliability has been upheld (e.g., S. Cohen & Williamson, 1988) with test-retest reliability at .85 after 2 days (S. Cohen et al., 1983). For the present study, the internal consistency reliability was calculated at α = .76. Correlations between the perceived stress total score and CBI subscales ranged from r = .19 to .55.

Brief COPE

The Brief COPE (Carver, 1997) is a 28-item inventory designed to measure coping responses or processes and includes 14 subscales. We followed previous researchers’ (e.g., Deatherage et al., 2014) grouping of the 14 subscales into three coping processes (i.e., problem-focused, active-emotional, and avoidant-emotional). Therefore, problem-focused coping contained the Active Coping, Planning, Instrumental Support, and Religion subscales. Active-emotional coping contained the Venting, Positive Reframing, Humor, Acceptance, and Emotional Support subscales. Avoidant-emotional coping contained the Self-Distraction, Denial, Behavioral Disengagement, and Self-Blame subscales. For the present study, the items pertaining to participants’ alcohol and illegal drug use as coping responses were omitted because of their sensitive nature. Therefore, 26 items were included in the present study. The inventory uses a 4-point Likert-type scale with scores ranging from 0 (I haven’t been doing this at all) to 3 (I’ve been doing this a lot). A sample item on the Brief COPE is “I’ve been turning to work or other activities to take my mind off things.” Construct validity has been upheld with the three coping processes (e.g., Deatherage et al., 2014). Test-retest reliability for the three subscale groups has been upheld over a year timespan (Cooper et al., 2008). For the present study, the internal consistency reliability was calculated for problem-focused coping at α = .84, avoidant-emotional coping at α = .70, and active-emotional coping at α = .81. Correlations between problem-focused coping and the CBI subscales ranged from r = .00 to .13, correlations between avoidant-emotional coping and CBI subscales ranged from r = .20 to .48, and correlations between active-emotional coping and CBI subscales ranged from r = .01 to .16.

Job Satisfaction Survey (JSS)

The JSS (Spector, 1985) is a 36-item inventory intended to measure an individual’s perceived job satisfaction or attitudes and aspects of the job. The JSS contains nine subscales: Pay, Promotion, Supervision, Fringe Benefits, Contingent Rewards, Operating Procedures, Coworkers, Nature of Work, and Communication. The inventory uses a 6-point Likert-type scale with scores ranging from 1 (disagree very much) to 6 (agree very much). Total scores range from 36 to 216 with the higher the score, the higher job satisfaction experienced. An example item on the JSS is “My job is enjoyable” (Spector, 1985, p. 711). The JSS was constructed for, and normed on, social service, education, and mental health professionals (Spector, 1985, 2011). Spector (1985) established convergent validity with the Job Descriptive Index (Smith et al., 1969), and produced scores ranging from .61 to .80. Strong reliability has been established for the JSS, including a Cronbach coefficient alpha of .91 for all factors combined, and at 18 months, the test-retest reliability score was .71 (Spector, 1985). For the present study, the internal consistency reliability was calculated for the total scores at α = .91. Correlations between the perceived job satisfaction total score and CBI subscales ranged from r = -.13 to -.75.

Role Questionnaire (RQ)

The RQ (Rizzo et al., 1970) is a 14-item inventory designed to measure the level of role conflict and role ambiguity an individual has about a job. The RQ has been factor analyzed with school counselors (Freeman & Coll, 1997) and found to have three distinct factors (i.e., role incongruity, role conflict, and role ambiguity). The inventory uses a 7-point Likert-type scale with scores ranging from 1 (very false) to 7 (very true). Role incongruity refers to conflicts with the structure of the system and allocation of resources (Freeman & Coll, 1997). The role incongruity factor comprises items 1–4. Total scores range from 8 to 32, with the higher the score, the higher role incongruity experienced. A sample item for role incongruity is “I receive an assignment without adequate resources and materials to execute it.” Role conflict refers to the contradictory requests of work expectations with varying groups (Freeman & Coll, 1997). The role conflict factor comprises items 5–8. The higher the score, the higher role conflict experienced, which can range from 8 to 32. A sample item for role conflict is “I receive incompatible requests from two or more people.” The role ambiguity factor, which measures a lack of clarity on the job, is negatively worded; therefore, the lower the score, the higher the role ambiguity experienced. The role ambiguity factor comprises items 9–14, and total scores range from 6 to 42. A sample item for role ambiguity is “Explanation is clear of what has to be done.” Construct validity for the three factors with school counselors was established by Freeman and Coll (1997). Reliability of the three factors have been upheld for school counselor participants (Culbreth et al., 2005; Wilkerson, 2009; Wilkerson & Bellini, 2006). For the present study, the internal consistency reliability was calculated for role incongruity at α = .82, role conflict at α = .79, and role ambiguity at α = .90. Correlations between role incongruity and CBI subscales ranged from r = .14 to .65, correlations between role conflict and CBI subscales ranged from r = .14 to .53, and correlations between role ambiguity and CBI subscales ranged from r = -.22 to -.56.

Counselor Burnout Inventory (CBI)

The CBI (Lee et al., 2007) is a 20-item inventory designed to measure counselors’ burnout levels. The CBI includes five subscales, with four questions for each subscale: Exhaustion, Incompetence, Negative Work Environment, Devaluing Clients, and Deterioration in Personal Life. The CBI uses a 5-point Likert-type scale ranging from 1 (never true) to 5 (always true). Total scores on each subscale range from 5 to 20, with the higher the score, the higher level of burnout. A sample item from the Exhaustion subscale is “Due to my job as a counselor, I feel tired most of the time.” A sample item from the Incompetence subscale is “I am not confident in my counseling skills.” A sample item from the Negative Work Environment subscale is “I am treated unfairly in my workplace.” A sample item from the Devaluing Clients subscale is “I am not interested in my clients and their problems.” A sample item from the Deterioration in Personal Life subscale is “I feel I have poor boundaries between work and my personal life.” Two independent samples composed of counselors from a variety of settings across the United States were used to explore and confirm the factor structure (Lee et al., 2007). Gnilka et al. (2015) upheld the CBI five-factor structure with a confirmatory factor analysis in a sample of school counselors. Cronbach’s alpha for the total CBI was .88, with scores ranging from .73 to .85 for the subscales (Lee et al., 2007). For the present study, internal consistency reliability for the CBI subscales were calculated and ranged from α = .78 to .89.


Prior to conducting the primary analyses, we used SPSS (Version 25.0) to clean the data, impute missing data values, and test the assumptions of the primary analyses (i.e., hierarchal regressions), as recommended by Tabachnick and Fidell (2013). We used expectation-maximization (EM) to impute missing data (Cook, 2020), after we tested the randomness of the missing values with Little’s missing completely at random (MCAR). All missing values were determined to be MCAR, except for the active-emotional coping of the Brief COPE and the JSS: χ2(40, N = 227) = 79.13, p = .000, and χ2(671, N = 227) = 836.57, p = .000, respectively. Because the missing values for the active-emotional coping and JSS were less than 1%, expectation-maximization was an appropriate imputation method (Cook, 2020). Less than 5% of values were imputed for the PSS-4, the factors of the RQ (role ambiguity, role incongruity, and role conflict), and the five subscales of the CBI (Exhaustion, Incompetence, Negative Work Environment, Devaluing Clients, and Deterioration in Personal Life), and less than 1% of the values were imputed for the problem-focused and avoidant-emotional processes of the Brief COPE.

To answer the research question, we used three-step hierarchical regression models to analyze the individual and cumulative contributions for demographic, individual, and organizational factors with each subscale of the CBI. Qualities of the instruments are provided in Table 1. In Step 1, we entered the demographic factors (i.e., years of experience and school district). In Step 2, we entered the individual factors (i.e., perceived stress, problem-focused coping, avoidant-emotional coping, and active-emotional coping). In Step 3, we entered the organizational factors (i.e., perceived job satisfaction, role incongruity, role conflict, and role ambiguity). Completed assumption checks showed no outliers or influential data points, as concluded by an examination of the Q-Q plots, histograms, scatterplots, and Mahalanobis distance. We checked multicollinearity and found it to be an issue for school district (tolerance < .01). Therefore, we removed the school district variable and reentered years of experience in Step 1. To control for Type I error, we used the Bonferroni method to adjust the family-wise alpha (Darlington & Hayes, 2017), which resulted in .01 as the cutoff for statistical significance for Step 2 (i.e., individual factors) and .0056 as the cutoff for statistical significance for Step 3 (i.e., organizational factors). Results for each of these models are presented in Table 2.


Table 1

Qualities of Instrumentation

Instrumentation  Scores      M    SD   α
Perceived Stress Scale-4 Total Score


Problem-Focused Coping


Avoidant-Emotional Coping


Active-Emotional Coping


Job Satisfaction Scale Total Score


Role Ambiguity


Role Incongruity


Role Conflict






Negative Work Environment


Devaluing Client


Deterioration in Personal Life






































































































Table 2

Results of Hierarchal Regression Analyses of School Counselor Burnout

Exhaustion Incompetence Negative Work Environment Devaluing Clients Deterioration in Personal Life
Step 1
Years of Experience    -.038        -.233*        -.072      -.190*         -.047
R2     .001         .054         .005       .036          .002
F     .323     12.89**       1.17     8.46*          .500
Step 2  
Years of Experience     .030       -.151**       -.042      -.155          .001
Perceived Stress     .392**         .184         .283**       .093          .491**
Avoidant-Emotional Coping     .160         .360**         .025       .180          .103
Active-Emotional Coping     .030         .087         .026       .131          .151
Problem-Focused Coping    -.043        -.151         .081      -.229**         -.105
R2     .240         .284         .109       .116          .323
Δ R2     .239         .229         .104       .080          .321
ΔF 17.34**     17.69**       6.43**     4.98**      26.24**
Step 3
Years of Experience     .056        -.097         .052      -.125          .025
Perceived Stress     .303         .150         .057       .070          .437
Avoidant-Emotional Coping     .170         .338         .025       .165          .077
Active-Emotional Coping     .034         .126         .050       .151          .155
Problem-Focused Coping    -.064        -.180         .042      -.243         -.127
Perceived Job Satisfaction    -.198         .080        -.489       .032          .029
Role Ambiguity     .014        -.276        -.122      -.147         -.029
Role Incongruity     .207         .190         .220       .069          .172
Role Conflict   -.014        -.096         .106      -.018          .188
R2     .351         .367         .666       .140          .383
Δ R2     .111         .092         .652       .024          .060
ΔF   9.29**       8.03**     90.43**     1.51        5.26**
Note. N = 227
* p < .05. ** p < .01. p < .0056.



