Self-Efficacy, Attachment Style and Service Delivery of Elementary School Counseling

Kimberly Ernst, Gerta Bardhoshi, Richard P. Lanthier

This study explored the relationships between demographic variables, self-efficacy and attachment style with a range of performed and preferred school counseling activities in a national sample of elementary school counselors (N = 515). Demographic variables, such as school counselor experience and American School Counselor Association (ASCA) National Model training and use, were positively related to performing intervention activities that align with the ASCA National Model. Results of hierarchical regression analyses supported that self-efficacy beliefs also predicted levels of both actual and preferred service delivery of intervention activities. Interestingly, self-efficacy beliefs also predicted higher levels of performing “other” non-counseling activities that are considered to be outside of the school counselor role. An insecure attachment style characterized by high anxiety predicted a lower preference for intervention activities and also predicted the discrepancy between actual and preferred “other” non-counseling activities, revealing a higher preference for performing them.

Keywords: school counselor, ASCA National Model, self-efficacy, attachment style, service delivery

Professional school counselors are important contributors to education and serve an essential role in the academic, personal, social and career development of all students (American School Counselor Association [ASCA], 2012). Over the past decade, school counselors have been increasingly called upon to embrace data-driven, evidence-based standards of practice (ASCA, 2012; Erford, 2016) that bolster the achievement of all students (Shillingford & Lambie, 2010). Comprehensive developmental school counseling programs that are consistent with the ASCA National Model are currently considered best practice (ASCA, 2012) and identified as an effective means of delivering services to all students (Burnham & Jackson, 2000; Carey & Dimmitt, 2012; Gysbers & Henderson, 2012).

Data from school counseling research indicate that comprehensive developmental school counseling programs make a positive difference in student outcomes (Carey & Dimmitt, 2012; Scarborough & Luke, 2008). These programs are shown to impact overall student development positively, including academic, career and emotional development, as well as academic achievement (Fitch & Marshall, 2004; Lapan, Gysbers, & Petroski, 2001; Sink & Stroh, 2003). Furthermore, a range of individual school counselor activities and interventions is associated with positive changes in a number of important student outcomes, including academic performance, school attendance, classroom behavior and self-esteem (Whiston, Tai, Rahardja, & Eder, 2011).

However, studies examining actual school counselor practice indicate that school counselors spend a significant amount of time on activities that are not reflective of ASCA best practices, including clerical, administrative and fair share duties that take them away from performing essential school counseling activities (Bardhoshi, Schweinle, & Duncan, 2014; Burnham & Jackson, 2000; Foster, Young, & Hermann, 2005; Scarborough & Luke, 2008). A factor impeding school counselors’ ability to perform activities that align with best practices includes being burdened with time-consuming tasks that are outside their scope of practice (Bardhoshi et al., 2014). This may stem from either the historically ambiguous school counselor role (Gysbers & Henderson, 2012) or from competing demands from numerous stakeholders who may not fully understand the components of an effective school counseling program (Bemak & Chung, 2008). Indeed, school counselors report not spending adequate time engaged in the professional activities that they prefer (Scarborough, 2005; Scarborough & Luke, 2008), even though these preferences are consistent with best practice recommendations (Scarborough & Culbreth, 2008). Therefore, for many school counselors, performing within their professional role and sticking to best practice recommendations regarding their service delivery can be challenging and stressful (McCarthy, Kerne, Calfa, Lambert, & Guzmán, 2010).

Given that school counseling program implementation and interventions that align with ASCA are associated with positive outcomes for students in a variety of domains, and that tension exists between the actual and preferred practice of school counselors, the question now becomes: What factors contribute to effective school counseling service delivery? Studies indicate a positive relationship between years of experience and school counselor practice (Scarborough & Culbreth, 2008; Sink & Yillik-Downer, 2001), as it may take several years of experience to implement the breadth and complexity of interventions in a programmatic manner. Research outside the field of school counseling also has expanded beyond demographic variables to indicate that a number of individual characteristics, such as attachment style (Dozier, Lomax Tyrrell, & Lee, 2001; Hazan & Shaver, 1987), emotional stability, locus of control, self-esteem (Judge & Bono, 2001) and self-efficacy (Judge & Bono, 2001; Larson & Daniels, 1998), are related to an individual’s work performance.

To understand the underlying mechanisms that affect school counselor work performance, studies have explored potential organizational (e.g., school climate, perceived administration support), structural (e.g., training, supervision), and personal variables (e.g., experience, self-efficacy) related to counselor practice (Scarborough & Luke, 2008). Two school counselor interpersonal variables are of special focus in this study: self-efficacy and attachment. Individuals with higher levels of self-efficacy set higher goals for themselves and show higher levels of commitment, motivation, resilience and perseverance in achieving set goals (Bodenhorn & Skaggs, 2005), making the examination of school counselor self-efficacy important in investigating effective service delivery. On the other hand, attachment theory highlights the process by which early childhood development influences an individual’s capacity to relate to others and regulate emotion. Many lines of theoretical and empirical research in education and psychology have examined how attachment characteristics influence adult functioning, supporting the introduction of school counselor attachment style as a factor relating to work performance (Desivilya, Sabag, & Ashton, 2006; Hazan & Shaver, 1987; Kennedy & Kennedy, 2004; Marotta, 2002). School counselor self-efficacy and attachment characteristics are personal attributes conceptualized to contribute to the ability of school counselors to perform intervention activities that align with ASCA recommendations and positively impact student development and achievement.

 

Self-Efficacy

Self-efficacy involves beliefs about one’s own capability to successfully perform given tasks to accomplish specific goals (Lent & Hackett, 1987). As individuals confront important problems and tasks, they choose actions based on their beliefs of personal efficacy (Bandura, 1996). Self-efficacy may be a critical factor in school counselor work performance. Two meta-analytic studies of empirical research examining self-efficacy have shown that for a variety of occupations, there is a positive relationship between self-efficacy and work performance (Larson & Daniels, 1998; Stajkovic & Luthans, 1998). Studies examining school counselor self-efficacy have been a more recent addition to the literature, with reported results indicating that self-efficacy is related to school counselor gender, teaching experience (Bodenhorn & Skaggs, 2005), and supportive staff and administrators (Sutton & Fall, 1995).

In a study that extended the findings of previous self-efficacy research (Sutton & Fall, 1995), Scarborough and Culbreth (2008) examined factors that predicted discrepancies between actual and preferred practice in school counselors. Both self-efficacy beliefs and the amount of perceived administrative support predicted the difference between school counselors’ actual and preferred practice, with higher levels of support and outcome expectancy predicting higher levels of preferred intervention activities performance. In the current study, we plan to extend Scarborough and Culbreth’s work by examining the links between comprehensive elementary school counselor practice and overall school counselor self-efficacy while introducing attachment characteristics as a possible variable related to school counselor performance.

 

Attachment

Attachment theory describes how early experiences with attachment figures (e.g., mother) create inner representations referred to as internal working models. Those internal working models then shape patterns of behavior in response to significant others and to stressful situations (Mikulincer, Shaver, & Pereg, 2003). Adult attachment categories reflect those created in infancy and childhood and include secure, preoccupied (or anxious), dismissing (or avoidant), and fearful (both anxious and avoidant) styles (Bartholomew & Horowitz, 1991). In adults, attachment style encompasses affective responses in a variety of relationships, including co-workers, and can be activated by a number of stressful situations, including a stressful work environment (Mikulincer & Shaver, 2003, 2007).

Working effectively in a job or career contributes in meaningful ways to life satisfaction, self-esteem and social status, whereas not working effectively (and experiencing overload or burnout) can be extremely stressful and can cause serious emotional and physical difficulties (Mikulincer & Shaver, 2007). Specifically for school counselors, Wilkerson and Bellini (2006) reported that emotion-focused coping is a significant predictor of burnout, lending support to the examination of interpersonal factors in school counselor practice. To work effectively and not succumb to burnout, school counselors may have to activate self-regulatory skills associated with attachment, such as exploring alternatives, refining skills, adjusting to variation in tasks and role demands, and exercising self-control (Mikulincer & Shaver, 2007). In the field of school counseling, challenges include facing multiple demands and conflicting responsibilities (Cinotti, 2014); therefore, interpersonal communication, negotiation and adaptation become essential. Although attachment theory has received very little attention in school counseling literature (Pfaller & Kiselica, 1996), existing research suggests that various aspects of work are likely to be affected by individual differences in attachment style (Mikulincer & Shaver, 2007).

The purpose of this study was to explore demographic and interpersonal factors related to elementary school counseling practice. This research employed an associational survey research design to examine the relationships between school counselor overall self-efficacy, attachment style, and a range of performed and preferred activities in a sample of ASCA members who are elementary school counselors. Building on previous studies, we controlled for the anticipated variance in school counselor activities that might be contributed by previously identified demographic variables, including years of experience, ASCA National Model training and ASCA National Model use (Scarborough & Culbreth, 2008).

The first research question inquired about the relationship between self-efficacy beliefs and school counselor performed and preferred intervention activities that align with ASCA, controlling for the potential effect of the identified demographic variables. We hypothesized that self-efficacy beliefs would predict both school counselor preference and actual performance of these core activities, after controlling for the potential effect of relevant demographic variables. The second research question inquired about the relationship between attachment style and both counseling and non-counseling activities, controlling for the effect of the identified demographic variables. We hypothesized that school counselors who endorse higher levels of anxiety may prefer to engage in fewer intervention activities and more non-counseling activities. This could be in an effort to please others and conform to the administrative, fair share and clerical demands of the job. No hypothesis was forwarded on attachment avoidance and discrepancies between actual and preferred activities, as related research has not examined a possible relationship.

 

Method

 

Participants

The sample for this study consisted of elementary-level school counselors whose e-mail addresses were listed on the ASCA national database. We made the decision to select only elementary school counselors because of the unique emphasis on student personal and social development at this level (Dahir, 2004), as well as the distinct developmental needs of the student population that could potentially tap into school counselor attachment (Scarborough, 2005). Recruitment e-mails were sent to 3,798 ASCA member elementary school counselors through SurveyMonkey, employing a 3-wave multiple contact procedure. The original sample was adjusted to 3,550 because of undeliverable e-mail addresses. In total, 663 individuals responded to the survey, yielding a return rate of 19%. A priori power analysis using G*Power software determined that a minimum sample of 107 participants likely was necessary when conducting a multiple regression analysis with three independent variables. This G*Power calculation was based on an alpha level of .05, minimum power established at .80 and a moderate treatment effect size, and was conducted in the planning stages to inform needed sample size and minimize the probability of Type II error (Faul, Erdfelder, Buchner, & Lang, 2009). Therefore, surveys with incomplete data were completely removed from the analysis, resulting in a final sample size of 515 and a usable response rate of 14.5%.

The sample consisted of 89.6% females and 9.8% males (3 participants did not indicate gender). In terms of race and ethnicity, 86.6% were Caucasian, 6% African American, 2.9% Hispanic, 1.6% Multiracial, 1.4 % Asian/Pacific Islander, and 0.4% Native American (1.2% did not indicate race or ethnicity). The predominately female and Caucasian sample is consistent with school counseling research and reflective of the population (Bodenhorn & Skaggs, 2005).

Years of experience ranged from < 1 to 38, with a mean of 10.24 years. School enrollment ranged from 70 to 3,400 students, with a mean of 583.49 students. The large maximum enrollment number was caused by the inclusion of elementary-level counselors who were employed in K–12 schools. Counselor caseload ranged from 6 to 1,500, with the mean being 454.68 students. The mean age of respondents was 44 years, with a standard deviation of 11.02 years, and an age range spanning from 25 to 68 years. Regarding ASCA National Model (2012) training, only 8.5% reported not having received any training, with the overwhelming majority of the participants having received training from professional development opportunities sought on their own (67.6%), as part of master’s-level coursework (53.2%), or through their school district (31.5%). Only 5.2% of respondents reported no use of the ASCA National Model, with 14% reporting limited use, 33.8% some use, 31.5% a lot of use, and 15% extensive use.