The hierarchical regression model for Exhaustion revealed that years of experience was not statistically significant: F(1, 225) = .323, p > .05. Introducing individual factors explained 23.9% of the variation in Exhaustion, and this change in R2 was significant: F(5, 221) = 13.96, p < .001. The inclusion of organizational factors explained an additional 11.1% of the variation in Exhaustion, and this change in R2 was significant: F(9, 217) = 13.05, p < .001. However, the β values revealed that the only statistically significant factor of Exhaustion was perceived stress (β = .303, p < .001). Together the independent variables accounted for 35.1% of the variance in Exhaustion.


For Incompetence, years of experience explained 5.4% of its variation and was significant: F(1, 225) = 12.89, p < .001. Adding individual factors explained an additional 22.9% of the variation in Incompetence, and this change in R2 was significant: F(5, 221) = 17.50, p < .001. Including organizational factors explained an additional 9.2% of the variation in Incompetence, and this change in R2 was significant: F(9, 217) = 14.53, p < .001. The statistically significant factors of Incompetence were avoidant-emotional coping (β = .338, p < .001) and role ambiguity (β = -.276, p < .001). Together the independent variables accounted for 36.7% of the variance in Incompetence.

Negative Work Environment

      For Negative Work Environment, years of experience was not statistically significant: F(1,225) = 1.17, p > .05, R2 = .005. Adding individual factors explained 10.9% of the variation in Negative Work Environment, and this change in R2 was significant: F(5, 221) = 5.40, p < .001. Including organizational factors explained an additional 65.2% of the variation in Negative Work Environment, and this change in R2 was significant: F(9, 217) = 48.05, p < .001. In the final model, perceived job satisfaction (β = -.489, p = .000) and role incongruity (β = .220, p = .000) significantly explained Negative Work Environment. Together the independent variables accounted for 66.6% of the variance in Negative Work Environment.

Devaluing Clients

For Devaluing Clients, years of experience contributed significantly to the model and accounted for 3.6% of its variation: F(1, 225) = 8.46, p < .05. Including individual factors explained an additional 8.0% of the variation in Devaluing Clients, and this change in R2 was significant: F(5, 221) = 5.80, p < .01. Adding the organizational factors in the third step was significant: F(9, 217) = 3.92, p < .001, R2 = .140. However, the inclusion of the organizational variables did not explain a significantly different equation: ΔF(4, 217) = 1.51, p > .05, ΔR2 = .024. Therefore, we interpreted the β values of the second step, and the statistically significant factor of Devaluing Clients was problem-focused coping (β = -.229, p = .009).

Deterioration in Personal Life

Finally, for Deterioration in Personal Life, years of experience was not significant: F(1, 225) = .500,
p > .05, R2 = .002. Including individual factors explained 32.1% of the variation in Deterioration in Personal Life, and the change in R2 was significant: F(5, 221) = 21.14, p < .001. Including the organizational factors explained an additional 6.0% of the variation in Deterioration in Personal Life, and this change in R2 was significant: F(9, 217) = 14.98, p < .001. An examination of the β values revealed that only perceived stress was a statistically significant variable for Deterioration in Personal Life (β = .437, p = .000). Together the independent variables accounted for 38.3% of the variance in Deterioration in Personal Life.


The present study illustrates an expanded understanding of individual and organizational factors associated with the subscales of school counselor burnout (i.e., Exhaustion, Incompetence, Negative Work Environment, Devaluing Clients, and Deterioration in Personal Life; Lee et al., 2007). We intended to control for years of experience but found that before adding the individual and organizational factors, it was a statistically significant variable and negatively related with Incompetence and Devaluing Clients. School counselor researchers have reported contradictory findings between years of experience and burnout. Similar to our findings, Wilkerson and Bellini (2006) and Mullen et al. (2018) reported a negative relationship between years of experience and burnout—essentially describing that those earlier in their careers have a higher risk of experiencing burnout. In contrast, Butler and Constantine (2005) and Wilkerson (2009) reported burnout happening over time (i.e., a positive relationship between years of experience and burnout). Our study underscores the vulnerability school counselors may experience earlier in their careers (Mullen et al., 2018). Our results also provide a unique finding in that fewer years of experience as a school counselor is associated with the burnout dimensions of Incompetence and Devaluing Clients.

In the present study, we found individual factors (i.e., perceived stress, problem-focused coping, and avoidant-emotional coping) significantly related to Exhaustion, Incompetence, Devaluing Clients, and Deterioration in Personal Life. School counselor scholars (e.g., Mullen et al., 2018; Mullen & Gutierrez, 2016) reported a statistically significant positive relationship between school counselors’ perceived stress and burnout. Our results provide unique findings in that stress was positively related with the Exhaustion and Deterioration in Personal Life dimensions of burnout. Other school counselor scholars (e.g., Bardhoshi et al., 2014; Moyer, 2011) found the stress-related variable of engagement in non-counseling duties was significantly related to Exhaustion and Deterioration in Personal Life.

For the coping processes, avoidant-emotional coping was positively related to Incompetence and problem-focused coping was negatively related to Devaluing Clients. These findings provide two distinct understandings of school counselor burnout. First, and notably, school counselor participants who were experiencing Incompetence were also engaging in increased avoidant-emotional coping. This finding is similar to those of Fye et al. (2018), who found maladaptive perfectionists were more frequently engaging in avoidant-coping processes. We did not research perfectionism in the present study; however, our findings may expand an understanding of a positive relationship between avoidant-emotional coping and burnout dimensions for school counselors regardless of perfectionism types. Second, we discovered school counselor participants’ problem-focused coping was negatively related to Devaluing Clients. This is a promising finding from our study because participants were likely to incorporate increased problem-focused coping alongside valuing students. As previously discussed, it appears that these school counselor participants were maintaining high levels of positive regard and empathy for students (Gnilka et al., 2015; Mullen & Gutierrez, 2016). Engaging in problem-focused coping may be beneficial to their engagement in student care and maintaining professional vitality.

The organizational factors of role ambiguity, role incongruity, and perceived job satisfaction were significantly related to the Incompetence and Negative Work Environment dimensions of burnout. Specifically, role ambiguity was positively related to Incompetence. Our results confirm that when school counselors’ roles are increasingly unclear, they are experiencing higher levels of burnout (Mullen et al., 2018), and specifically Incompetence. Perceived job satisfaction was negatively related to Negative Work Environment, while role incongruity was positively related to Negative Work Environment. Consistent with previous research, our findings support the significant relationships between organizational factors (i.e., administrative and clerical duties contributing to role stress) and Negative Work Environment (Bardhoshi et al., 2014). Other scholars have studied perceived job satisfaction as an outcome and potential preclusion to school counselor burnout (Baggerly & Osborn, 2006; Bryant & Constantine, 2006). School counseling scholars have found that burnout mediated the relationship between perceived stress and perceived job satisfaction (Mullen et al., 2018). In the present study, the perceived job satisfaction factor had the highest β at -.489. It appears that perceived job satisfaction is an important factor alongside school counselors’ specific experiences of Negative Work Environments. Perceived stress was a statistically significant factor in Step 2 with Negative Work Environment, but insignificant in the context of the organizational variables. This is an important finding because burnout, by definition, is a function of one’s work context (Lee et al., 2007; Maslach & Leiter, 2017), and we found that organizational factors explained a large amount of the variance (i.e., 65.2%) for the Negative Work Environment dimension of burnout. Overall, our findings support the complex and multidimensional nature of school counselor burnout.

Limitations and Future Research

     We attempted to research multidimensional burnout with a nationally representative and diverse sample of ASCA member school counselors. Despite our efforts, the response rate was 5.68%. The majority of our participants identified as White and female, which is similar to the reported demographics of professional school counselor members (ASCA, 2018). However, caution may be warranted when generalizing our findings to all school counselors. Expanding research efforts (i.e., qualitative methods) to increase understanding of the burnout experiences of school counselors unrepresented by our participant sample is warranted. Last, it is unknown whether or not participants answered sensitive questions, such as those about burnout, in a socially desirable manner.

Future research should seek to understand additional individual and organizational variables related to the burnout dimensions for school counselors (Lee et al., 2007). For example, the Devaluing Clients dimension has been viewed by school counseling scholars as a complicated construct that has functioned differently from the other dimensions of burnout (Bardhoshi et al., 2014; Mullen & Gutierrez, 2016). Additional research is needed to understand this burnout dimension with school counselors. Kim and Lambie (2018) discussed the need for research to focus on burnout interventions. We concur and believe the distinction of individual and organizational factors within the dimensions of school counselor burnout should be considered when constructing these interventions, which may be important because burnout may not be an end state; instead, it may be a mediator of other important outcomes, such as work and health (Maslach & Leiter, 2017). It may be helpful to expand research that studies relationships between school counselor burnout and physical and mental health outcomes.       

Implications for the School Counseling Profession

Our findings have implications for school counselors, school counselors-in-training, and counselor educators and supervisors. They illustrate the importance of conceptualizing the ecological relationship between individual and organizational factors with school counselor burnout. School counselors may have more control over individual factors, and supervisors may have more control over organizational factors. Despite these considerations, it is important to share the responsibility of burnout prevention within the school system. This is important because despite one’s efforts to increase helpful coping, self-care, or wellness practices, it appears that continued exposure to negative work environments will continue to place school counselors at risk for burnout.

Because school counselors are responsible for providing counseling services that align with professional and ethical standards (Kim & Lambie, 2018), it is imperative for them to recognize, monitor, and address their symptoms of burnout (ASCA, 2016). Therefore, it may be helpful for school counselors and supervisors to identify and understand the dimensions of burnout experienced and their relationships with individual and organizational factors. By using the instruments from this study, school counselors can identify contributions of individual and organizational factors with their burnout scores. This would allow supervisees to understand the relationships between these factors and burnout dimensions. During supervision, time could be dedicated to setting personal goals for maintaining self-care and professional vitality. This may be important, especially in identifying and decreasing avoidant-emotional coping, alongside increasing problem-focused coping processes. In general, school counselors should monitor their own self-care in relation to work context stressors and perceived job satisfaction. Our results may provide support to the potential limitations that wellness practices have on decreasing burnout within the Negative Work Environment (Puig et al., 2012)—meaning, wellness practices may be important in alleviating the individual factors related to burnout (i.e., high perceived stress, coping responses) but may have limited ability to decrease factors out of school counselors’ control (i.e., work context practices and policies).