 

Instruments

Instrumentation consisted of four measures, including a demographic questionnaire, the School Counselor Activity Rating Scale (SCARS; Scarborough, 2005), the School Counselor Self-Efficacy Scale (SCSE; Bodenhorn & Skaggs, 2005) and the Experiences in Close Relationships Scale-Short Form (ECR-Short Form; Wei, Russell, Mallinckrodt, & Vogel, 2007).

Demographic questionnaire. A demographic questionnaire consisting of 14 questions collected relevant information regarding participant age, gender, ethnicity, region, school setting (i.e., private, public) and level (e.g., elementary, middle), student enrollment, counselor caseload characteristics, degree earned, licensure and certification, years of experience and training in and use of the ASCA National Model. Demographic data were selected for inclusion based on a literature review indicating important relationships between these variables and school counseling outcomes (Scarborough & Culbreth, 2008; Sink & Yillik-Downer, 2001).

     School Counselor Activity Rating Scale (SCARS). The SCARS is a 48-item scale reflecting best practice recommendations for school counselors based on the ASCA National Standards (Campbell & Dahir, 1997) and the ASCA National Model (ASCA, 2003). It was designed to measure the frequency with which school counselors perform specific work activities, and the preferred frequency of performing those activities (Scarborough, 2005; Scarborough & Culbreth, 2008). The instrument contains five sections—counseling, consultation, curriculum, coordination and “other” activities. Participants indicate their actual and preferred performance of common school counseling activities on a frequency scale (1 = rarely do this activity to 5 = routinely do this activity), including “other” non-counseling activities that fall outside the school counselor role (e.g., coordinate the standardized testing program). A SCARS total score is calculated by adding the totals from each subscale or calculating mean scores, with higher scores indicating higher levels of engagement.

The SCARS validation study supported a four-factor solution representing the counseling, coordination, consultation and curriculum categories. Analysis on the “other” school counseling activities subscale, consisting of 10 items reflecting non-counseling activities, resulted in three factors: clerical, fair share and administrative. Convergent and discriminant construct validity also were reported (Scarborough, 2005). Cronbach’s alpha reliability coefficients, as reported by Scarborough on the eight subscales of actual and preferred dimensions, were .93 and .90 for curriculum; .84 and .85 for coordination; .85 and .83 for counseling; .75 and .77 for consultation; .84 and .80 for clerical; .53 and .58 for fair share; and .43 and .52 for administrative. In the current study, the Cronbach’s alpha coefficients for actual and preferred practice were .90 and .83 for curriculum; .84 and .86 for coordination; .80 and .81 for counseling; and .76 and .73 for consultation.

The intervention total subscale in our study consisted of the composite of the counseling, consultation, curriculum and coordination subscales, with Cronbach’s alpha reliability coefficients of .91 on both the actual and the preferred use dimensions. Similar to Scarborough (2005), the “other” duties subscale, consisting of clerical, fair share and administrative duties, had moderate reliability, with Cronbach’s alpha of .63 on the actual, and .68 on the preferred. The activities total subscale consisted of a combination of all SCARS subscales, with Cronbach’s alpha being .89 on the actual and .90 on the preferred. Various studies have been conducted since the initial validation of the SCARS and support its use as a tool yielding valid and reliable school counselor process scores (Scarborough & Culbreth, 2008; Shillingford & Lambie, 2010).

School Counselor Self-Efficacy Scale (SCSE). The SCSE (Bodenhorn & Skaggs, 2005) is a 43-item

self-report instrument designed to measure school counselor self-efficacy. The SCSE uses a 5-point Likert-type scale to measure responses (ranging from 1 = not confident to 5 = highly confident) and consists of five subscales: personal and social development; leadership and assessment; career and academic development; collaboration; and cultural acceptance. A composite mean is calculated to demonstrate overall self-efficacy. SCSE responses were evaluated for reliability, omission, discrimination and group differences (Bodenhorn & Skaggs, 2005), with results supporting high reliability for the composite scale (α = .95). Analyses also indicated group differences demonstrating score validity for the scale—participants who had teaching experience, had been practicing for three or more years, and were trained in and used the ASCA National Standards reported higher levels of self-efficacy. The total scale SCSE alpha in the current study was .96.

     Experiences in Close Relationships Scale (ECR)-Short Form. The ECR-Short Form (Wei et al., 2007) is a 12-item self-report measure designed to assess a general pattern of adult attachment. The ECR-Short Form is based on the longer Experiences in Close Relationship Scale (Brennan, Clark, & Shaver, 1998). Factor analysis revealed two dimensions of adult attachment, anxiety and avoidance, which have received professional consensus (Bartholomew & Horowitz, 1991; Mikulincer & Shaver, 2003). High scores on either or both of these dimensions are indicative of an insecure adult attachment orientation. Low levels of attachment anxiety and avoidance indicate a secure orientation (Bartholomew & Horowitz, 1991; Brennan et al., 1998; Lopez & Brennan, 2000; Mallinckrodt, 2000).

Internal consistency was adequate with coefficient alphas from .77 to .86 for the anxiety subscale and from .78 to .88 for the avoidance subscale, and confirmatory factor analyses provided evidence of construct validity with a two-factor model (i.e., anxiety and avoidance), indicating a good fit for the data. Reported test-retest reliabilities averaged .83. For the current study, ECR-S alphas were .75 for the anxiety subscale and .81 for the avoidance subscale.

Data Analysis
Data were analyzed using the Statistical Package for Social Sciences (SPSS Version 18), with multiple hierarchical regressions used to answer both research questions. Hierarchical regression was selected to determine the relative importance of the predictor variables, over and above that which can be accounted for by other previously identified predictors regarding school counselor service delivery (i.e., years of experience, ASCA National Model training and ASCA National Model use). Predictor variables included self-efficacy beliefs (SCSE total score), attachment anxiety (ECR-Short Form Anxiety subscale) and attachment avoidance (ECR-Short Form Avoidance subscale). Outcome variables included actual (SCARS total Actual scale) and preferred (SCARS total Preferred scale) intervention activities, “other” non-counseling activities (SCARS Other Activities scale) and the discrepancy between actual and preferred intervention and “other” activities.

Prior to analysis of the research questions, correlations were conducted among the predictor and outcome variables. Identified predictors (i.e., years of experience, ASCA National Model training and ASCA National Model use) were also correlated with the SCARS criterion variables. For the hierarchical regression, identified predictors were entered first as a block, followed by the new predictors included in this study (Field, 2009). This predetermined order of entry is congruent with Cohen and Cohen’s (1993) recommendations for using hierarchical regression and entering the demographic variables in the initial step. Additionally, the order of entry reflected the principle of presumed causal priority (Cohen & Cohen, 1993; Petrocelli, 2003). For the second step, we decided to enter attachment anxiety prior to avoidance, as we anticipated it would be more important in predicting the outcome variables (Field, 2009). Reported effect size estimates reflect the following guidelines: r of .1 (small), .3 (medium) and .5 (large); and R2 of .01 (small), .09 (medium) and .25 (large; Cohen, 1988).

 

Results

We first examined the correlation among the identified school counselor demographic variables (control variables) and the actual and preferred SCARS variables. Years of experience showed a small but significant positive correlation with actual intervention activities (r = .20, p < .05). ASCA National Model use showed a moderate positive correlation with actual intervention activities (r = .44, p < .05), but smaller relationships with preferred intervention activities (r = .15, p < .05). Additional correlation analysis revealed relationships among school counseling experience and the main predictor variables that were of interest in this study. For example, years of experience showed a significant, although small, negative correlation to attachment anxiety (r = -.14, p < .05). Both attachment anxiety and avoid-
ance showed negative correlations to self-efficacy (r = -.20 and -.15, p < .05, respectively). Lastly, self-
efficacy showed a small positive correlation with years of experience (r = .25, p < .05) and ASCA National Model use (r =.27, p < .05).

Self-Efficacy Predicting Actual and Preferred Intervention and Other Activities
     Multiple hierarchical regression analyses were conducted to determine if self-efficacy was positively associated with actual and preferred intervention activities, after controlling for demographic variables (see Table 1). Self-efficacy was the predictor variable and actual and preferred intervention activities were the criterion variables in separate analyses. Because years of experience, ASCA National Model training and ASCA National Model use were correlated with the SCARS criterion variables, these control variables were entered as a block prior to entering self-efficacy beliefs. The model for actual activities was significant: F(1, 506) = 112.37, p < .05, supporting the hypothesis. The standardized beta between self-efficacy and actual intervention activities was .40 and the effect size based on the adjusted R2 statistic indicated that 37% of the variance in actual activities was accounted for by self-efficacy, after blocking for the control variables, a large effect size. Results for preferred school counselor activities showed a similar result, as the model for preferred activities also was significant: F(1, 506) = 78.59, p < .05. The standardized beta between self-efficacy and preferred intervention activities was .39, and the adjusted R2 indicated 15% of the variance in preferred activities was accounted for by self-efficacy, after blocking for the control variables, a medium effect size.


Table 1.

Results from hierarchical multiple regression using self-efficacy to predict SCARS actual and preferred intervention activities

Block 1

Block 2

Predictor Variable

B

SE B

β

B

SE B

β

Actual
Experience (years)

0.01

0.00

 0.20*

0.01

0.01

0.10*

A.N.M. Training

-0.02

0.03

-0.60

-0.02

0.03

-0.03

A.N.M. Use

0.22

0.02

0.44*

0.17

0.02

0.34*

Self-Efficacy

0.45

0.04

0.40*

R2

0.23

0.37

F for change in R2

50.46*

112.37**

Preferred
Experience (Years)

0.00

0.00

 0.04

-0.00

0.00

-0.05

A.N.M. Training

-0.00

0.03

-0.01

-0.01

0.03

-0.01

A.N.M. Use

0.06

0.02

0.15*

0.02

0.02

0.05

Self-Efficacy

0.37

0.04

0.39**

R2

0.02

0.15

F for change in R2

3.92*

78.59*


Note: Analysis N = 511 (actual & preferred); * p < .05. A.N.M. denotes ASCA National Model.

 

Similar hierarchical multiple regression analyses were conducted using school counselor self-efficacy as the predictor variable and “other” school counseling activities as the criterion variable, after controlling for demographic variables (see Table 2). The models for preferred and actual “other” activities were both significant; F(1, 506) = 20.89, p < .05; and F(1, 506) = 13.60, p < .05, respectively. The standardized beta for actual “other” activities was .21 and for preferred “other” activities was .17. Self-efficacy accounted for (R2 =) 43% of the variance in actual “other” activities performed and (R2 =) 33% of preferred “other” activities, indicating large effect sizes.
Table 2.

Results from hierarchical multiple regression using self-efficacy to predict SCARS actual and preferred “other” non-counseling activities

Block 1

Block 2

Predictor Variable

B

SE B

β

B

SE B

β

Actual
Experience (Years)

0.00

0.00

0.02

-0.00

0.00

-0.03

A.N.M. Training

0.04

0.04

0.05

0.04

0.04

-0.05

A.N.M. Use

-0.04

0.03

-0.06

-0.07

0.03

-0.11

Self-Efficacy

0.29

0.06

0.21*

R2

0.00

0.43

F for change in R2

0.63

20.89*

Preferred
Experience (Years)

0.01

0.00

 0.07

0.00

0.00

0.03

A.N.M. Training

-0.02

0.04

-0.03

-0.02

0.04

-0.03

A.N.M. Use

-0.00

0.03

-0.0

-0.00

0.03

-0.00

Self-Efficacy

0.22

0.06

0.17*

R2

0.02

0.33

F for change in R2

1.13

13.60**


Note: Analysis N = 511 (actual & preferred); * p < .05. A.N.M. denotes ASCA National Model.
Attachment Predicting Actual and Preferred Intervention and “Other” Activities
     Hierarchical multiple regressions were used to assess the ability of attachment style to predict school counselor interventions and “other” non-counseling activities, after controlling for demographic variables. In our study, attachment style was measured by the ECR-Short Form (Wei et al., 2007) on two dimensions—attachment anxiety and avoidance. As in the regression analyses for counselor self-efficacy, years of experience, ASCA National Model training and ASCA National Model use were entered as a block prior to entering attachment anxiety and avoidance. Attachment anxiety, but not attachment avoidance, revealed predictive utility for the SCARS preferred intervention subscale scores, showing a negative relationship: F(1, 505) = 2.60, p < .05. The standardized beta for preferred intervention activities was -.11 and attachment anxiety accounted for only 2% of the variance for preferred intervention activities, a small effect size.

To test whether attachment anxiety was associated with discrepancies between a range of actual and preferred school counseling activities, separate regression analyses were performed. We used attachment anxiety and attachment avoidance as the predictor variables and the discrepancy score variables that were created by subtracting the actual from the preferred scores for the actual and preferred intervention activities and “other” activities subscales. As before, years of experience, ASCA National Model training and ASCA National Model use were correlated with the SCARS criterion variables and were entered as a block prior to entering the attachment variables. For intervention activities, a relationship was not supported for either attachment anxiety or attachment avoidance. However for the “other” non-counseling activities, a relationship between attachment anxiety and the actual/preferred discrepancy revealed a statistically significant result over and above that accounted for by demographic variables: F(1, 505) = 3.16, p < .05 with a standardized beta of .12. Therefore, attachment anxiety predicted a discrepancy that revealed a higher preference for performing “other” non-counseling activities. However, the effect size showed that anxiety accounted for only 1% of the variance in the “other” activities discrepancy score (see Table 3).


Table 3

Results from hierarchical multiple regression using attachment to predict SCARS intervention scores and the actual/prefer discrepancy scores for intervention and “other” activities

Block 1

Block 2

Block 1

Block 2

Predictor Variable

B

SE B

β

B

SE B

β

B

SE B

β

B

SE B

β

Intervention Actual

Intervention Discrepancy

Experience (years)

0.01

0.00

 0.20*

0.02

0.00

 0.19*

-0.01

0.00

-0.18*

-0.01

 0.00

 -0.18*

A.N.M. Training

-0.02

0.03

 -0.03

-0.02

0.02

 -0.02

0.01

0.03

  0.02

0.02

0.03

 0.02

A.N.M. Use

0.22

0.02

 0.44*

0.22

0.02

 0.44*

-0.16

0.02

-0.34*

0.16

0.02

 0.34*

Anxiety

-0.03

0.02

 -0.06

-0.01

0.02

 -0.03

Avoidance

0.01

0.02

 0.02

0.00

0.02

 -0.01

R2

0.23

0.00

0.15

         0.00

F for change in R2

       50.46*

0.34

        29.69*

0.33

Intervention Preferred

“Other” Discrepancy

Experience (years)

0.00

0.00

 0.04

0.00

0.03

0.02

0.04

0.03

 0.06

0.03

0.03

 0.04

A.N.M. Training

0.00

0.03

 -0.01

0.00

0.03

0.00

-0.61

0.31

 -0.10*

-0.57

0.31

-0.09

A.N.M. Use

0.06

0.02

0.15*

0.06

0.02

 0.14*

0.57

0.24

 0.12*

0.57

0.23

 0.12*

Anxiety

-0.05

0.02

-0.11*

-0.58

0.23

 0.12*

Avoidance

0.01

0.02

0.02

0.29

0.25

 0.06

R2

0.02

 0.01

0.02

0.01

F for change in R2

         3.92*

         2.6

         3.21*

         3.16*


Note:
Analysis N = 511 (actual & preferred); * p < .05. A.N.M. denotes ASCA National Model.

 

Discussion

To date, few studies have examined how school counselor personal characteristics are linked to successful programs (Scarborough & Luke, 2008). Using a nationwide sample, we examined how self-efficacy is related to a range of school counselor activities in elementary schools and introduced attachment style as a potential variable related to school counselor practice. Years of experience working as a school counselor as well as the training in and use of the ASCA National Model in program implementation were identified from the literature as variables of importance and were included in our analyses.

As anticipated the number of years of experience was related to actual performance of intervention activities by school counselors. Also, school counselors in this sample who had received more training in the ASCA National Model were more likely to perform the intervention activities of counseling, consultation, curriculum and coordination. These activities are considered core activities for effective program implementation. Furthermore, counselors who endorsed more fully implementing the ASCA National Model within their program were significantly more likely to perform these core intervention activities and also indicated a preference for spending their time in these activities. This result is in line with previous findings supporting that counselors who incorporated the National Standards for School Counseling Programs (Campbell & Dahir, 1997) into their programs were more likely to have preferences that aligned with professional standards and actually practiced as they preferred (Scarborough & Culbreth, 2008). It is promising that over 75% of school counselors in the current study reported some use to extensive use of the ASCA National Model. The large number of counselors who reported ASCA National Model use could be indicative of a recent focus to define standards of practice and increase positive student outcomes through systematic and programmatic delivery. With regard to non-counseling activities, results did not support a relationship with ASCA National Model training and use.

     Looking beyond the demographic variables, the findings of the current study support previous research that found important links between school counselor self-efficacy beliefs and program implementation (Bodenhorn, Wolfe,  & Airen, 2010). In the current study, overall school counselor self-efficacy beliefs predicted the delivery of activities aligned with the ASCA National Model above and beyond the demographic variables analyzed. School counselors who believed they were capable of performing in accordance with activities aligned with the ASCA National Standards were more likely to actually perform and want to perform school counseling intervention activities consistent with the ASCA National Model.

It is interesting to note that school counselors with higher self-efficacy beliefs were more likely to perform non-counseling activities when compared to counselors with lower self-efficacy. These results suggest that counselors with higher levels of self-efficacy beliefs may not discriminate between intervention and “other” non-counseling activities, by performing both more frequently. Highly efficacious school counselors may simply do more, whether or not the activity aligns with ASCA recommendations. As demands for school counselors increase and current expectations for school counselors do not perfectly align with professional best practices (Cinotti, 2014), highly efficacious school counselors may tackle all duties earnestly in order to address their responsibilities.

In the current study, attachment anxiety negatively predicted school counselor preferred engagement in intervention activities (i.e., counseling, consultation, curriculum, coordination), indicating that anxiously attached school counselors actually preferred to perform fewer intervention activities. Additionally, school counselor attachment anxiety predicted a discrepancy between actual and preferred activities that are considered outside the scope of school counseling practice, including clerical, administrative and fair share responsibilities. When considering the relationship between attachment anxiety and this discrepancy, which revealed a higher preference for performing these “other” activities, there are a few possible explanations. Perhaps anxiously attached counselors reporting a greater discrepancy on the “other” subscale find it more difficult to align their identity with the counseling professional identity model promoted by ASCA. Although these non-counseling activities do not align with ASCA recommendations, they are nevertheless expected and valued by supervisors. Research has suggested that anxiously attached individuals may tend to take on additional work obligations as a way to please others and tend to be motivated by approval of colleagues and supervisors (Hazan & Shaver, 1987). Additionally, anxiously attached workers seek close relationships with their colleagues and supervisors and have more difficulty resisting unreasonable demands in the workplace (Leiter, Day, & Price, 2015). Given that school administrators directly influence the assignment of inappropriate duties performed by school counselors, and that strong advocacy and leaderships skills are essential to negotiate an identity and role that is more aligned with ASCA recommendations (Cinotti, 2014), anxiously attached school counselors may find it more difficult to test those relationships and may instead endorse the identity expected by their supervisors. Indeed, the literature points out that school administrators perceive school counselors as operating mainly from an educator—versus a counselor—professional identity (Cinotti, 2014).

There was a low variability in attachment scores of this particular sample (i.e., school counselors endorsed relatively high levels of self-efficacy and low levels of attachment insecurity), which could have contributed to the results of this research. Within the clinical training component of their education, school counselors are taught the importance of ongoing self-exploration and to develop awareness of their responses within the context of clinical practice. It is possible that education and training in the importance of self-awareness could interrupt effects on school counselor practice that are related to higher levels of attachment anxiety.

Counselors in this sample consistently indicated that they preferred to spend more time in intervention activities that are in keeping with best practices and are related to positive outcomes for students and preferred to spend less time in non-counseling related activities. When compared to other research using the SCARS, they also reported engaging in fewer non-counseling activities. As performing non-counseling activities is associated with burnout in school counselors (Bardhoshi et al., 2014), this is a positive finding that might be reflective of the current direction of the profession.

 

Study Limitations

The potential for self-selection and social desirability bias was a limitation of this study. Only elementary school counselors who were ASCA members were invited to participate. It is possible that those members who did volunteer to participate may differ in a variety of ways from those individuals who did not respond. Given the $115 membership fee to join the association, it is possible that counselors from wealthier school districts, with higher salaries or access to a counseling budget assisting with the membership fee, are more heavily represented. School counselors who chose to become members of ASCA may vary distinctly in work-related performance, self-efficacy beliefs and attachment style than those counselors who chose not to become members of the association. ASCA members likely have more professional development opportunities and more exposure to information regarding best practices, which could impact both their self-efficacy beliefs and practice.

Despite our use of multiple contact procedures to obtain an acceptable response rate, a limitation worth noting is the lower response rate. Lower response rates are often noted for online surveys (Dillman, Smyth, & Christian, 2014), including in the field of counseling (Granello & Wheaton, 2004). Although we received over 200 undeliverable e-mails, which reduced the original sample size, there is no way to accurately estimate how many individuals actually received the survey in their inbox (Granello & Wheaton, 2004). It is indeed possible that spam-filtering software resulted in many invitations not reaching their intended recipients. Therefore, our reported response rate represents a conservative estimate (Vespia, Fitzpatrick, Fouad, Kantamneni, & Chen, 2010). In addition, it was assumed that the attrition of 100 participants was likely the result of the time required to complete the survey. Our analysis supported that there were no statistically significant differences between the two groups (i.e., completers and non-completers) on demographic variables and that our final sample size was adequate for the selected statistical tests. However, readers should use caution when generalizing the results of this study to all elementary school counselors. A final consideration is that causal relationships cannot be derived from the results of this study, as the research design was relational in nature.

 

Implications for School Counseling Practice
     Previous studies have indicated that higher levels of school counselor self-efficacy are positively associated with higher levels of comprehensive program implementation (Bodenhorn et al., 2010). For many, the route to increased self-efficacy is through personal and vicarious accomplishments (Bodenhorn et al., 2010; Scarborough & Culbreth, 2008; Sutton & Fall, 1995). Therefore, opportunities to learn and practice the skill set specific to school counseling must be promoted in the education and training of students.

School counselor educators have a crucial role in ensuring that future school counselors have a strong foundation with which to begin their careers. Counselor education programs have often not provided adequate preparation for school counselors because there has been incongruence between their training and their actual roles in schools (McMahon, Mason, & Paisley, 2009). A novice school counselor who has had education and training that is consistent with his or her actual work role will have greater chances of acquiring increased self-efficacy from the start. In a cascade, self-efficacy will likely promote stronger program implementation and, in turn, positive student outcomes.