Despite best practice guidelines, the reality remains that school counselors engage in various non-counseling duties (Bardhoshi et al., 2014; Gutierrez & Mullen, 2016), which contributes to role stress. To lessen organizational stressors, as early as graduate school, counselor educators and supervisors should allow space in the learning process for students to learn the various counseling and related duties expected of school counselors within the school environment. Providing learning contexts for graduate students to explore these various roles may set the stage for lessened role stress. Specifically, assignments should be included in the curriculum that allow graduate students to explore school counselors’ professional identity and the real and ideal roles of the school counselor. These discussions should be engaged in along with conversations of how these varying roles can affect burnout (specifically role incongruity and role ambiguity), especially for those earlier in their careers. These dialogues should be reinforced during the practicum and internship experiences and include personal sources of perceived job satisfaction. In schools, supervisors can help to facilitate school counselors’ competence by clearly defining expectations through measurable outcomes. For example, school counselors and supervisors can use the ASCA National Model’s (ASCA, 2019) Annual Administrative Conference Template (p. 60) and Annual Calendar Template (p. 70) to open communication between the school counselors and their supervisors and document their duties. This discussion may additionally open communication regarding the adequacy of funding, resources, materials, and staff available to school counselors (Freeman & Coll, 1997). If inadequate, school counselors may use the opportunity to advocate for increased support from supervisors and administrators.

It is important to note that in the present study, school counselors earlier in their careers reported higher levels of Incompetence and Devaluing Clients. School counselor supervisors should understand these relationships. Mentoring of school counselors who are earlier in their careers by those with significant experience may help the younger professionals build their professional identities and student-focused work. Last, recognizing dimensions of burnout in relation to individual and organizational factors may not be enough to maintain professional vitality. The school counseling profession may find it helpful to train school counselors and graduate students in advocacy skills. Trusty and Brown (2005) outlined advocacy competencies for school counselors, which include dispositional statements, knowledge, and skills necessary to becoming effective advocates. The self-advocacy model prepares school counselors to have the communication (oral and written) necessary to maintain effective advocacy roles.


In conclusion, our results provide an expansion of findings related to relative contributions for individual and organizational factors with school counselor multidimensional burnout. In short, burnout dimensions are uniquely related to personal and work context factors. It is difficult to conceive of burnout absent its relationship to some aspect of the work setting. School counselors and supervisors can use our results to conceptualize burnout from a multidimensional perspective, which may in turn help them find new ways to remain professionally vital to themselves, their students, and their school community.


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



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Heather J. Fye, PhD, NCC, LPC, is an assistant professor at the University of Alabama and a certified PK–12 school counselor. Ryan M. Cook, ACS, LPC, is an assistant professor at the University of Alabama. Eric R. Baltrinic, LPCC-S, is an assistant professor at the University of Alabama. Andrea Baylin, NCC, PEL, is a doctoral student at the University of Alabama. Correspondence may be addressed to Heather Fye, Box 870231, Graves Hall 315B, Tuscaloosa, AL 35487, hjfye@ua.edu.

Counselors and Workplace Wellness Programs: A Conceptual Model

Yvette Saliba, Sejal Barden

Occupational stress is a top source of stress for over 65% of Americans due to extended hours in the workplace. Recent changes in health care have encouraged employers to build workplace wellness programs to improve physical and mental health for employees to mitigate the effects of occupational stress. Wellness programs focus on either disease management; treating chronic illnesses, such as hypertension and diabetes; lifestyle management; or preventing chronic illnesses through health promotion. This manuscript provides an overview of recent changes in health care and describes a conceptual framework, Steps to Better Health (S2BH), that counselors can use in workplace wellness programs. S2BH is an 8-week psychoeducational group based on the combination of motivational interviewing (MI) and the transtheoretical model of change (TTM).


Keywords: wellness, health care, workplace, stress, Steps to Better Health


Health and wellness are two concepts that have captured the attention of people throughout history. From Greek mythology to modern times, the idea of well-being has permeated society (Myers & Sweeney, 2007). Today, with the Patient Protection and Affordable Care Act (PPACA), health care is moving away from a disease treatment model and embracing a disease prevention model (PPACA, 2010). Although individuals typically do not invest in preventive health measures, many businesses and companies are eager to improve their health care programs for employees (Willis Towers Watson, 2017). These changes in health care are relevant to mental health providers, as a new focus on prevention has created opportunities for counselors to help effect lasting health changes among employees. Therefore, to fit into this paradigm shift, professional counseling should be strongly connected to prevention and wellness (Granello, 2013). This article discusses the changes in health care models, how those changes are creating spaces for mental health counselors to fill and implications for the counseling profession.


The Changing Landscape of Health Care

In 2015, the Kaiser Family Foundation released a report highlighting the rising cost of health care expenditures from 1960 to 2013. This report indicated that health care costs, which include total costs for hospital visits, physicians and clinics, as well as prescription medications, have risen from 27.4 billion dollars to over $2 trillion (Kaiser Family Foundation, 2015). Due in part to increases in the cost of health care and health insurance, the PPACA was passed into federal law in 2010. Mandates of the PPACA include: (a) preventing the denial of coverage for pre-existing conditions; (b) strengthening community health centers; (c) decreasing health disparities; (d) promoting integrated health systems; (e) connecting physician payments to the quality rather than the quantity of care provided; and (f) lowering long-term costs by providing free and more comprehensive preventive care (U.S. Department of Health and Human Services, Health Care, 2016). In a White House memo sent out during National Public Health Week in 2014, President Obama stated, “my administration is supporting efforts across our country to improve public health and shift the focus from sickness and disease to wellness and prevention” (Obama, 2014, p. 1).

This shift is clearly seen in the PPACA. Section 4001 of the PPACA, entitled “Modernizing Disease Prevention and Public Health Systems,” discusses ways in which health prevention should be carried out within the public sector (PPACA, 2010). This portion of the law includes a taskforce team that would: (a) evaluate wellness programs in 2013; (b) create the Prevention and Public Health Fund to distribute money to worksites establishing wellness programs; (c) further the education of health and wellness promotion; and (d) report on measures enacted that address lifestyle behavior modification (PPACA, 2010). Lifestyle behavior modification is defined as activities that include “smoking cessation, proper nutrition, appropriate exercise, mental health, behavioral health, substance use disorder, and domestic violence screenings” (PPACA, 2010, p. 422). In other words, initiatives from the federal government highlight the emphasis on prevention in both community and clinical health venues and extend this focus by supporting research into workplace wellness initiatives (Anderko et al., 2012). Though the PPACA encourages workplace wellness programs, many employers see the benefits to their employees even without federal regulations. In a recent survey, employers indicated they are still committed to better workplace wellness programs despite the unknown future of the PPACA (Willis Towers Watson, 2017). One primary motivator behind these programs is a reduction of employee stress through health promotion.


Health Promotion in the Workplace

According to the 2015 Bureau of Labor and Statistics report, Americans spent 8.8 hours a day at work or doing work-related activities (U.S. Department of Labor, 2016). Therefore, it can be estimated that Americans spend much of their lives in workplace settings, which can lead to occupation-related stress. In 2012, the American Psychological Association’s (APA) Stress in America Survey revealed that 65% of Americans reported work as a top source of stress (APA, 2016). Stress can affect a person’s emotional state, and it also can weaken the body’s ability to regulate itself after a stressful experience, which can eventually cause detrimental health consequences (Galla, O’Reilly, Kitil, Smalley, & Black, 2015). For example, the effects of chronic stress have been shown to lead to obesity and metabolic diseases (Razzoli & Bartolomucci, 2016). As a result, many individuals have resorted to maladaptive ways of coping with stress, highlighting the need for bringing stress management skills to the workplace (Galla et al., 2015). In addition, the World Health Organization has stated that health promotion in the workplace (promoting aspects of physical and emotional wellness) is beneficial in combating work-related stress (Jarman, Martin, Venn, Otahal, & Sanderson, 2015).

Finding ways to help employees manage their stress through health promotion in the workplace is typically conducted through workplace wellness programs, which include both lifestyle and disease management programs (Caloyeras, Hangsheng, Exum, Broderick, & Mattke, 2014; Kaspin, Gorman, & Miller, 2013; Mattke et al., 2013). Promoting positive health habits among employees maintains affordable health coverage and increases worker productivity (Anderko et al., 2012; Parkinson, Peele, Keyser, Liu, & Doyle, 2014; Shapiro & Moseley, 2013). Most workplace wellness programs focus on disease management, treating chronic illnesses such as diabetes and hypertension. Disease management programs also typically utilize health care professionals, such as nurses, to conduct face-to-face meetings or telephone consultations (Caloyeras et al., 2014). Conversely, lifestyle management programs prevent chronic illnesses by: (a) reducing stress; (b) lowering weight; (c) encouraging exercise; (d) promoting smoking cessation; and (e) fostering overall well-being (Caloyeras et al., 2014; Kaspin et al., 2013; Mattke et al., 2013).


Wellness Programs

Johnson & Johnson was an early pioneer in the creation and promotion of workplace wellness programs. In the 1970s, the company implemented a wellness program for employees called Live for Life (Ozminkowski et al., 2002). In 1993, this program was modified to integrate the following additional services: (a) employee health; (b) occupational medicine; (c) health promotion; (d) disability management; and (e) an employee assistance program. A modified program was rebranded with a new title: The Johnson & Johnson Health & Wellness Program (Ozminkowski et al., 2002). At the time of the program analysis, Johnson & Johnson employed approximately 40,000 people in the United States, 90% of whom participated in their wellness program. The program was evaluated by comparing outpatient doctor visits, hospital inpatient stays and mental health visits over the course of four years as compared to three years prior to the start of the wellness program. The worksite wellness program resulted in significant annual savings per employee/per year. On average, the study reported $45.17 savings for each outpatient visit, $119.67 per inpatient stays and $70.69 for mental health visits. In sum, Johnson & Johnson reported over $8 million in annual savings (Kaspin et al., 2013; Ozminkowski et al., 2002), creating a model wellness program that has been replicated in other organizations to varying degrees.