More specifically, requiring trainees to provide a range of services will support the transition from training to work. Trainees need opportunities to provide specific interventions (e.g., counseling individuals and groups, teaching classroom lessons) while also evaluating the impact of these interventions, teaching them how to use data in their programs and potentially boosting self-efficacy beliefs (Akos & Scarborough, 2004). Trainees should also be given opportunities to engage in coordination activities to gain experience in the organizational aspects of a comprehensive developmental school counseling program. Finally, counselor educators who supervise internship courses must maintain strong communication with site supervisors to ensure continuity and appropriate trainee experiences.

Although effect sizes related to attachment characteristics in this study were small, they imply that attachment theory could be a useful adjunct to understanding school counselor practice. Using attachment concepts as a guide for supervision or structured professional development opportunities could assist school counselors’ ongoing efforts to understand their own behavior and motivations in the work setting. Graduate coursework specific to attachment constructs has the potential to be a useful component of school counselor education, especially because the cultivation of healthy interpersonal relationships has a tremendous potential to facilitate positive change in schools.

 

Recommendations for Future Counseling Research
The moderately strong association in this study between school counselor self-efficacy and activities recommended by the ASCA National Model indicates that understanding the factors affecting school counselor self-efficacy warrants further attention. Research outside the field of school counseling has identified a positive relationship between attachment security and higher levels of competence and self-efficacy beliefs (Mikulincer & Shaver, 2007). Given that self-efficacy was significantly negatively correlated to both attachment anxiety and avoidance in this study, additional studies examining these relationships may clarify possible connections between school counselor self-efficacy beliefs and attachment characteristics. We did not examine whether SCSE subscales were differentially related to school counselor activities. Doing so could identify professional areas about which counselors feel most efficacious and those that need bolstering. Explaining the reasons some school counselors perform more successfully is an enduring goal of counseling research (Sutton & Fall, 1995).

Our results did indicate significant relationships between attachment anxiety and school counselor practice. Specifically, attachment anxiety predicted a lower preference for intervention activities, as well as a discrepancy between actual and preferred “other” non-counseling activities that revealed a higher preference for performing them. Although small, these results could lead to further understanding of the factors related to differences in school counselor practice. As this study has taken a broad view of how school counselor practice could be affected by attachment dimensions, qualitative studies examining the unique experiences of anxiously attached counselors in their work environment have the potential to reveal important perspectives. Identifying how attachment style may contribute to the endorsement and performance of specific intervention activities could lead to a greater understanding of school counseling practice.

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.


References

Akos, P., & Scarborough, J. L. (2004). An examination of the clinical preparation of school counselors. Counselor Education and Supervision, 44, 96–107. doi:10.1002/j.1556-6978.2004.tb01863.x

American School Counselor Association. (2003). The ASCA national model: A framework for school counseling programs. Alexandria, VA: Author.

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

Bandura, A. (1996). Ontological and epistemological terrains revisited. Journal of Behavior Therapy and Experimental Psychiatry, 27, 323–345. doi:10.1016/S0005-7916(96)000493

Bardhoshi, G., Schweinle, A., & Duncan, K. J. (2014). Understanding the impact of school factors on school counselor burnout: A mixed methods study. The Professional Counselor, 4, 426–443.

Bartholomew, K., & Horowitz, L. M. (1991). Attachment styles among young adults: A test of a four-category model. Journal of Personality and Social Psychology, 61, 226–244. doi:10.1037/0022-3514.61.2.226

Bemak, F., & Chung, R. C.-Y. (2008). New professional roles and advocacy strategies for school counselors: A multicultural/social justice perspective to move beyond the nice counselor syndrome. Journal of Counseling & Development, 86, 372–381.

Bodenhorn, N., & Skaggs, G. (2005). Development of the School Counselor Self-Efficacy Scale. Measurement and Evaluation in Counseling and Development, 38, 14–28.

Bodenhorn, N., Wolfe, E. W., & Airen, O. E. (2010). School counselor program choice and self-efficacy: Relationship to achievement gap and equity. Professional School Counseling, 13, 165–174.

Brennan, K. A., Clark, C. L., & Shaver, P. R. (1998). Self-report measurement of adult attachment: An integrative overview. In J. A. Simpson & W. S. Rholes (Eds.), Attachment theory and close relationships (pp. 46–76). New York, NY: Guilford Press.

Burnham, J. J., & Jackson, C. M. (2000). School counselor roles: Discrepancies between actual practice and existing models. Professional School Counseling, 4, 41–49.

Campbell, C. A., & Dahir, C. A, (1997). Sharing the vision: The national standards for school counseling programs. Alexandra, VA: American School Counselor Association Press.

Carey, J., & Dimmitt, C. (2012). School counseling and student outcomes: Summary of six statewide studies. Professional School Counseling, 16, 146–153.

Cinotti, D. (2014). Competing professional identity models in school counseling: A historical perspective and commentary. The Professional Counselor, 4, 417–425.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Cohen, J., & Cohen, P. (1993). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Dahir, C. A. (2004). Supporting a nation of learners: The role of school counseling in educational reform. Journal of Counseling & Development, 82, 344–353.

Desivilya, H. S., Sabag, Y., & Ashton, E. (2006). Prosocial tendencies in organizations: The role of attachment styles and organizational justice in shaping organizational citizenship behaviour. International Journal of Organizational Studies, 14, 22–42. doi:10.1108/10553180610739731

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, mail, and mixed-mode surveys: The tailored design method (4th ed.). New York, NY: Wiley.

Dozier, M., Lomax, L., Tyrrell, C. L., & Lee, S.W. (2001). The challenge of treatment for clients with dismissing states of mind. Attachment and Human Development, 3, 62–76.

Erford, B. T. (2016). Professional school counseling: Integrating theory and practice into a data-driven, evidence-based approach. In B. T. Erford (Ed.), Professional School Counseling: A Handbook of Theories, Programs, and Practices (3rd ed., pp. 3–8). Austin, TX: ProEd.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. doi:10.3758/BRM.41.4.1149

Field, A. P. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles, CA: Sage.

Fitch, T. J., & Marshall, J. L. (2004). What counselors do in high-achieving schools: A study on the role of the school counselor. Professional School Counseling, 7, 172–179.

Foster, L. H., Young, J. S., & Hermann, M. (2005). The work activities of professional school counselors: Are the national standards being addressed? Professional School Counseling, 8, 313–321.

Granello, D. H., & Wheaton, J. E. (2004). Online data collection: Strategies for research. Journal of Counseling & Development82, 387–393.

Gysbers, N. C., & Henderson, P. (2012). Developing and managing your school guidance program (5th ed.). Alexandria, VA: American Counseling Association.

Hazan, C., & Shaver, P. (1987). Romantic love conceptualized as an attachment process. Journal of Personality and Social Psychology, 52, 511–524. doi:10.1037/0022-3514.52.3.511

Judge, T. A., & Bono, J. E. (2001). Relationship of core self-evaluations traits—self-esteem, generalized self-efficacy, locus of control, and emotional stability—with job satisfaction and job performance: A meta-analysis. Journal of Applied Psychology, 86, 80–92. doi:10.1037//0021-9010.86.1.80

Kennedy, J. H., & Kennedy, C. E. (2004). Attachment theory: Implications for school psychology. Psychology in the Schools, 41, 247–259. doi:10.1002/pits.10153

Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A statewide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x

Larson, L. M., & Daniels, J. A. (1998). Review of counseling self-efficacy literature. The Counseling Psychologist, 26, 179–218. doi:10.1177/0011000098262001

Leiter, M. P., Day, A., & Price, L. (2015). Attachment styles at work: Measurement, collegial relationships, and burnout. Burnout Research, 2, 25–35.

Lent, R. W., & Hackett, G. (1987). Career self-efficacy: Empirical status and future directions. Journal of Vocational Behavior, 30, 347–382. doi:10.1016/0001-8791(87)90010-8

Lopez, F. G., & Brennan, K. A. (2000). Dynamic processes underlying adult attachment organization: Toward an attachment theoretical perspective on the healthy and effective self. Journal of Counseling Psychology, 47, 283–300. doi:10.1037/0022-0167.47.3.283

Mallinckrodt, B. (2000). Attachment, social competencies, social support, and interpersonal process in psycho-therapy. Psychotherapy Research, 10, 239–266. doi:10.1093/ptr/10.3.23

Marotta, S. A. (2002). An ecological view of attachment theory: Implications for counseling. Journal of Counseling & Development, 80, 507–510.

McCarthy, C. J., Kerne, V. V. H., Calfa, N. A., Lambert, R. G., & Guzmán, M. (2010). An exploration of school

counselors’ demands and resources: Relationship to stress, biographic, and caseload characteristics.

Professional School Counselor, 13, 146–158.

McMahon, H. G., Mason, E. C. M., & Paisley, P. (2009). School counselor educators as educational leaders promoting systemic change. Professional School Counseling, 13, 116–124. doi:10.5330/PSC.n.2010-13.116

Mikulincer, M., & Shaver, P. R. (2003). The attachment behavioral system in adulthood: Activation, psycho-dynamics, and interpersonal processes. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 35, pp. 53–152). New York, NY: Academic Press.

Mikulincer, M., & Shaver, P. R. (2007). Attachment in adulthood: Structure, dynamics, and change. New York, NY: Guilford Press.

Mikulincer, M., Shaver, P. R., & Pereg, D. (2003). Attachment theory and affect regulation: The dynamics, development, and cognitive consequences of attachment-related strategies. Motivation and Emotion, 27, 77–102. doi:10.1023/A:1024515519160

Petrocelli, J. V. (2003). Hierarchical multiple regression in counseling research: Common problems and possible remedies. Measurement and Evaluation in Counseling and Development, 36, 9–22.

Pfaller, J. E., & Kiselica, M. S. (1996). Implications of attachment theory for the role of school counselors. The School Counselor, 43, 208–217.

Scarborough, J. L. (2005). The School Counselor Activity Rating Scale: An instrument for gathering process data. Professional School Counseling, 8, 274–283.

Scarborough, J. L., & Culbreth, J. R. (2008). Examining discrepancies between actual and preferred practice of school counselors. Journal of Counseling & Development, 86, 446–459. doi:10.1002/j.1556-6678.2008.tb00533.x

Scarborough, J. L., & Luke, M. (2008). School counselors walking the walk and talking the talk: A grounded theory of effective program implementation. Professional School Counseling, 11, 404–416.

Shillingford, M. A., & Lambie, G. W. (2010). Contribution of professional school counselors’ values and leader-

ship practices to their programmatic service delivery. Professional School Counseling, 13, 208–217. doi:10.5330/PSC.n.2010-13.208

Sink, C. A., & Stroh, H. R. (2003). Raising achievement test scores of early elementary school students through comprehensive school counseling programs. Professional School Counseling, 6, 350–364.

Sink, C. A., & Yillik-Downer, A. (2001). School counselors’ perceptions of comprehensive guidance and counsel-ing programs: A national survey. Professional School Counseling, 4, 278–288.

Stajkovic, A., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-analysis. Psychological Bulletin, 124, 240–261. doi:10.1037/0033-2909.124.2.240

Sutton, J. M., Jr., & Fall, M. (1995). The relationship of school climate factors to counselor self-efficacy. Journal of Counseling & Development, 73, 331–336.

Vespia, K. M., Fitzpatrick, M. E., Fouad, N. A., Kantamneni, N., & Chen, Y.-L. (2010). Multicultural career counseling: A national survey of competencies and practices. The Career Development Quarterly59, 54–71. doi:10.1002/j.2161-0045.2010.tb00130.x

Wei, M., Russell, D. W., Mallinckrodt, B., & Vogel, D. L. (2007). The Experiences in Close Relationship Scale (ECR)-Short Form: Reliability, validity, and factor structure. Journal of Personality Assessment, 88, 187–204. doi:10.1080/00223890701268041

Whiston, S. C., Tai, W. L. , Rahardja, D., & Eder, K. C. (2011). School counseling outcome: A meta-analytic

examination of interventions. Journal of Counseling & Development, 89, 37–55.
doi:10.1002/j.1556-6678.2011.tb00059.x

Wilkerson, K., & Bellini, J. (2006). Intrapersonal and organizational factors associated with burnout among school counselors. Journal of Counseling & Development, 84, 440–450. doi:10.1002/j.1556-6678.2006.tb00428.


Kimberly Ernst is a counselor in independent practice in Washington, DC. Gerta Bardhoshi, NCC, is an Assistant Professor at the University of Iowa. Richard P. Lanthier is an Associate Professor at George Washington University. Data for this article originated from the first author’s doctoral dissertation. Correspondence can be addressed to Gerta Bardhoshi, College of Education, N352 Lindquist Center, Iowa City, IA 52242-1529, gerta-bardhoshi@uiowa.edu.

The ASCA Model and a Multi-Tiered System of Supports: A Framework to Support Students of Color With Problem Behavior

Christopher T. Belser, M. Ann Shillingford, J. Richelle Joe

The American School Counselor Association (ASCA) National Model and a multi-tiered system of supports (MTSS) both provide frameworks for systematically solving problems in schools, including student behavior concerns. The authors outline a model that integrates overlapping elements of the National Model and MTSS as a support for marginalized students of color exhibiting problem behaviors. Individually, the frameworks employ data-driven decision making as well as prevention services for all students and intervention services for at-risk students. Thus, the integrated model allows schools to provide objective alternatives to exclusionary disciplinary actions (e.g., suspensions and expulsions) that are being assigned to students of color at a disproportionate rate. The manuscript outlines the steps within the integrated model and provides implications for school counselors and counselor educators.

Keywords: ASCA National Model, multi-tiered system of supports, school counselors, marginalized students, students of color

Educational disparities are well documented for students of color in the United States (Delpit, 2006; Ford & Moore, 2013; U.S. Department of Education [USDOE], 2014). Today’s students of color are facing lower graduation rates, overuse of exclusionary disciplinary action, overrepresentation in exceptional education programming and school policies that negatively impact students of color rather than support them (Moore, Henfield, & Owens, 2008; USDOE, 2014; R. Palmer & Maramba, 2010; Toldson & Lewis, 2012). School discipline policies based on a framework of zero tolerance have not reduced suspensions or expulsions as initially intended. Instead, these policies have resulted in more students being excluded from the classroom due to reactive disciplinary action (Skiba, 2014). Bernstein (2014) posited that these policies are increasing the educational achievement gap and negatively impacting the development of students of color. What then can be done as an alternative to or as a measure to prevent exclusionary disciplinary actions such as suspensions and expulsions?

A multi-tiered system of supports (MTSS) is a systematic data-driven program designed to address academic concerns and problem behavior by utilizing both prevention and intervention strategies (Sugai & Horner, 2009). Specific to behavior-related concerns, MTSS programs offer a structured method for providing both universal and individual support for students and present data-driven alternatives to suspension and expulsion. School counselors are uniquely positioned to play a critical role in the implementation of such programs due to their training in data analysis, program development and direct service delivery. Moreover, MTSS programs align well with the American School Counselor Association (ASCA) National Model (2012a).

The ASCA National Model has themes of social justice, advocacy and systemic change infused throughout, as comprehensive school counseling programs are designed to remove barriers to student success and help students reach their potential in the areas of academic, career, social and emotional development (ASCA, 2012a). With these themes in mind, integrating the National Model with the objective and data-driven framework of MTSS may offer one solution for systemic educational disparities such as the school-to-prison pipeline. The purpose of this article is to describe a model for integrating elements of the ASCA National Model within the MTSS framework. The authors will describe steps involved in the process and will provide context for how such an intervention can specifically benefit students of color.

The School-to-Prison Pipeline

More than 6.8 million individuals were under supervision of the adult correctional system in the United States at the end of 2014, a rate of 1 in 36 adults (Kaeble, Glaze, Tsoutis, & Minton, 2015). Of those under correctional supervision, over 1.5 million were held in state and federal correctional facilities (Carson, 2015). Although these numbers mark a slight decrease in the correctional population since 2007 (Kaeble et al., 2015), the American incarceration rate has quadrupled since the 1970s (Travis, Western, & Redburn, 2014). The growth of incarceration in the United States over the past four decades has largely affected the Black and Latino communities, both of which are disproportionately represented among individuals involved with the correctional system (Carson, 2015). Scholars in multiple academic disciplines have linked American drug policy and enforcement with mass incarceration of primarily individuals of color (Alexander, 2010; Travis et al., 2014). In education, however, a parallel cause has contributed to the expansion of the correctional system in the United States. Increasingly punitive discipline policies marked by zero tolerance approaches have created a pipeline from schools to prisons where exclusion from the educational environment and criminalization of student misbehavior contribute to school dropout and involvement with the juvenile justice system (Fowler, 2011).

The effects of this school-to-prison pipeline have been particularly detrimental for students of color, who are disproportionately suspended, expelled or otherwise excluded from the academic setting. Starting in preschool, Black children are suspended at a higher rate than their White counterparts (USDOE, 2014). Whereas 5% of White students are suspended, three times as many Black students are suspended on average (USDOE, 2014). Additionally, American Indian and Native-Alaskan students, who are less than 1% of the population in American schools, account for 2% of out-of-school suspensions and 3% of expulsions. Both gender and disability intersect with race and ethnicity, resulting in disproportionate suspensions of boys and girls of color and students with disabilities (USDOE, 2014). Among students with disabilities, those with emotional-behavioral disorders are most likely to experience academic exclusion and to experience such exclusion multiple times (Bowman-Perrott et al., 2011). Double minority status can increase the likelihood of exclusion, such as with Black males who are consistently over-identified in special education (Artiles, Harry, Reschly, & Chinn, 2002; Bowman-Perrott et al., 2011; Ferri & Connor, 2005).

Similar disparities exist among the rates of arrests and referrals to law enforcement for Black students and students with disabilities. Although only 16% of the student population, Black students account for 31% of school-related arrests and 27% of referrals to law enforcement (USDOE, 2014). Similarly, students with disabilities, which comprise about 12% of the student population, represent 25% of students arrested or referred to law enforcement (USDOE, 2014). School-related arrests and referrals to law enforcement can place students at risk for future involvement with the juvenile justice system and ultimately prison. Carmichael, Whitten, and Voloudakis’s (2005) investigation of minority overrepresentation in the juvenile justice system of Texas indicated that students with a disciplinary history were more likely to be involved with juvenile justice. Although this was the case for youth in all categories of race and ethnicity, both Latino and Black youth had more frequent contact with the justice system than White youth (Carmichael et al., 2005). Demonstrating the cumulative effect of involvement with the juvenile system, Natsuaki, Ge, and Wenk’s (2008) longitudinal study of young male offenders identified age of first arrest as an indicator of criminal trajectory with a younger age producing a steeper cumulative trajectory. Additionally, for those first arrested early during their adolescent years, the pace at which they committed criminal offenses was not slowed by completion of high school (Natsuaki et al., 2008). Hence, when school discipline policies result in the exclusion of students from the educational setting and involvement with law enforcement, students are likely to be involved with the justice system as juveniles and adults (Natsuaki et al., 2008; USDOE, 2014; Wiesner, Kim, & Capaldi, 2010).

The American School Counselor Association National Model

ASCA developed a National Model (2012a) in order to provide school counselors with clear guidelines on how to meet the needs of all students. The ASCA National Model boasts a comprehensive, data-driven approach to meeting the needs of students and focuses on addressing students’ academic, personal, social and career needs. The model is driven by a key question: “How are students different as a result of what school counselors do?” Considering the data presented on the school-to-prison pipeline, this question is significant in ensuring that school counselors are providing students of color with the necessary support systems in order to foster more positive academic and social outcomes.

The National Model highlighted a collaborative approach centered on incorporating the efforts of teachers, administrators, families and other stakeholders in developing a comprehensive school counseling program. With school counselors at the helm, the model provided a new vision for the profession and emphasized school counselor accountability, leadership, advocacy, collaboration and systemic change (ASCA, 2012a). That is, the focus shifted to elevating the function of the school counseling program to align more readily with the mission of the school at large.

As a result of this new vision, school counseling programs have been able to observe significant improvements in students’ academic as well as social performance. For instance, L. Palmer and Erford (2012) found increases in high school attendance and graduation trends as the school counseling program implementation was increased. L. Palmer and Erford also reported positive changes in the academic performance of high school students, particularly improvements on Maryland State Assessment English and algebra scores. These results suggested optimistic influences of utilizing a comprehensive school counseling program as promoted by the National Model. Similarly, Carey and Dimmitt (2012) reported positive associations between the delivery of the comprehensive school counseling program and student performance; most specifically, rates of student suspensions and other disciplinary actions decreased, attendance increased, and math and reading proficiency improved. Dimmit and Wilkerson (2012) found that minority students were less likely to have access to comprehensive school counseling programs in their schools but noted correlations between an increase in counseling services and improved attendance, a decrease in suspensions, and a drop in reports of bullying. Similarly, Lapan, Whitcomb, and Aleman (2012) noted that schools with low counselor-to-student ratios and fully implemented ASCA Model programming had lower rates of suspension and fewer discipline issues.

Although much has been written on the benefits of school counselors addressing academic, personal, social and career development of students, there appears to be a paucity of research studies focused on the topic of college and career readiness of students of color. In terms of recommendations for school counselors and career development, Mayes and Hines (2014) discussed the need for more culturally sensitive and gendered approaches to college and career readiness for gifted Black females, including assisting these students in navigating through systemic and even social challenges that they may face. Similarly, Belser (2015) highlighted the impact that the school-to-prison pipeline has on career opportunities later in life for adolescent males of color. Considering the challenges that students face, especially those from marginalized populations, as well as the significant benefits of data-driven comprehensive school counseling programs, it seems appropriate that school counselors utilize the National Model as the foundation for stimulating more positive student outcomes.

Multi-Tiered System of Supports (MTSS)

Initially framed as Response to Intervention (RTI), the implementation of MTSS resulted from federal education initiatives after the 2004 reauthorization of the Individuals with Disabilities Education Improvement Act (IDEA), which called for more alignment between this policy and the No Child Left Behind Act (NCLB) of 2001 (Sugai & Horner, 2009). MTSS programs in schools are designed to provide a more systematic, data-driven and equitable approach to solving academic and behavioral issues with students. Within such programs, students are divided into three tiered categories based on the level of risk and need: (a) Tier 1 represents students who are in the general education population and who are thriving, (b) Tier 2 represents students who need slightly more intensive intervention that can be delivered both individually or in a small group setting, and (c) Tier 3 represents students who need intensive individualized interventions (Ockerman, Mason, & Hollenbeck, 2012). The process involves universal screening or testing, intervention implementation and progress monitoring.