In contrast, PepsiCo offered a program in 2004 that did not produce similar results. Over 55,000 employees participated in a 3-year study, and it was determined that while costs were high in the initial year, it was the disease management portion of the program that lowered overall medical expenses by the third year (Liu et al., 2013). The disease management program was six to nine months in length and involved regular phone calls with a nurse for 15 to 25 minutes (Caloyeras et al., 2014). The program focused primarily on conditions such as asthma, coronary artery disease, congestive heart failure, hypertension and strokes (Caloyeras et al., 2014). Conversely, the lifestyle management portion of the program, which focused on weight management, nutrition management, fitness, stress management and smoking cessation, was described simply as involving a “series of telephonic calls with a wellness coach over a six-month period” (Caloyeras et al., 2014, p. 125). Training to become a wellness coach varies widely, ranging from a few days to 6 months. Training typically requires an associate degree and 18 weeks of classes conducted over the telephone or four full days of training in topics that include: (a) growth-promoting relationships; (b) expressing compassion; and (c) eliciting motivation to overcome ambivalence (Wellcoaches, 2016). The lack of sustainable changes in lifestyle wellness programs may be due to the variation and brevity of training for wellness coaches.

Hospitals have started employee wellness programs to lower employee health insurance costs, support mental health, and recruit and retain quality employees (Caloyeras et al., 2014; Hochart & Lang, 2011; Liu et al., 2013; Parkinson et al., 2014). Ironically, while the health care system is designed to help patients achieve good health, it often comes at the price of high stress levels and poor health for the employees (Chang, Hancock, Johnson, Daly, & Jackson, 2005; McClafferty & Brown, 2014; Smith, 2014). In fact, hospital employees tend to exhibit poorer health than other types of employees, which results in hospitals having the highest health care costs among employment sectors in the United States (Parkinson et al., 2014). As a result, some hospitals, such as the University of Pittsburgh Medical Center, are introducing the idea of employee wellness programs. In 2005, the University of Pittsburgh Medical Center utilized a prepackaged wellness program called MyHealth—a program that included both lifestyle and disease management components (Parkinson et al., 2014). Based on the number of requirements an employee met and activities he or she engaged in, the program provided credit that could be used to lower insurance deductibles (Parkinson et al., 2014). MyHealth consisted of online education materials, self-help tools, telephonic health coaching and support groups for lifestyle issues such as smoking cessation, depression, and emotional health and stress issues (Parkinson et al., 2014). Over a 5-year period, overall health care costs were lowered, but again, savings were attributed to the disease management portion of the program and not the lifestyle management portion (Caloyeras et al., 2014). Although there has been moderate success with wellness programs, the inclusion of counselors could make these programs more successful.


Need for Counselors in Wellness Programs

Changes in health care and increases in worksite wellness programs have created footholds for trained mental health professionals. As evidenced in the cases above, health care professionals, rather than mental health professionals, are facilitating lifestyle wellness programs. This is unfortunate, as professional counselors are trained in the skills of rapport building, demonstrating empathy and helping others achieve their goals. To build upon counselors’ inherent training and strengths may reduce the need for additional support and behavior change training. Utilizing counselors may result in stronger program implementation and cost savings for companies (Groeneveld, Proper, Absalah, van der Beek, and van Mechelen, 2011). Furthermore, although there have been some promising results and modest savings due to wellness programs, the variability in the content of wellness programs ranges widely. Therefore, it is proposed that having a program designed and led by counselors may have the potential to create larger savings for the lifestyle management portion of worksite wellness programs. With counselors utilizing their skills and coupling these techniques with aspects of motivational interviewing (MI) and the transtheoretical model of change (TTM), they could strengthen the lifestyle management portion of wellness programs and build on the foundation of wellness in counseling. To this end, we propose a psychoeducational lifestyle management conceptual framework that combines both MI and the TTM in an 8-week program, entitled Steps to Better Health (S2BH), which is described in the following section.


Components of S2BH

MI is an approach that helps individuals motivate themselves to pursue the changes that they seek. The founders of MI, Miller and Rollnick (2013), defined MI as “a collaborative conversation style for strengthening a person’s own motivation and commitment to change” (p. 12). More precisely, MI is about skillfully arranging conversations so that people talk themselves into changing (Miller & Rollnick, 2013). Further, MI has been positively correlated with stress reduction, medication adherence, diet change and exercise participation (Rollnick, Miller, & Butler, 2008). Miller and Rollnick (2013) asserted that people from all backgrounds could be trained to use the tools of MI; however, they emphasize that MI is not simply a collection of techniques (Miller & Rollnick, 2013). Rather, MI should be applied in a context that is characterized by client-counselor collaboration, client independence, and empowering clients to find and use their own resources for change (Young, Gutierrez, & Hagedorn, 2012). In addition to MI, the proposed wellness program integrates the TTM, an evidence-based model for change, and research on effective group work.

The TTM was developed by Prochaska and DiClemente (1982) to facilitate behavioral changes for individuals (Campbell, Eichhorn, Early, Caraccioli, & Greeley, 2012). The TTM consists of five stages of change individuals experience when changing behavior. The five stages are: (a) pre-contemplative (not thinking about change); (b) contemplative (thinking about change); (c) preparation (taking steps to begin change); (d) action (making the change); and (e) maintenance (creating a habit of new change; Shinitzky & Kub, 2001).

Prochaska et al. (2008) reviewed employee health promotion interventions, and results demonstrated that both MI and the TTM individually can lead to effective change. Participants (N = 1400) at a major medical university were assigned to three treatment groups: brief health risk intervention (BHRI) only (n = 433), online TTM-tailored treatment (n = 504), and an MI treatment group (n = 433; Prochaska et al., 2008). The results of the study showed that both the MI and TTM treatment groups had more individuals participating in the action stage for exercise and indicated better management of stress along with less health risk behaviors in 6 months than the BHRI only group (Prochaska et al., 2008). This study suggests that if both MI and TTM are effective separately, then combining them could lead to further success. Additionally, utilizing this combination within the framework of a psychoeducational group for a workplace would create efficiency.

Psychoeducational group work is ideal for a wellness program as it is a “hybrid of an academic course and counseling session” (Brown, 2011, p. 8). This format allows participants to feel as though they are attending a class, which can help them focus on learning and implementing a specific task without the potential stigma of therapy. For working professionals who may not feel the need to participate in traditional counseling, a psychoeducational group provides opportunity for discussions and activities in which individuals can practice various wellness techniques in a safe setting. Additionally, groups can be more cost-effective for businesses and organizations, as a number of individuals can simultaneously accomplish goals in a shared timeframe.

For many wellness programs, the results have been mixed due to expensive training and inadequate application of behavior change principles. For the lifestyle management portion of these wellness programs to be successful, a stronger framework would need to be implemented along with the use of professionally trained counselors. Therefore, a conceptual framework that counselors can consider adapting for a wellness lifestyle management program is proposed. The intention is to emphasize critical theoretical components while integrating practical ideas for counselors to build upon and adapt into their own lifestyle and health management programs.



     The proposed intervention of S2BH is an 8-week pyschoeducational group that incorporates aspects of both MI and the TTM. Each session consists of a short lesson about a concept related to change followed by a discussion that progressively moves each participant toward making the decision to change and successfully enacting those changes. Devoting 1 hour per week over the span of 8 weeks would yield overall balance and wellness among employees, leading to higher work performance and lower absenteeism (Vitality Institute, 2014). In addition to group sessions, the counselor should be available for optional one-on-one follow-up sessions, up to two times after the initial 8 weeks, ideally at the employer’s expense. These sessions would provide the opportunity for employees to address specific wellness concerns to help maintain changes. For demonstration purposes, below is a brief case example that demonstrates how S2BH could be utilized. In addition, Table 1 contains an overview of the program.


Case Illustration

Polly, a 46-year-old oncology nurse for 20 years, and Amelia, a 35-year-old oncology nurse for 9 years, work at Metro Hospital, a 2,000-bed acute care medical facility located in a busy downtown area. Both Polly and Amelia were frustrated about their workloads and felt burned out because of job stressors. They were both interested in joining the S2BH group, as it would give them more points in Metro’s HealthyYou! Campaign. These additional points could later be translated into monetary bonuses to encourage employee participation. After gaining permission from their nurse manager to be part of the S2BH group, both women joined seven other nurses from different floors once a week for an hour during their lunch break. Both Polly and Amelia completed physicals as a part of the campaign, and despite weight and blood pressure issues, neither of the physicals for both women showed severe health concerns.

During their first meeting, Polly shared feeling fatigued and believing that her lack of exercise played a part in that. Amelia stated that though she managed to walk once a week, she still felt lethargic both emotionally and physically, but was not sure why. During this first group, the counselor utilized one of the central principles of MI, which reflects listening skills to express empathy and genuine caring for the nurses. To close the group, everyone received the S2BH Wellness Primer Worksheet as homework.


Table 1

Suggested Curriculum for Steps to Better Health


Weekly Session

Session Details

Activities in Session

Homework Assigned

Week 1: Rapport Building and Therapeutic Alliance

Counselor will welcome the group and explain the weekly format, with emphasis on goal attainment. Participants will be encouraged to share work-related stressors and wellness goals. A worksheet will be provided for participants to outline wellness goals, steps needed to achieve goals and identification of stressors.

Week 2:

Wellness Education

Participants will explore reasons for change and discuss the homework from the previous session. Participants will discuss potential pitfalls and necessary supports for successful change. Participants will identify what problems they encountered with their last change attempts.

Week 3:

The Stages of Change

Counselor will give lesson on TTM, focusing on the stages of change. Participants will identify which stage of change they are in and work to develop stage-matched interventions. Participants will write down the advantages and disadvantages of achieving their wellness goal(s).

Week 4:

Exploring Ambivalence

Counselor will lead a discussion on ambivalence (Miller & Rollnick, 2013; Shinitzky & Kub, 2001). Participants will discuss benefits and costs of not changing behavior. Each participant will identify one to two new habits as they move toward their wellness goal(s).

Week 5:

Habit Formation

Counselor will discuss how participants can create new habits. Using homework, members will identify cues/routines/rewards for each new habit identified (Duhigg, 2012). Each participant will bring to the next session a brief update on their wellness goal(s).

Week 6:

Reframing & Risk Assessments

Participants will discuss triggers and potential tactics to adhere to personal goals. Participants will identify and isolate potential triggers and solutions for the individual. Participants will identify stressors from work and life that could jeopardize wellness goal(s).