To combat problem behaviors, MTSS is often linked to Positive Behavioral Interventions and Supports (PBIS) as an additional source of support for students. These programs have shown to reduce office disciplinary referrals and increase attendance (Freeman et al., 2016). Moreover, Horner, Sugai, and Anderson (2010) determined that PBIS programs are associated with reductions in problem behaviors, improved perception of school safety and improved academic results. Banks and Obiakor (2015) provided strategies for implementing culturally responsive positive behavior supports in schools, noting that doing so can reduce the marginalization of minority students and foster a safe and supportive school climate. With outcomes such as these, PBIS and MTSS programs have become known as best practices (Horner et al., 2010).

Several authors have noted the overlapping elements of MTSS and the ASCA National Model (ASCA, 2012a; Martens & Andreen, 2013; Ockerman et al., 2012). As both frameworks have yielded positive outcomes with the general population and minority students, it would appear that a coordinated approach would be beneficial for schools. However, existing discussions of how to integrate the two have not been comprehensive in their discussion or have not addressed the potential impact on students of color. In this manuscript, the authors have sought to provide a solution to this problem.

Putting MTSS and Comprehensive School Counseling Programs Into Practice

Integrating the ASCA National Model with MTSS involves strategic data-driven planning and decision making. The process begins with collecting baseline data on students via screening scales and surveys and then analyzing this data to group students into tiers based on indicated level of risk. A more objective approach driven by data could especially benefit students of color, who have historically been subject to disproportionate and—at times—unfair discipline policies (Hoffman, 2012). Once students have been placed in one of three MTSS tier groups, the decision-making team and school counselors can generate appropriate prevention and intervention strategies that fit with each tier and with students’ needs. The process is cyclical, as progress-monitoring data is collected periodically to determine future steps. Figure 1 outlines the process from start to finish, and the sections that follow will further highlight the phases of the process. In addition, the authors will address how these steps can affect students of color.

 

Figure 1. The MTSS Cycle for Behavior Intervention

Team Development and Planning

     The process of providing MTSS services is not a job for a single person; rather, a team of stakeholders (e.g., school counselors, administrators, teachers) must be involved in planning, enacting and evaluating the services and interventions utilized. With the integration of the ASCA National Model within MTSS, school counselors can utilize elements of the model, such as the Advisory Council and the Annual Agreement, to aid in the planning process (ASCA, 2012a). Each member of the team provides a unique role, from direct service delivery to data management. School counselors should be mindful of their numerous other duties within the school and only take the lead on program components that are appropriate and directly relate to the role of school counselors in schools (ASCA, 2014; Ockerman et al., 2012).

In the planning phase, the team should examine preliminary discipline-related data to gauge what types of universal supports might be necessary; within this conversation, understanding the school’s demographic data is crucial so the team can account for potential culture-bound concerns that may need to be addressed during the MTSS process. Additionally, the team should determine what instrument will be used for universal screening, a process that will be discussed in more detail in the next section. Once the team has a preliminary plan of action, including a timeline of key events, this information should be presented to the entire school faculty to provide a rationale for the services and procedural information to boost fidelity of implementation, especially with program elements implemented schoolwide like universal screening.

Universal Screening

Data collection through universal assessment is a necessary step to the MTSS process (Harn, Basaraba, Chard, & Fritz, 2015; von der Embse, Pendergast, Kilgus, & Eklund, 2015). School counselors often rely on referrals from teachers, parents and students to match students with interventions; however, integrating a universal screening approach to comprehensive school counseling programs can help mitigate students falling through the cracks (Ockerman et al., 2012). Universal screening involves all students being evaluated using one instrument, such as the Student Risk Screening Scale (SRSS; Drummond, 1994), which allows a decision-making team to categorize students based on level of risk for the respective issue. Cheney and Yong (2014) noted that a universal screening instrument should be time efficient for teachers to complete and should be both valid and reliable; they further noted that the purpose of such a screening tool is to identify which students warrant interventions beyond Tier 1 supports (i.e., Tier 2 and 3 interventions).

Various instruments exist for universal screening of behavior or emotional risk (Lane, Kalberg, et al., 2011). The SRSS (Drummond, 1994) is one freely available screening instrument that allows teachers to rate an entire class of students quickly on seven behavioral or social subscales. This tool fits well into an MTSS framework as the scoring places students into a category of low, moderate, or high levels of risk (Lane et al., 2015); in addition, researchers have established validity and reliability for the SRSS at the elementary (Lane et al., 2012), middle (Lane, Oakes, Carter, Lambert, & Jenkins, 2013), and high school levels (Lane, Oakes, et al., 2011), as well as in urban elementary schools (Ennis, Lane, & Oakes, 2012). Other universal screening instruments that support the MTSS framework for behavior-related concerns include the Behavioral and Emotional Screening System (BESS; Kamphaus & Reynolds, 2007), the Systematic Screening for Behavioral Disorders (SSBD; Walker & Severson, 1992), and the Social, Academic, and Emotional Behavioral Risk Screener (SAEBRS; von der Embse et al., 2015).

Procedurally, the process of conducting a universal screening at a school would need to be driven by a collaborative faculty team with heavy administrative support. Carter, Carter, Johnson, and Pool (2012) described steps that educators took at one school to identify students for Tier 2 and 3 interventions and beyond. Within their process, faculty members would complete the screening instrument on a class of students whom they see regularly (e.g., a homeroom class). Ideally, multiple faculty members would complete the instrument on a single class to provide multiple data points on each student as a means of reducing teacher bias; in such an instance, the scores could be averaged together. Once the screening process is complete, the MTSS team (or whatever team has been assembled for this purpose) can view the compiled data to identify at-risk students. The faculty team can then sort and view this data easily by students’ scores on the instrument to reveal which students are most at risk based on the assessment. The final step in this process is to place students within one of the three MTSS tiers based on the results of the universal screening instrument. After this process is complete, the school counselors and the team can design interventions for students at each level. The faculty team may find it useful to consult other school discipline data points (e.g., office disciplinary referrals and suspensions) as additional baseline measures for students identified as needing Tier 2 or Tier 3 interventions. However, the team should keep in mind that these disciplinary actions have historically been applied to students of color, particularly Black males, at a disproportionate rate; thus, these data points may not be in line with the goal of using a more objective measurement strategy (Hoffman, 2012).

Tiering and Intervention

Whereas school counselors can be an integral part of the universal screening process, they can also be a driving force with direct service delivery for students at all three MTSS tiers (Ockerman et al., 2012). The ASCA National Model (2012a) highlighted the overlapping nature of the model’s direct student services component to the three tiers of the MTSS model. The following sections will highlight the connections between the three MTSS tiers and the levels of service delivery within comprehensive school counseling programs; moreover, the authors will convey strategies and interventions that may be especially helpful for students of color facing social and behavioral concerns.

Tier 1. Tier 1 instruction or intervention takes place in the general education environment and is presented universally to students (Harn et al., 2015). Two programs commonly used at this level are PBIS and Social-Emotional Learning (Cook et al., 2015). However, Ockerman et al. (2012) noted that some elements of comprehensive school counseling programs (e.g., schoolwide interventions, large group interventions and the counseling core curriculum) fall within the first tier, as they are designed to target all or most students. For example, school counselors can partner with administrators and teachers to develop or adopt a data-driven PBIS program that integrates classroom lessons (e.g., character education) and schoolwide programming (e.g., an anti-bullying rally or positive behavior reward events). Additionally, school counselors can align their counseling curriculum with the goals of the MTSS or PBIS program and create lessons or units that support these goals. Potential topics for these lessons or units include social skills, conflict resolution, respecting diversity and differences in others, and managing one’s anger. School counselors can gather needs assessment data from students, teachers, parents and other stakeholders to determine which topics may be of most benefit to students. Tier 1 interventions are designed to effectively serve approximately 80–85% of students (Martens & Andreen, 2013).

Tier 2. Tier 2 interventions are enacted for students whose needs are not being met by Tier 1 services and may include a variety of interventions such as the following: (a) targeted interventions, (b) group interventions, and (c) individualized interventions for less problematic behaviors (Newcomer, Freeman, & Barrett, 2013). School counselors may be involved with any or all of these types of interventions but are more likely to provide direct services to students through small group interventions and individualized interventions for minor problem behaviors. The MTSS decision-making team should evaluate data from the universal screening process to determine which students may need a Tier 2 support and what type of intervention that should be. For example, after the first author compiled data from the SRSS at his middle school, he and his team evaluated the scores of students who fell in the moderate risk range to determine what interventions (e.g., small group counseling, behavior contract, Check-in/Check-out) would be appropriate for each student. Unlike Tier 1 supports, Tier 2 interventions should not be one-size-fits-all, but driven by the needs of each unique student.

Small group counseling. As students of color have been subject to disproportionate use of exclusionary disciplinary actions (e.g., in-school or out-of-school suspensions), school counselors and the decision-making team should utilize Tier 2 interventions that promote alternatives to suspension and help re-engage students with prosocial behaviors. Group counseling interventions can be more psychoeducational in nature (e.g., anger management, social skills development, conflict resolution, problem solving) or can be geared more toward personal growth and exploration of students’ feelings and concerns about everyday problems (Gladding, 2016). Regardless of the type of group, school counselors should foster an environment where students can openly express themselves and simultaneously work on an individual goal. Safety, trust and universality within the group may be especially helpful for marginalized students, as they can often feel disenfranchised from the school environment because of exclusionary discipline practices (Caton, 2012; Gladding, 2016).

Individualized interventions. Some students are not appropriate for counseling groups or their presenting issues do not warrant a group intervention. For these students, an individual approach to Tier 2 interventions is necessary. Two commonly used strategies are Check-in/Check-out and behavior contracts. Check-in/Check-out is a structured method for providing students with feedback regarding their behavior with higher frequency (Crone, Hawken, & Horner, 2010). With this strategy, students “check-in” with a designated faculty member in the morning as a source of encouragement and non-contingent attention, receive a behavior report card that is carried with them throughout their day for teachers to record feedback, and “check-out” with the same faculty member at the end of the day to evaluate progress and possibly receive a reward. The report card can then be taken home to parents as a form of home–school collaboration (Maggin, Zurheide, Pickett, & Baillie, 2015). Check-in/check-out has been shown to be an intervention that successfully prevents escalation of student behavior and reduces disciplinary referrals (Maggin et al., 2015; Martens & Andreen, 2013). Moreover, it also helps students build a positive relationship with school staff members.

Behavior contracts have a similar approach but also take the form of a less intensive behavior intervention plan (BIP). With both approaches, the report card or behavior tracking form should be modified based on the developmental and behavioral needs of the student. The first author utilized an approach that integrated both of these interventions, and each identified student was matched with an adult with whom they had a trusting relationship who acted as their designated check-in/check-out person. Students receiving an individual intervention also may benefit from small group counseling as an additional support. If Tier 2 interventions are unsuccessful in mitigating students’ problem behaviors, the team’s attention should shift to Tier 3 interventions.

Tier 3. Tier 3 interventions are appropriate for students identified as highly at risk by the universal screening and students who have not responded positively to Tier 2 interventions. As with Tier 2 interventions, school counselors’ roles with Tier 3 interventions may vary, ranging from a supporting or consultative role to directly delivering interventions. Counseling interventions at this level include individual counseling, one-on-one mentoring, or referrals to community agencies for more intensive services (Ockerman et al., 2012). School counselors should keep in mind that ASCA has identified providing long-term individual counseling as an inappropriate role for school counselors (ASCA, 2012a) due to time constraints and lack of resources. As such, referrals to community agencies may be most helpful in supporting students in need of more intensive one-on-one counseling services.

Behavior intervention plans are another Tier 3 strategy to mitigate more severe problem behaviors (Bohanon, McIntosh, & Goodman, 2015). Lo and Cartledge (2006) found that conducting functional behavioral assessments (FBAs) and creating BIPs was a successful intervention for reducing problem behaviors and increasing replacement behaviors in elementary-aged Black males. Whether through counseling intervention or intensive behavior support, structured Tier 3 interventions can provide alternatives to suspensions, which is especially helpful for students of color as previously discussed.

Progress Monitoring

The MTSS process does not end with universal screening or service delivery; the decision-making team must have a clear and systematic plan for monitoring student outcomes. Carter et al. (2012) recommended administering the universal screening tool at least twice during the school year to evaluate progress. By taking such action, the decision-making team can determine which students are responding well to interventions and which students are not. Those students responding well to Tier 2 or 3 interventions may be moved down to Tier 1, whereas those not responding well to Tier 1 or 2 may be moved up a tier. Students not responding to Tier 3 interventions may warrant additional behavioral or psychological assessment to determine if further services are more appropriate (Ockerman et al., 2012). Progress monitoring also can provide clues about the efficacy of an intervention or the fidelity of its implementation. For example, if only one student in a class is responding to a Tier 1 intervention, the team may want to evaluate the delivery of that intervention for that class or consider an alternative intervention. A primary benefit of utilizing a data-driven progress monitoring approach is that it allows for objective decision making based on data, rather than subjective decision making that may be influenced by bias.

Implications for School Counselors

In line with the ASCA National Model (2012a), school counselors are called to be advocates and agents of systemic change in their schools. Part of this calling includes implementing comprehensive school counseling programs that address inequities within the school and provide programming to address the achievement gap. As has been discussed previously, integrating MTSS and the National Model can be especially helpful for students of color who have historically been subject to bias within discipline policies and procedures, resulting in disproportionate rates of disciplinary action. School counselors acting as advocates and agents of change should be proactive in analyzing school data to determine whether these inequities are at play and must be vocal about the need to solve these problems if they do exist at their schools (ASCA, 2012b).

As such, school counselors should ensure that they are versed in best practices such as MTSS that have been shown to positively impact racial and cultural inequities. However, school counselors cannot solve the problem alone. The other two themes of the ASCA National Model (2012a)—leadership, and collaboration and teaming—are also critically important if school counselors are to implement such programs. With training in data analysis, program development and direct service implementation, school counselors are uniquely positioned to take on leadership roles with regard to MTSS programming. However, they also should recognize their roles as collaborators and team members for program elements that do not directly fall within the role of school counselors (Ockerman et al., 2012).

Implications for Counselor Educators and Researchers

As stakeholders charged with training the next generation of school counselors, counselor educators must remain versed in newer topics within school counseling and education. Although PBIS has been around since 1997, MTSS is still a relatively new concept, especially when integrated with the ASCA National Model. School counselor educators should ensure that coursework prepares future school counselors to engage in such programming. More specifically, school counselor preparation courses should include discussion and application of MTSS, data analysis, program evaluation, behavior interventions and other concepts that are vital to coordinating ASCA Model programming. At the same time, counselor educators also must empower graduate students to become advocates for marginalized students at their future schools and for themselves as professionals. Because there is little research available that evaluates the integration of MTSS and ASCA Model programming, it is imperative that school counselors and counselor educators collaborate to conduct such research.

Conclusion

Research on the school-to-prison pipeline has demonstrated an unfortunate link between the criminal justice system and K–12 disproportionate disciplinary practices faced by students of color. An integrated system including a multi-tiered system of supports and the ASCA (2012a) National Model has been introduced in this manuscript to address disciplinary concerns in a more systemically balanced manner. MTSS and the ASCA National Model utilize a similar data-driven structured approach to solving issues related to academic and behavioral concerns. When integrated, the overlapping elements of each framework can provide an avenue for addressing key concerns for students of color exhibiting problem behaviors. Rather than relying on disciplinary procedures that may result in students being excluded from class, an approach integrating frameworks of prevention and intervention can provide a much-needed alternative. The framework provided herein details steps that school counselors and other educators can take to address the school-to-prison pipeline. In order to best support marginalized students, school counselors must heed the call to leadership, advocacy, collaboration and systemic change given by the National Model; moreover, joining forces with other educators through collaborative efforts such as MTSS can only strengthen the effort to best support the success of all students.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest or funding contributions for the development of this manuscript.

References

Alexander, M. (2010). The new Jim Crow: Mass incarceration in the age of colorblindness. New York, NY: The New Press.

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

American School Counselor Association. (2012b). ASCA school counselor competencies. Retrieved from https://www.schoolcounselor.org/asca/media/asca/home/SCCompetencies.pdf

American School Counselor Association. (2014). The school counselor and multitiered system of supports. Retrieved from http://www.schoolcounselor.org/asca/media/asca/PositionStatements/
PS_MultitieredSupportSystem.pdf

Artiles, A. J., Harry, B., Reschly, D. J., & Chinn, P. C. (2002). Over-identification of students of color in special education: A critical overview. Multicultural Perspectives, 4, 3–10. doi:10.1207/s15327892mcp0401_2

Banks, T., & Obiakor, F. E. (2015). Culturally responsive positive behavior supports: Considerations for practice. Journal of Education and Training Studies, 3(2), 83–90. doi:10.11114/jets.v3i2.636

Belser, C. T. (2015). African American males: A career and college readiness crisis. In J. R. Curry & M. A. Shillingford (Eds), African American students’ career and college readiness: The journey unraveled (pp. 279–307). Washington, DC: Lexington Books.

Bernstein, N. (2014). Burning down the house: The end of juvenile prison. New York, NY: The New Press.

Bohanon, H., McIntosh, K., & Goodman, S. (2015). Integrating academic and behavior supports within an RtI framework, part 4: Tertiary supports. Retrieved from http://www.rtinetwork.org/learn/behavior-supports/integrating-academic-and-behavior-supports-tertiary-supports

Bowman-Perrott, L., Benz, M. R., Hsu, H.-Y., Kwok, O.-M., Eisterhold, L. A., & Zhang, D. (2011). Patterns and predictors of disciplinary exclusion over time: An analysis of the SEELS national data set. Journal of Emotional and Behavioral Disorders, 21(2), 83–96. doi:10.1177/1063426611407501

Carey, J., & Dimmitt, C. (2012). School counseling and student outcomes: Summary of six statewide studies. Professional School Counseling, 16, 146–153. doi:10.5330/PSC.n.2012-16.146

Carmichael, D., Whitten, G., & Voloudakis, M. (2005). Study of minority over-representation in the Texas juvenile justice system: Final report. College Station, TX: Public Policy Research Institute at Texas A&M University.

Carson, E. A. (2015). Prisoners in 2014. Retrieved from http://www.bjs.gov/index.cfm?ty=pbdetail
&iid=5387

Carter, D. R., Carter, G. M., Johnson, E. S., & Pool, J. L. (2012). Systematic implementation of a Tier 2

behavior intervention. Intervention in School and Clinic, 48, 223–231. doi:10.1177/1053451212462879

Caton, M. T. (2012). Black male perspectives on their educational experiences in high school. Urban Education, 47, 1055–1085. doi:10.1177/0042085912454442

Cheney, D. A., & Yong, M. (2014). RE-AIM checklist for integrating and sustaining Tier 2 social-behavioral interventions. Intervention in School and Clinic, 50, 39–44. doi:10.1177/1053451214532343

Cook, C. R., Frye, M., Slemrod, T., Lyon, A. R., Renshaw, T. L., & Zhang, Y. (2015). An integrated approach to universal prevention: Independent and combined effects of PBIS and SEL on youths’ mental health. School Psychology Quarterly, 30, 166–183. doi:10.1037/spq0000102

Crone, D. A., Hawken, L. S., & Horner, R. H. (2010). Responding to problem behavior in schools: The behavior education program (2nd ed.). New York, NY: Guilford Press.

Delpit, L. (2006). Other people’s children: Cultural conflict in the classroom. New York, NY: Norton.

Dimmitt, C., & Wilkerson, B. (2012). Comprehensive school counseling in Rhode Island: Access to services and student outcomes. Professional School Counseling, 16, 125–135. doi:10.5330/PSC.n.2012-16.125

Drummond, T. (1994). The Student Risk Screening Scale (SRSS). Grants Pass, OR: Josephine County Mental Health Program.

Ennis, R. P., Lane, K. L., & Oakes, W. P. (2012). Score reliability and validity of the student risk screening scale: A psychometrically sound, feasible tool for use in urban elementary schools. Journal of Emotional and Behavioral Disorders, 20, 241–259. doi:10.1177/1063426611400082

Ferri, B. A., & Connor, D. J. (2005). In the shadow of Brown: Special education and overrepre-sentation of students of color. Remedial and Special Education, 26, 93–100. doi:10.1177/07419325050260020401

Ford, D. Y., & Moore, J. L., III. (2013). Understanding and reversing underachievement, low achievement, and achievement gaps among high-ability African American males in urban school contexts. Urban Review, 45, 399–415. doi:10.1007/s11256-013-0256-3

Fowler, D. (2011). School discipline feeds the pipeline to prison. Phi Delta Kappan, 93(2), 14–19. doi:10.1177/003172171109300204

Freeman, J., Simonsen, B., McCoach, D. B., Sugai, G., Lombardi, A., & Horner, R. (2016). Relationship between school-wide positive behavior interventions and supports and academic, attendance, and behavior outcomes in high schools. Journal of Positive Behavior Intervention, 18, 41–51. doi:10.1177/1098300715580992

Gladding, S. (2016). Groups: A counseling specialty (7th ed.). Upper Saddle River, NJ: Prentice-Hall.

Harn, B., Basaraba, D., Chard, D., & Fritz, R. (2015). The impact of schoolwide prevention efforts: Lessons learned from implementing independent academic and behavior support systems. Learning Disabilities: A Contemporary Journal, 13, 3–20. doi:10.1177/0022219407313588

Hoffman, S. (2012). Zero benefit: Estimating the effect of zero tolerance discipline policies on racial disparities in school discipline. Education Policy, 28, 69–95. doi:10.1177/0895904812453999

Horner, R. H., Sugai, G. M., & Anderson, C. M. (2010). Examining the evidence base for school-wide positive behavior support. Focus on Exceptional Children, 42(8), 1–14.

Kaeble, D., Glaze, L. E., Tsoutis, A., & Minton, T. D. (2015). Correctional populations in the United States, 2014. Retrieved from http://www.bjs.gov/index.cfm?ty=pbdetail&iid=5519

Kamphaus, R. W., & Reynolds, C. R. (2007). BASC-2 Behavioral and emotional screening system (BASC-2 BESS). Minneapolis, MN: Pearson.

Lane, K. L., Kalberg, J. R., Menzies, H., Bruhn, A., Eisner, S., & Crnobori, M. (2011). Using systematic screening data to assess risk and identify students for targeted supports: Illustrations across the K-12 continuum. Remedial and Special Education, 32, 39–54. doi:10.1177/0741932510361263

Lane, K. L., Oakes, W. P., Carter, E. W., Lambert, W. E., & Jenkins, A. B. (2013). Initial evidence for the reliability and validity of the student risk screening scale for internalizing and externalizing behaviors at the middle school level. Assessment for Effective Intervention, 39, 24–38. doi:10.1177/1534508413489336

Lane, K. L., Oakes, W. P., Ennis, R. P., Cox, M. L., Schatschneider, C., & Lambert, W. (2011). Additional evidence for the reliability and validity of the student risk screening scale at the high school level: A replication and extension. Journal of Emotional and Behavioral Disorders, 21(2), 97–115. doi:10.1177/1063426611407339

Lane, K. L., Oakes, W. P., Harris, P. J., Menzies, H. M., Cox, M., & Lambert, W. (2012). Initial evidence for the reliability and validity of the student risk screening scale for internalizing and externalizing behaviors at the elementary level. Behavioral Disorders, 37, 99–122.