Week 7:

Stress Busters

Participants will discuss stress and ways to enhance coping skills (e.g., emotion-based and action-based). Participants will use homework to identify appropriate coping skills for each stressor. Participants will use one of the identified coping skills over the next week.

Week 8:


Participants will discuss how to stay motivated and engaged with wellness plans. Participants will discuss achievements followed by a termination activity. No homework assigned.


Polly and Amelia came back to the second group with their S2BH Wellness Primer Worksheet results and were a little hesitant to begin discussing their results. After a few other members shared, Polly stated that the wellness primer made her more aware of her lack of exercise. Amelia then shared that this was the first time she had sat down and reflected on her health and well-being, and though she was not sure it was necessarily helpful, she was willing to try anything to stop feeling “blah.” Following the discussion on the wellness primer, group members worked on developing a wellness plan for the areas they wanted to improve. To close the session, the counselor discussed with the members ways to begin working on their goals in incremental steps and noted different ways they had started addressing those steps.

After learning about the stages of change from the TTM in the third session, Polly was animated about which stage she was on in relation to her goal of exercising more. She shared that she had been stuck on the contemplative stage of change for more years than she could count. She stated that she wanted to lose weight but could not seem to motivate herself to walk before her shift started.

Amelia stated that she wanted to eat better and classified herself as being in the pre-contemplative stage of change. She reported that she needed to eat better because she relied too often on caffeine and sugary foods to keep her going throughout the work day. Several of the group members expressed hope in knowing that they were not just “being lazy,” but were in a process of change. Amelia stated that just knowing that gave her a boost of energy.

After checking in during the fourth session and finding out where everyone was with their goals, the counselor led a discussion on the MI concept of ambivalence. Polly found this a little challenging, as she just wanted to list the pros and cons of her new health goals: exercising and eating better. Once she understood that she was to list both the benefits and costs of continuing her current behavior versus enacting her new health goal, she became more involved in the activity. As a result, Polly listed some pros of walking in the morning as being “it centers me as I release some of the frustration from the day before,” and “I use this time to organize my mind for the upcoming tasks for the day.” Amelia stated that some of her cons for not changing her behavior included “crashing hard around 4 p.m. in the afternoon” and “losing focus when working with patients.”

For the fifth session, a discussion centered around Duhigg’s (2012) book, The Power of Habit: Why We Do What We Do in Life and Business, and how members could apply the principle of cue, reward and routine to help them achieve their goals. Polly stated that she started putting her walking shoes out with her exercise clothes so that she could immediately see them when she woke up (cue). She would play her favorite podcast while walking (routine), and reward herself with a small low-calorie pastry for breakfast (reward). Amelia stated that she started to place almonds and other energy-boosting snacks at the nurses’ station so she could easily see them (cue), then would snack on those items while talking with colleagues (routine). As a result, she felt her energy lasting longer throughout the day (reward).

The nurses enjoyed reframing their previous “relapses” in the sixth session. Amelia reported that she was aware it was normal to move back and forth between the stages and that this knowledge alleviated concerns about failure. The group had a lively discussion about what triggers or pitfalls stood in their way and what places or things they should avoid as a result. For example, Polly stated that if she hit the “snooze button,” she would stay in bed and forgo her walk. Realizing this, she opted to place her alarm clock across the room so that she would have to get out of bed to turn off the alarm.

The seventh session on stressors became more emotional than anticipated as many of the nurses talked about their work and the unique stress they experience when taking care of ill and terminally ill patients. The group members talked about their thoughts and feelings and supported one another during this session. As a result, a spontaneous sharing of how nurses deal with the grief of losing patients occurred. Amelia shared that she had recently decided to join Team in Training for the Leukemia and Lymphoma Society and train for a half marathon in memory of one of her younger patients. She stated that letting the family know and beginning to raise money for research in this area was helping her to positively channel her grief. As a result of this discussion, several of the nurses stated that they left the group with hope, connectivity, and ideas for channeling their grief and stress.

The final session of the group focused on closure. Amelia shared that although she was initially dubious about the group, as a result of her sharing and the small changes she was making with her snacking, she was not feeling as “blah” anymore. Polly also shared that while she had not lost weight yet, she felt more motivated to continue walking and noticed that she felt more positive about walking.



Changes in health care have increased job opportunities in health care for counselors. The PPACA allows counselors the opportunity to expand their background of wellness while capitalizing on preventive health care initiatives (Barden, Conley, & Young, 2015; Granello & Witmer, 2013). With the interrelatedness between physical and mental health, counselors are ideally positioned to help clients achieve their wellness goals. Connections between physical activity and psychological well-being are well established, as are the potential benefits of improved coping with stress and adversity (Focht & Lewis, 2013). Because chronic stress has been shown to contribute to obesity and metabolic diseases (Razzoli & Bartolomucci, 2016), helping employees improve their coping skills can lead to adaptive ways of dealing with stress, which ultimately impacts chronic health conditions. To better manage occupational stress, counselors can fill the need for bringing stress management skills to the workplace (Galla et al., 2015).

In addition, wellness programs provide the ability for counselors to research their contributions to workplace wellness programs, thereby providing an opportunity to study counselor effectiveness. Research has shown that using health care professionals in disease management portions of wellness programs can lower costs. The focus of this manuscript has been to describe a framework for counselors to facilitate lifestyle management programs in corporate settings. Considerable sponsored research opportunities also are available, especially for worksite wellness programs targeted to underserved populations (U.S. Department of Health and Human Services Office of Minority Health, 2016).



Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.



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Yvette Saliba, NCC, is a doctoral student at the University of Central Florida. Sejal Barden, NCC, is an Associate Professor at the University of Central Florida. Correspondence can be addressed to Yvette Saliba, 851 South State Road 434, Suite #1070-170, Altamonte Springs, FL 32714, ysaliba@knights.ucf.edu.

Burnout, Stress and Direct Student Services Among School Counselors

Patrick R. Mullen, Daniel Gutierrez

The burnout and stress experienced by school counselors is likely to have a negative influence on the services they provide to students, but there is little research exploring the relationship among these variables. Therefore, we report findings from our study that examined the relationship between practicing school counselors’ (N = 926) reported levels of burnout, perceived stress and their facilitation of direct student services. The findings indicated that school counselor participants’ burnout had a negative contribution to the direct student services they facilitated. In addition, school counselors’ perceived stress demonstrated a statistically significant correlation with burnout but did not contribute to their facilitation of direct student services. We believe these findings bring attention to school counselors’ need to assess and manage their stress and burnout that if left unchecked may lead to fewer services for students. We recommend that future research further explore the relationship between stress, burnout and programmatic service delivery to support and expand upon the findings in this investigation.


Keywords: burnout, stress, school counselors, student services, service delivery


The American School Counselor Association (ASCA; 2012) recommends that school counselors enhance the personal, social, academic and career development of all students through the organization and facilitation of comprehensive programmatic counseling services. Delivery of student services is part of a larger framework articulated by ASCA’s National Model (2012) that also includes management, accountability and foundation components of school counseling programs. However, ASCA notes that school counselors should “spend 80 percent or more of their time in direct and indirect services to students” (ASCA, 2012, p. xii). ASCA defines indirect student services as services that are in support of students and involve interactions (e.g., referrals, consultations, collaborations and leadership) with stakeholders other than the student (e.g., parents, teachers and community members). On the other hand, direct student services are interactions that occur face-to-face and involve the facilitation of curriculum (e.g., classroom guidance lessons), individual student planning and responsive services (e.g., individual, group and crisis counseling). In either case, ASCA charges school counselors with prioritizing the delivery of student services.


As a part of their work, school counselors often incur high levels of stress that may result from multiple job responsibilities, role ambiguity, high caseloads, limited resources for coping and limited clinical supervision (DeMato & Curcio, 2004; Lambie, 2007; McCarthy, Kerne, Calfa, Lambert, & Guzmán, 2010). In addition, burnout can result from the ongoing experience of stress (Cordes & Dougherty, 1993; Maslach, 2003; Schaufeli & Enzmann, 1998) and can result in diminished or lower quality rendered services (Lawson & Venart, 2005; Maslach, 2003). While research on burnout is common in the school counseling literature (Butler & Constantine, 2005; Lambie, 2007; Wachter, Clemens, & Lewis, 2008; Wilkerson & Bellini, 2006), studies have not focused on the relationship between burnout and school counselors’ service delivery. Yet, burnout has the potential to produce negative consequences for the work rendered by school counselors and could result in fewer services for students (Lambie, 2007; Lawson & Venart, 2005; Maslach, 2003). Therefore, the purpose of this research was to examine the contribution of school counselors’ levels of burnout and stress to their delivery of direct student services.


School Counselors and the Delivery of Student Services


Research on school counselors’ delivery of student services has produced positive findings. In a meta-analysis that included 117 experimental studies, Whiston, Tai, Rahardja, and Eder (2011) identified that, in general, school counseling services have a positive influence on students’ problem-solving and school behavior. Furthermore, in schools where school counselors completed higher levels of student services focused on improving academic success, personal and social development, and career and college readiness, students experienced a variety of positive outcomes, such as increased sense of belongingness, increased attendance, fewer hassles with other students, and less bullying (Dimmitt & Wilkerson, 2012). Moreover, researchers have shown that the higher occurrence of school counselor-facilitated services is beneficial for students’ educational experience and academic outcomes (Carey & Dimmitt, 2012; Lapan, Gysbers, & Petroski, 2001; Wilkerson, Pérusse, & Hughes, 2013). Overall, the services conducted by school counselors have a positive impact on student success. As such, research investigating the factors related to higher incidence of school counselors’ direct student services could provide significant educational benefits to schools.


Researchers have examined a variety of topics that relate to increased student services. Clemens, Milsom, and Cashwell (2009) found that if school counselors had a good relationship with their principal and were engaged in higher levels of advocacy, they were likely to have increased implementation of programmatic counseling services. Another study concluded that school counselors’ values were not associated with the occurrence of service delivery, but researchers did find counselors with higher levels of leadership practices also delivered more school counseling services (Shillingford & Lambie, 2010). Other factors related to increased levels of school counselors’ service delivery are increased job satisfaction (Baggerly & Osborn, 2006; Pyne, 2011) and higher self-efficacy (Ernst, 2012; Mullen & Lambie, 2016). These studies provided notable contributions to the literature; however, at this time no known studies have examined the relationship among school counselors’ burnout, perceived stress and direct student services.


Stress and Burnout Among School Counselors


Stress is a significant issue that relates to the impairment of work performance (Salas, Driskell, & Hughes, 1996) and is a likely problem for school counselors. The construct of stress has a rich history in scientific literature dating back to the 1930s (Cannon, 1935; Selye, 1936). Selye (1980) articulated one of the first broad definitions of stress by defining it as the “nonspecific results of any demand upon the body” (p. vii). Over time, various authors developed an assortment of definitions (Ivancevich & Matteson, 1980; Janis & Mann, 1977; McGrath, 1976), but Lazarus and Folkman’s (1984) definition of stress is common among scholars (Driskell & Salas, 1996; Lazarus, 2006). In their Transactional Model of Stress and Coping, Lazarus and Folkman (1984) defined stress as a “particular relationship between the person and the environment that is appraised by the person as taxing or exceeding his or her resources and endangering his or her wellbeing” (p. 19). Lazarus and Folkman conceptualized that stress results from an imbalance between one’s perception of demands or threats and their ability to cope with the perceived demands or threats. Consequently, one’s appraisal of demands and their assessment of their coping ability becomes a critical issue in relationship to whether or not the demand will trigger a stress response.


McCarthy et al. (2010) applied Lazarus and Folkman’s model of stress (1984) to school counselors using an instrument that measures the demands and resources experienced by school counselors called the Classroom Appraisal of Resources and Demands–School Counselor Version (McCarthy & Lambert, 2008). McCarthy et al. (2010) found that school counselors who reported challenging demands as a part of their job also had higher levels of stress. This finding is troubling considering that school counselors oftentimes encounter ambiguous job duties, inconsistent job roles and conflicts in their job expectations (Burnham & Jackson, 2000; Culbreth, Scarborough, Banks-Johnson, & Solomon, 2005; Lambie, 2007; Scarborough & Culbreth, 2008). An additional concern is that stress occurring over an extended period of time can lead to emotional and physical health problems (Sapolsky, 2004) along with increased likelihood of leaving the profession (DeMato & Curcio, 2004). Fortunately, prior research reveals that school counselors have reported low stress levels (McCarthy et al., 2010; Rayle, 2006). Still, research on school counselors’ stress and its effects on the services they provide is important.


An additional factor that we believe may have an impact on direct student services is burnout. Burnout was first recognized in the 1970s (Freudenberger, 1974; Maslach, 1976) and is considered to have significant consequences for counseling professionals (Butler & Constantine, 2005; Lambie, 2007; Lawson, 2007; Lee et al., 2007). The topic of burnout is common in the literature across many disciplines (Schaufeli, Leiter, & Maslach, 2009) and has been given particular attention in school counseling research (Butler & Constantine, 2005; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006). Freudenberger (1974, 1986) suggested that burnout results from depleted energy and the feelings of being overwhelmed that emerge from the exposure to diverse issues related to helping others, which over time affects one’s attitude, perception and judgment. Pines and Maslach (1978) described burnout as an ailment “of physical and emotional exhaustion, involving the development of negative self-concept, negative job attitude, and loss of concern and feelings for clients” (p. 234). In 1981, the Maslach Burnout Inventory (MBI) was developed as a method to measure one’s experience of burnout in the helping and human service field (Maslach & Jackson, 1981).


More recently, Lee et al. (2007) expanded the measurement of burnout and presented the construct of counselor burnout, which they defined as “the failure to perform clinical tasks appropriately because of personal discouragement, apathy to symptom stress, and emotional/physical harm” (p. 143). Within their model, Lee and associates found that counselor burnout includes the constructs of exhaustion, negative work environment, devaluing clients, incompetence and deterioration in personal life. These constructs correlate with the factors measured by the MBI (Maslach & Jackson, 1981), but provide a definition consistent with the work of school counselors (Gnilka, Karpinski, & Smith, 2015).


Many researchers have explored factors related to school counselor burnout. Overall, scholars have found that school counselors report low levels of burnout (Butler & Constantine, 2005; Gnilka et al., 2015; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006). Nonetheless, researchers also reported that higher collective self-esteem is associated with a higher sense of personal accomplishment and lower emotional exhaustion (Butler & Constantine, 2005), whereas higher levels of ego development are associated with higher personal accomplishment (Lambie, 2007). Moreover, Wilkerson and Bellini (2006) discovered that school counselors who handle stressors with emotion-focused coping are at a higher risk of experiencing burnout symptoms, and Wilkerson (2009) established that school counselors’ emotion-focused coping increases their likelihood of experiencing symptoms of burnout. Yet, there is no research on the connection between school counselors’ burnout and the direct student services they provide despite a high likelihood that burnout is the cause of fewer and deteriorated services for students (Maslach, 2003).


The purpose of this study was to build upon existing literature regarding school counselors’ stress, burnout and their facilitation of direct student services. The guiding research questions were: (a) Do practicing school counselors’ levels of burnout and perceived stress contribute to their levels of service delivery? and (b) Do practicing school counselors’ levels of stress correlate with their burnout? Consequently, the following research hypotheses were examined: (a) School counselors’ degree of burnout and perceived stress contributes to their facilitation of direct student services, and (b) School counselors’ degree of perceived stress correlates positively with their level of burnout.





To answer the research questions associated with this study, we employed a cross-sectional research design (Gall, Gall, & Borg, 2007). Furthermore, this study utilized online survey data collection procedures. Prior to any data collection, we received approval from the Institutional Review Board at the first author’s university. During the first step in the data collection process, we retrieved the name and e-mail address of every school counselor listed in the ASCA online directory of membership. Next, we generated a simple random sample of school counselors. Then, we sent the sample selected from the ASCA online directory a series of three e-mails that aligned with tailored design method (Dillman, Smyth, & Christian, 2009) recommendations for survey research. Each e-mail contained a brief description of the survey and a link to the online survey managed by Qualtrics (2013). If a participant wished to take the survey, he or she was directed to the Web site that posted the explanation of the study. If they agreed to participate, they would move forward and complete the survey. Participants were screened as to whether they were practicing school counselors or not (e.g., student, counselor educator or retired). Of the 6,500 participants sampled, 41 indicated they were not a practicing school counselor. In addition, 312 e-mails were not working at the time of the survey. Out of the 6,147 practicing school counselors surveyed, 1,304 (21.21% visit response rate) visited the survey Web site and 926 completed the survey in its entirety, which resulted in a 15.06% useable response rate. The response rate received for this study is high in comparison to studies using similar methods (e.g., 14%, Harris, 2013; 11.4%, Mullen, Lambie & Conley, 2014).


Participant Characteristics

     Participants (N = 926) were practicing school counselors in private, public and charter K–12 educational settings from across the United States. The mean age was 43.27 (SD = 10.03) and included 816 (88.1%) female and 110 (11.9%) male respondents. The participants’ ethnicity included 50 (5.4%) African Americans, 5 (.5%) Asian Americans, 29 (3.1%) Hispanic Americans, 11 (1.2%) Multiracial, 2 (.2%) Native Americans, 4 (.4%) Pacific Islanders, 811 (87.6%) European Americans, and 13 (1.5%) participants who identified their ethnicity as “Other.” On average, participants had 10.97 (SD = 6.92) years of experience and 401.45 (SD = 262.05) students on their caseload. The geographical location of the participants’ work setting favored suburban (n = 434, 46.9%) and rural communities (n = 321, 34.7%) with fewer school counselors working in urban settings (n = 171, 18.5%). Most participants reported that they worked in the high school grade levels (n = 317, 34.2%) closely followed by elementary (n = 270, 29.2%) and middle school or junior high school (n = 203, 21.9%) grade levels, with 136 (14.7%) respondents working in another grade level format (e.g., grades K–12, K–8, or 6–12).



This study used the (a) Counselor Burnout Inventory (CBI; Lee et al., 2007), (b) the School Counselor Activity Rating Scale (SCARS; Scarborough, 2005), and (c) the Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983). Participants also completed a researcher-created demographics form regarding their personal characteristics (e.g., age, gender and ethnicity) and work-related characteristics (e.g., location type, grade level, caseload, experience as a school counselor and percentage of time they directly work with students).


CBI. The CBI (Lee et al., 2007) is a 20-item self-report measure that examines counselor burnout across five domains. The domains that make up the CBI include: (a) exhaustion, (b) incompetence, (c) negative work environment, (d) devaluing client, and (e) deterioration in personal life. The CBI makes use of a 5-point Likert rating scale that ranges from 1 (never true) to 5 (always true) and examines emotional states and behaviors representative of burnout. Some sample items include “I feel exhausted due to my work as a counselor” (exhaustion), “I feel I am an incompetent counselor” (incompetence), “I feel negative energy from my supervisor” (negative work environment), “I have little empathy for my clients” (devaluing client), and “I feel I have poor boundaries between work and my personal life” (deterioration in personal life). Lee et al. (2007) demonstrated the construct validity of the CBI through an exploratory factor analysis that identified a five-factor solution in addition to a confirmatory factor analysis that supported the five-factor model with an adequate fit to the data.


Gnilka et al. (2015) found support for the five-factor structure of the CBI (Lee et al., 2007) with school counseling using confirmatory factor analysis, which supports the CBI as an appropriate measure for school counselor burnout. Lee et al. (2007) established convergent validity for the CBI based upon the correlations between the subscales on the Maslach Burnout Inventory-Human Services Survey (Maslach & Jackson, 198l) and the CBI. In prior research, the Cronbach’s alphas of the CBI subscales indicated good internal consistency (Streiner, 2003) with score ranges of .80 to .86 for exhaustion, .73 to .81 for incompetence, .83 to .85 for negative work environment, .61 to .83 for devaluing client, and .67 to .84 for deterioration in personal life (Lee et al., 2007; Lee, Cho, Kissinger, & Ogle, 2010; Puig et al., 2012). The internal consistency coefficients of the CBI in this investigation also were good (Streiner, 2003) with Cronbach’s alphas of .87 for exhaustion, .79 for incompetence, .84 for negative work environment, .79 for devaluing client, and .81 for deterioration in personal life.


SCARS. The SCARS (Scarborough, 2005) is a 48-item verbal frequency measure that examines the occurrence that school counselors actually perform and prefer to perform components of the ASCA National Model (2012). The SCARS measures school counselors’ ratings of activities based on the four levels of interventions articulated by ASCA (1999) and the ASCA National Model (2003). Unfortunately, a more recent version of the SCARS that articulates the new ASCA National Model (2012) does not exist. Nevertheless, this study utilized two SCARS scales (counseling and curriculum) that measure the incidence of direct student services. To the benefit of this investigation, the direct services measured on the SCARS have not changed in the new edition of the ASCA National Model (2003, 2012). Similar to Shillingford and Lambie (2010) and Mullen and Lambie (2016), this investigation utilized the actual scale, but not the prefer scale, on the SCARS (Scarborough, 2005) because this study sought to examine the frequency that school counselors delivered direct student services, not their preferences and not the difference between their preference and actuality. The subscales that measure direct student services used in this study included the counseling (e.g., group and individual counseling interventions; 10 items) and curriculum (e.g., classroom guidance interventions; 8 items) subscales, whereas the coordination, consultation and other activities scales were not used because they measure indirect activities.


The SCARS (Scarborough, 2005) assesses the frequency of school counselor service delivery with a 5-point Likert rating scale that ranges from 1 (I never do this) to 5 (I routinely do this). Scores on the SCARS can be total scores or mean scores. Some sample items from the counseling subscale are “Counsel with students regarding school behavior” and “Provide small group counseling for academic issues.” Some sample items from the curriculum subscale are “Conduct classroom lessons addressing career development and the world of work” and “Conduct classroom lessons on conflict resolution.” Scarborough (2005) examined the validity by investigating the variances in score on the actual scale based on participant grade level and found that participants’ grade level had a statistically significant effect across the scales with small to large effect sizes (e.g., ranging from .11 to .68[ω2]), which supported the convergent validity of the SCARS. Additionally, construct validity was supported using factor analysis. In prior research using the SCARS, the internal consistency of the counseling and curriculum scales was strong with Cronbach’s alphas of .93 for the curriculum actual scale and .85 for the counseling actual scale (Scarborough, 2005). The internal consistency coefficients of the SCARS actual subscales in this investigation were good (Streiner, 2003) with Cronbach’s alphas of .77 for the counseling scale and .93 for the curriculum scale.


PSS. The PSS (Cohen et al., 1983) is a 10-item self-report measure that examines the participants’ appraisal of stress by asking about feelings and thoughts during the past month. The PSS uses a 5-point Likert scale that ranges from 0 (never) to 4 (very often) and includes four positively stated items that are reverse coded. Some sample items include, “In the last month, how often have you felt that you were on top of things?” (reverse coded), and “In the last month, how often have you been upset because of something that happened unexpectedly?” The PSS has been shown to have acceptable internal consistency with Cronbach’s alphas ranging from .84 to .91 (Chao, 2011; Cohen et al., 1983; Daire, Dominguez, Carlson, & Case-Pease, 2014). The internal consistency coefficient of the PSS in this study also was acceptable (Streiner, 2003) with a Cronbach’s alpha of .88.




Preliminary Analysis

Initial screening of the data included the search for outliers (e.g., data points three or more standard deviations from the mean) using converted z-scores (Osborne, 2012), which resulted in identifying 21 cases that had at least one variable with an extreme outlier. To accommodate for these outliers, the researchers utilized a Windorized mean based on adjacent data points (Barnett & Lewis, 1994; Osborne & Overbay, 2004). Next, the assumptions associated with structural equation modeling (SEM) were tested (e.g., normality and multicollinearity; Hair, Black, Babin, Anderson, & Tatham, 2006; Tabachnick & Fidell, 2007). Multicollinearity was not present with these data; however, the data violated the assumption of normality of a single composite variable (e.g., devaluing clients scale on the CBI). Researchers conducted descriptive analyses of the data using the statistical software SPSS. Table 1 presents the means, standard deviations and correlations for the study variables.


Model Testing

This correlational investigation utilized a two-step SEM method (Kline, 2011) to examine the research hypothesis employing AMOS (version 20) software. The first step included a confirmatory factor analysis (CFA) to inspect the measurement model of burnout and its fit with the data. Then, a structural model was developed based on the measurement model. The measurement model and structural model were appraised using model fit indices, standardized residual covariances, standardized factorial loadings and standardized regression estimates (Byrne, 2010; Kline, 2011). Modifications to the models were made as needed (Kline, 2011). Both the measurement and the structural models employed the use of maximum likelihood estimation technique despite the presence of non-normality based on recommendations from the literature (Curran, West, & Finch, 1996; Hu, Bentler, & Kano, 1992; Lei & Lomax 2005; Olsson, Foss, Troye, & Howell, 2000).






Table 1 Correlations among measures of direct student services, perceived stress, and burnout



















Percent of Time





Perceived Stress



















































Note. N = 926. All correlations (r) were statistically significant (p < .001). Counseling = frequency of direct counseling services, curriculum = frequency of direct curriculum services, percent of time = percent of time in direct services to students, NEW = negative work environment, DC = devaluing client, DPL = deterioration in personal life.



Multiple fit indices were examined to determine the goodness of fit for the measurement model and structural model (Hu & Bentler, 1999; Kline, 2011; Weston & Gore, 2006). The fit indices that were used include: (a) chi-square, (b) comparative fit index (CFI), (c) goodness of fit (GFI), (d) standardized root mean square residual (SRMSR), and (e) root mean square error of approximation (RMSEA). Furthermore, we consulted the normed fit index (NFI) and Tucker-Lewis index (TLI) because they are more robust to non-normal data as compared to other indices (Lei & Lomax, 2005). For a detailed description of these fit indices, readers can review the works of Hu and Bentler (1999), Kline (2011), and Weston and Gore (2006). We used these fit indices to establish a diverse view of model fit.


     Measurement model. First, we employed a CFA model to examine the latent variable representing burnout (Lee et al., 2007). The research team totaled each subscale on the CBIs to develop a composite score for each domain. The initial measurement model for burnout produced acceptable standardized factor loadings ranging from .41 (devaluing client) to .57 (incompetence), .62 (negative work environment), .77 (deterioration in personal life), and .82 (exhaustion). Furthermore, all fit indices for the measurement model indicated an adequate fitting model except chi-square, RMSEA, and TLI: χ2 (df = 5, N = 926) = 107.07, p < .001; GFI = .96; CFI = .92; RMSEA = .15; SRMR = .06; NFI = .92; TLI = .85. Therefore, we consulted the modification indices and standardized residual covariance matrix and tested a new CFA based upon these consultations.


The modifications indices indicated the need to correlate the error terms for incompetence and devaluing client. The resulting model produced a model in which all fit indices indicated an adequate fitting model: χ2 (df = 4, N = 926) = 12.03, p = .02; GFI = .99; CFI = .99; RMSEA = .05; SRMR = .02; NFI = .99; TLI = .99. Further inspection of the standardized factor loadings for the model indicated they were all acceptable except for the factor loading for devaluing client, which dropped to .36 (below .40; Stevens, 1992). While these modifications improved the overall fit of the CFA, the correlation of incompetence and devaluing client has no theoretical justification (Byrne, 2010). In addition, the correlation of the error terms for incompetence and devaluing client produced a standardized factor loading below the noted standard of .40 (Kline, 2011; Stevens, 1992). Subsequently, we removed the subscale of devaluating client given: (a) the low factor loading produced after modification of the initial model, and (b) the lack of normality in the composite score.


Next, we examined the new modified measurement model that included the removal of the subscale devaluing client. The resulting model (see Figure 1) produced a model in which all fit indices indicated a good fitting model: χ2 (df = 2, N = 926) = 8.25, p = .02; GFI = .99; CFI = .99; RMSEA = .06; SRMR = .02; NFI = .99; TLI = .98. The modified measurement model for burnout produced acceptable standardized factor loadings ranging from .53 (incompetence) to .63 (negative work environment), .77 (deterioration in personal life), and .85 (exhaustion). In review of the model fit indices and standardized factor loadings, we deemed the measurement model acceptable for use in the structural model.


     Structural model. We developed the structural model (see Figure 1) based on a review of the literature, and it was theorized in this model that school counselors’ perceived stress correlates to school counselors’ burnout and contributes to the frequency with which they provide direct student services. In addition, this model tested the hypothesized model that school counselors’ burnout contributes to their frequency of direct student services. The structural model includes the measurement model previously tested that consisted of the latent variable of burnout. School counselors’ perceived stress and burnout were defined as exogenous or independent variables. Perceived stress was a manifest variable consisting of participants’ composite scores on the PSS (Cohen et al., 1983).


Additionally, we defined the manifest variables of percentage of time at work providing direct services to students, direct curriculum activities, and direct counseling activities as the endogenous or dependent variables that measure participants’ facilitation of direct student services. The variable of percentage of time at work providing direct services to students was a single demographic item reported by participants, while direct curriculum activities and direct counseling activities were the participants’ composite scores derived from subscales on the SCARS (Scarborough, 2005). In addition, the error terms of the direct student services variables—percentage of time at work providing direct services to students, direct curriculum activities and direct counseling activities—were correlated given that they measure similar constructs.


An examination of the structural model indicated a strong goodness of fit for all fit indices except for chi-square: χ2 (df = 14, N = 926) = 108.37, p < .001; GFI = .97; CFI = .96; RMSEA = .07; SRMR = .04; NFI = .95; TLI = .91. The researchers deemed the structural model as suitable with these data despite the significant chi-square (Henson, 2006; Kline, 2011; Weston & Gore, 2006). A closer examination of the standardized regression weights identified that school counselors’ burnout scores contributed to 12% (β = -.35, p < .001) of the variance in their direct counseling activities and 5% (β = -.22, p < .001) of the variance in their direct curriculum activities. Furthermore, school counselors’ burnout scores contributed to 6% (β = -.24, p < .001) of the variance in percentage of time at work providing direct services to students. Perceived stress did not contribute to direct counseling activities (β = .11, p = .04), direct curriculum activities (β = .06, p = .31), and percentage of time at work providing direct services to students (β = .04, p = .51). In addition, perceived stress and burnout produced a statistically significant correlation (β = .75, p < .001; 56% of the variance explained).


The structural model (Figure 1) indicates that school counselors’ level of counselor burnout had a negative contribution to the frequency of their direct counseling activities, direct curriculum activities and percentage of time at work providing direct services to students. However, it should be noted that the effect sizes of these findings were small to medium (Sink & Stroh, 2006). An additional finding from this investigation was that the perceived stress correlated with burnout with a large effect size (Sink & Stroh, 2006); however, perceived stress did not have a statistically significant contribution to school counselors’ direct counseling activities, direct curriculum activities, and percentage of time at work providing direct services to students.



Figure 1. Final hypothesized structural model depicting the relationship between school counselors’ (N = 926) perceived stress, burnout, and direct student services.




This study examined the relationship between school counselors’ reported burnout, perceived stress and frequency of direct student services. The findings indicated burnout was a statistically significant contributor to the frequency of direct counseling services (β = -.35; medium effect size) and direct curriculum services (β = -.22; small to medium effect size). Furthermore, the findings identified that burnout was a significant contributor to the participants’ report of the percentage of time they spend on their job working directly with students (β = -.24; small to medium effect size). Although the results should be interpreted with some level of caution, we found that burnout also had a statistically significant relationship to frequency of direct student services with increased levels of burnout relating to lower levels of direct student services. Nonetheless, these findings are not surprising considering the literature on burnout emphasizes the important role burnout plays on the effort one places on their job, with individuals presenting with higher burnout typically having lower investment interest in their job (Garman, Corrigan, & Morris, 2002; Landrum, Knight, & Flynn, 2012; Maslach, 2003). While the findings support the literature on the role of burnout, they also bring attention to the possibility that burnout does not have a strong relationship to school counselors’ facilitation of direct counseling services as noted by the small effect size.


An interesting finding was that school counselors’ degree of perceived stress did not contribute to the direct student services variables and yet did correlate with burnout. In fact, the relationship between perceived stress and counselor burnout had a large effect size, with 56% of the variance among these variables explained by their relationship. This finding accentuates the difference between the constructs of burnout and stress because burnout had a statistically significant relationship with the direct student services variables and stress did not, despite the strength of the relationship between burnout and stress. One interpretation of this finding is that school counselors’ ability to manage and cope with stress permits them to complete their job functions, whereas burnout may be more challenging to overcome. Furthermore, scholars state that prolonged exposure to stress worsens or cultivates burnout (Cordes & Dougherty, 1993; Schaufeli & Enzmann, 1998). This finding is logical given the theory behind burnout (Lee et al., 2007; Maslach, 2003); yet, this is one of only a few studies (McCarthy et al., 2010; Wilkerson & Bellini, 2006) in the school counseling literature to examine this relationship. However, these results need further exploration. As McCarthy et al. (2010) noted, the construct of stress is multidimensional (includes appraisal of resources and demands) and the PSS (Cohen et al., 1983) is a single-dimension scale. Therefore, a scale that examines stress in a multifaceted manner may produce different results.


An additional finding worth discussion involves the measurement model of the CBI (Lee et al., 2007). Specifically, this study found that the construct of devaluing client did not fit with the data. Furthermore, participants reported low scores regarding the devaluing client scale, as indicated by the descriptive statistics. The devaluing client subscale also was the only subscale on the CBI that was not normally distributed. These results were similar to Gnilka et al.’s (2015) findings that indicated school counselors are likely to maintain high levels of empathy and positive regard for their students. These findings may indicate that the devaluing clients subscale may not reflect symptoms of burnout for school counselors. This is a promising finding as it suggests that school counselors do not develop a negative perspective of students because of the negative consequences of their job.


The descriptive statistics from this investigation also provide some noteworthy information. First, participants reported moderate to low levels of burnout across the five factors of the CBI (Lee et al., 2007), with exhaustion having the highest mean score. These results are consistent with prior research (Butler & Constantine, 2005; Lambie, 2007; Wachter et al., 2008; Wilkerson & Bellini, 2006) on burnout and indicate that, overall, school counselors report low levels of burnout. An additional finding was that school counselors reported a low level of perceived stress, which is surprising given the challenge of role ambiguity, confusion and conflict (Burnham & Jackson, 2000; Culbreth et al., 2005; Lambie, 2007; Scarborough & Culbreth, 2008). However, school counselors have reported low levels of stress in other research (e.g., McCarthy et al., 2010; Rayle, 2006). The last noteworthy finding from the descriptive statistics was the measures of direct student services. This investigation was one of the first to focus specifically on the topic of direct student services versus other aspects of school counselors’ roles. This study found that school counselors reported that, on average, they spend over half their time working directly with students. In addition, they reported high frequencies for facilitating both curriculum and counseling activities. These findings are promising and consistent with other research examining these constructs (Mullen & Lambie, 2016; Scarborough & Culbreth, 2005; Shillingford & Lambie, 2010). Overall, the results from this study provide new and novel information for the school counseling discipline.


Limitations and Implications for Future Research

Readers should interpret these findings within the context of their limitations. Some limitations from this study include: (a) associational research using correlation statistics does not establish cause and effect relationships; (b) the response rate, although high as compared to other studies with similar methods, is low; and (c) the generalizability of these findings is limited by the sampling procedures (e.g., only sampled ASCA members; Gall et al., 2007). In addition, participants who respond to surveys may have different characteristics as compared to those school counselors who chose not to participate (Gall et al., 2007).


The findings from this study have implications for future research. A prominent direction for future research is the examination of the relationship between stress and programmatic service delivery, including direct student services. This study identified that perceived stress has no relationship with direct service delivery, but a multidimensional measure of stress (McCarthy & Lambert, 2008) may produce different results. Similarly, this study found that perceived stress relates to higher levels of burnout and supports the theory that chronic stress relates to increased burnout. Future research might further confirm these findings.


Another relevant future research implication is exploring factors that prevent or mediate the contribution of burnout to school counselor service delivery, considering this investigation found a significant relationship between these constructs. A variety of mechanisms may serve as buffers between burnout and programmatic service delivery, such as coping skills, career-sustaining behaviors, emotional intelligence, grit, or self-efficacy. Nonetheless, the identification of preventative skills or personal traits that inhibit the effects of burnout may lead to interventions to support school counselors’ work. Future research also can examine training interventions that target school counselors’ susceptibility to burnout or stress. A final research implication is the need to replicate and confirm our findings. Researchers might consider replicating this study with similar or different measures and data collection methods.


Implications for School Counseling Practitioners and Supervisors

The degree of perceived stress for participants in this study had a positive correlation with their degree of burnout. Furthermore, participants’ burnout negatively contributed to their level of direct student services. While this study included several limitations, these findings provide more evidence for the positive relationship between stress and burnout, in addition to the negative contribution burnout can have on the job functions of school counselors. In an effort to support direct student services, it would behoove school counselors to take steps to increase their awareness about their well-being, including symptoms of burnout, and seek support to address concerns as they arise. Additionally, school counselors’ failure to address burnout is an ethical concern (American Counseling Association, 2014). School counselors could utilize a self-assessment (i.e., Counselor Burnout Inventory [Lee et al., 2007] or Professional Quality of Life Scale [Stamm, 2010]) to examine their level of burnout and subsequently address their work functions and lifestyle to alleviate symptoms.


As Moyer (2011) pointed out, supervision plays a vital role in school counselor development and can be a way to alleviate burnout. Thus, supervisors can provide opportunities for school counselors to learn ways to assess their well-being with the aim of developing career-sustaining behaviors to prevent burnout. For example, supervisors can inform school counselors of available screening measures and provide resources to aid in the development of career-sustaining behaviors. Similarly, supervisors can create activities (Lambie, 2006) that assess school counselors’ well-being, which allows counselors to address negative feelings. Efforts made to prevent burnout may increase the chances of school counselors performing direct student services. Higher rates of direct student services, such as individual and group counseling, also may lead to better educational outcomes for students (Lapan, 2012).


In an effort to reduce school counselors’ burnout and potentially increase their delivery of direct student services, practitioners and supervisors can initiate wellness-related activities. Butler and Constantine (2005) noted that peer supervision or consultation along with social support from colleagues and administrators might be helpful for reducing the effects of burnout. Furthermore, Lawson and Myers (2011) reported on the highest rated career-sustaining behavior, which provides potential to support the wellness of school counseling practitioners. As Meyer and Ponton (2006) noted, counselors as a whole tend to put their own wellness to the side in order to provide services to their clients. Therefore, another consideration for school districts and school counseling organizations is to offer wellness-focused training that could raise attention to counselors’ level of stress and burnout and provide strategies to enhance their wellness. Additionally, school counselors should remember to advocate for the profession and for themselves (Young & Lambie, 2007). It is important that administrators understand the critical wellness needs of school counselors, and school counselors should be among the first to advocate for this cause. As these findings indicate, there is a relationship between burnout and the quality of services offered by school counselors. Therefore, it is important that counselors “learn to be their own advocates and help dysfunctional workplaces become well” (Young & Lambie, 2007, p. 99).


In summary, this study examined the association of practicing school counselors’ degree of burnout, perceived stress and frequency of direct student services. The findings indicated that higher levels of burnout contribute to a decreased frequency of direct student services. Furthermore, school counselors’ perceived stress does not contribute to their facilitation of direct student services, but was positively associated with burnout. Overall, these findings are encouraging because the descriptive statistics indicate that school counselors operate at a low level of burnout and perceived stress and provide a moderate to high frequency of direct student services.



Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.






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Patrick R. Mullen, NCC, is an Assistant Professor at the College of William and Mary. Daniel Gutierrez, NCC, is an Assistant Professor at the University of North Carolina – Charlotte. Correspondence can be addressed to Patrick Mullen, School of Education, P.O. Box 8795, College of William & Mary, Williamsburg, VA  23188, prmullen@wm.edu.

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

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

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

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

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


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


IPV Typologies


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


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


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


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


Dating Violence


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


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


Technology and Conflict Resolution

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


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


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


Research Questions


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





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


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

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



Table 1


Descriptive Statistics for Study Constructs









Credit hours




Perceived stress (PSS)




Intimate justice (IJS)




Acceptance of violence (ACV)




Use of technology (UTR)




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



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


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


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


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


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




Preliminary Analysis

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


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


Primary Analysis

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


Table 2


Correlations Between Constructs of Interest

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

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


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


Table 3


Predictors of Partner Violence Risk (Intimate Justice)


            Δ R2



Model 1: ACV




Model 2: UTR



< .001

Model 3: PSS




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




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


Table 4


Frequency of Responses to Questions Regarding Use of Technology

Question (Has partner used technology to . . .)

% “Yes”

% “No”

1. Embarrass you?



2. Make you feel bad?



3. Control you?



4. Monitor you?



5. Argue with you?






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


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

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


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


Implications for Practice


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


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


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



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


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

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




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


Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.




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