Lane, K. L., Oakes, W. P., Swogger, E. D., Schatschneider, C., Menzies, H. M., & Sanchez, J. (2015). Student risk screening scale for internalizing and externalizing behaviors: Preliminary cut scores to support data-informed decision making. Behavioral Disorders, 40, 159–170.

doi:10.17988/0198-7429-40.3.159

Lapan, R. T., Whitcomb, S. A., & Aleman, N. M. (2012). Connecticut professional school counselors: College and career counseling services and smaller ratios benefit students. Professional School Counseling, 16, 117–124. doi:10.5330/PSC.n.2012-16.124

Lo, Y.-Y., & Cartledge, G. (2006). FBA and BIP: Increasing the behavior adjustment of African American boys in schools. Behavioral Disorders, 31, 147–161.

Maggin, D. M., Zurheide, J., Pickett, K. C., & Baillie, S. J. (2015). A systematic evidence review of the check-in/check-out program for reducing student challenging behaviors. Journal of Positive Behavior Interventions, 17, 197–208. doi:10.1177/1098300715573630

Martens, K., & Andreen, K. (2013). School counselors’ involvement with a school-wide positive behavior support system: Addressing student behavior issues in a proactive and positive manner. Professional School Counseling, 16, 313–322. doi:10.5330/PSC.n.2013-16.313

Mayes, R. D., & Hines, E. M. (2014). College and career readiness for gifted African American girls: A call to school counselors. Interdisciplinary Journal of Teaching and Learning, 4, 31–42.

Moore, J. L., Henfield, M. S., & Owens, D. (2008). African American males in special education: Their attitudes and perceptions toward high school counselors and school counseling services. American Behavioral Scientist, 51, 907–927. doi:10.1177/0002764207311997

Natsuaki, M. N., Ge, X., & Wenk, E. (2008). Continuity and changes in the developmental trajectories of criminal career: Examining the roles of timing of first arrest and high school graduation. Journal of Youth and Adolescence, 37, 431–444. doi:10.1007/s10964-006-9156-0

Newcomer, L. L., Freeman, R., & Barrett, S. (2013). Essential systems for sustainable implementation of Tier 2 supports. Journal of Applied School Psychology, 29, 126–147. doi:10.1080/15377903.2013.778770

Ockerman, M. S., Mason, E. C. M., & Hollenbeck, A. F. (2012). Integrating RTI with school counseling programs: Being a proactive professional school counselor. Journal of School Counseling, 10(15), 1–37. Retrieved from http://files.eric.ed.gov/fulltext/EJ978870.pdf

Palmer, L. E., & Erford, B. T. (2012). Predicting student outcome measures using the ASCA National Model program audit. The Professional Counselor, 2, 152–159. doi:10.15241/lep.2.2.152

Palmer, R. T., & Maramba, D. C. (2010). African American male achievement: Using a tenet of Critical Theory to explain the African American male achievement disparity. Education and Urban Society, 43, 431–450. doi:10.1177/0013124510380715

Skiba, R. J. (2014). The failure of zero tolerance. Reclaiming Children and Youth, 22(4), 27–33.

Sugai, G., & Horner, R. H. (2009). Responsiveness-to-intervention and school-wide positive behavior supports: Integration of multi-tiered system approaches. Exceptionality, 17, 223–237. doi:10.1080/09362830903235375

Toldson, I. A., & Lewis, C. W. (2012). Challenge the status quo: Academic success among school-age African American males. Retrieved from http://www.cbcfinc.org/oUploadedFiles/CTSQ.pdf

Travis, J., Western, B., & Redburn, S.  (2014). The growth of incarceration in the United States: Exploring causes and consequences. Committee on Causes and Consequences of High Rates of Incarceration, Committee on Law and Justice, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academic Press.

U.S. Department of Education, Office of Civil Rights. (2014). Data snapshot: School discipline (Issue Brief No. 1). Retrieved from http://www2.ed.gov/about/offices/list/ocr/docs/crdc-discipline-snapshot.pdf

Von der Embse, N. P., Pendergast, L. L., Kilgus, S. P., & Eklund, K. R. (2015). Evaluating the applied use of a mental health screener: Structural validity of the social, academic, and emotional behavior risk screener. Psychological Assessment. Advance online publication. doi:10.1037/pas0000253

Walker, H. M., & Severson, H. H. (1992). Systematic Screening for Behavior Disorders (SSBD): User’s guide and administration manual. Longmont, CO: Sopris West.

Wiesner, M., Kim, H. K., & Capaldi, D. M. (2010). History of juvenile arrests and vocational career

outcomes for at-risk young men. Journal of Research in Crime & Delinquency, 47, 91–117. doi:10.1177/0022427809348908

Christopher T. Belser, NCC, is a doctoral candidate at the University of Central Florida. M. Ann Shillingford is an Associate Professor at the University of Central Florida. J. Richelle Joe, NCC, is an Assistant Professor at the University of Central Florida. Correspondence can be addressed to Christopher Belser, 231B Mathematical Sciences Building, University of Central Florida, Orlando, FL 32816, christopher.belser@ucf.edu.

Development of a Logic Model to Guide Evaluations of the ASCA National Model for School Counseling Programs

Ian Martin, John Carey

A logic model was developed based on an analysis of the 2012 American School Counselor Association (ASCA) National Model in order to provide direction for program evaluation initiatives. The logic model identified three outcomes (increased student achievement/gap reduction, increased school counseling program resources, and systemic change and school improvement), seven outputs (student change, parent involvement, teacher competence, school policies and processes, competence of the school counselors, improvements in the school counseling program, and administrator support), six major clusters of activities (direct services, indirect services, school counselor personnel evaluation, program management processes, program evaluation processes and program advocacy) and two inputs (foundational elements and program resources). The identification of these logic model components and linkages among these components was used to identify a number of necessary and important evaluation studies of the ASCA National Model.

 

Keywords: ASCA National Model, school counseling, logic model, program evaluation, evaluation studies

 

 

Since its initial publication in 2003, The ASCA National Model: A Framework for School Counseling Programs has had a dramatic impact on the practice of school counseling (American School Counselor Association [ASCA], 2003). Many states have revised their model of school counseling to make it consistent with this model (Martin, Carey, & DeCoster, 2009), and many schools across the country have implemented 3the ASCA National Model. The ASCA Web site, for example, currently lists over 400 schools from 33 states that have won a Recognized ASCA Model Program (RAMP) award since 2003 as recognition for exemplary implementation of the model (ASCA, 2013).

 

While the ASCA National Model has had a profound impact on the practice of school counseling, very few studies have been published that evaluate the model itself. Evaluation is necessary to determine if the implementation of the model results in the model’s anticipated benefits and to determine how the model can be improved. The key studies typically cited (see ASCA, 2005) as supporting the effectiveness of the ASCA National Model (e.g., Lapan, Gysbers, & Petroski, 2001; Lapan, Gysbers, & Sun, 1997) were actually conducted before the model was developed and were designed as evaluations of Comprehensive Developmental Guidance, which is an important precursor and component of the ASCA National Model, but not the model itself.

 

Two recent statewide evaluations of school counseling programs focused on the relationships between the level of implementation of the ASCA National Model and student outcomes. In a statewide evaluation of school counseling programs in Nebraska, Carey, Harrington, Martin, and Hoffman (2012) found that the extent to which a school counseling program had a well-implemented, differentiated delivery system consistent with practices advocated by the ASCA National Model was associated with lower suspension rates, lower discipline incident rates, higher attendance rates, higher math proficiency and higher reading proficiency. These results suggest that model implementation is associated with increased student engagement, fewer disciplinary problems and higher student achievement. In a similar statewide evaluation study in Utah, Carey, Harrington, Martin, and Stevens (2012) found that the extent to which the school counseling program had a programmatic orientation, similar to that advocated in the ASCA National Model, was associated with both higher average ACT scores and a higher number of students taking the ACT. This suggests that model implementation is associated with both increased achievement and a broadening of student interest in college. While these studies suggest that benefits to students are associated with the implementation of the ASCA National Model, additional evaluations are necessary that use stronger (e.g., quasi-experimental and longitudinal) designs and investigate specific components of the model in order to determine their effectiveness or how they can be improved.

 

There are several possible reasons why the ASCA National Model has not been evaluated extensively. The school counseling field as a whole has struggled with general evaluation issues. For example, questions have been raised regarding the effectiveness of practitioner training in evaluation (Astramovich, Coker, & Hoskins, 2005; Heppner, Kivlighan, & Wampold, 1999; Sexton, Whiston, Bleuer, & Walz, 1997; Trevisan, 2000); practitioners have cited lack of time, evaluation resources and administrative support as major barriers to evaluation (Loesch, 2001; Lusky & Hayes, 2001); and some practitioners have feared that poor evaluation results may negatively impact their program credibility (Isaacs, 2003; Schmidt, 1995). Another contributing factor is that while the importance of evaluation is stressed in the literature, few actual examples of program evaluations and program evaluation results have been published (Astramovich & Coker, 2007; Martin & Carey, 2012; Martin et al., 2009; Trevisan, 2002).

 

In addition, there are several features of the ASCA National Model that make evaluations difficult. First, the model is complex, containing many components grouped into four interrelated, functional subsystems referred to as the foundation, delivery system, management system and accountability system. Second, ASCA created the National Model by combining elements of existing models that were developed by different individuals and groups. For example, the principle influences of the model (ASCA, 2012) are cited as Gysbers and Henderson (2000), Johnson and Johnson (2001) and Myrick (2003). Furthermore, principles and concepts derived from important movements such as the Transforming School Counseling Initiative (Martin, 2002) and evidence-based school counseling (Dimmitt, Carey, & Hatch, 2007) also were incorporated into the model during its development. While these preexisting models and movements share some common features, they differ in important ways. Elements of these approaches were combined and incorporated into the ASCA National Model without a full integration of their philosophical and theoretical perspectives and principles. Consequently, the ASCA National Model does not reflect a single cohesive approach to program organization and management. Instead, it reflects a collection of presumably effective principles and practices that have been applied in school counseling programs. Third, instruments for measuring important aspects of model implementation are lacking (Clemens, Carey, & Harrington, 2010). Fourth, the theory of action of the ASCA National Model has not been fully explicated, so it is difficult to determine what specific benefits are intended to result from the implementation of specific elements of the model. For example, it is not entirely clear how changing the performance evaluation of counselors is related to the desired benefits of the model.

 

In this article, the authors present the results of their work in developing a logic model for the ASCA National Model. Logic modeling is a systematic approach to enabling high-quality program evaluation through processes designed to result in pictorial representations of the theory of action of a program (Frechtling, 2007). Logic modeling surfaces and summarizes the explicit and implicit logic of how a program operates to produce its desired benefits and results. By applying logic modeling to an analysis of the ASCA National Model, the authors intended to fully explicate the relationships between structures and activities advocated by the model and their anticipated benefits so that these relationships can be tested in future evaluations of the model.

 

The purpose of this study, therefore, was to develop a useful logic model that describes the workings of the ASCA National Model in order to promote its evaluation. More specifically, the purpose was to mine the logic elements, program outcomes and implicit (unstated) assumptions about the relationships between program elements and outcomes. In developing this logic model, the authors followed the processes suggested by the W. K. Kellogg Foundation (2004) and Frechtling (2007). Several different frameworks exist for logic models, but the authors elected to use Frechtling’s framework because it focuses specifically on promoting evaluation of an existing program (as opposed to other possible uses such as program planning). This framework identifies the relationships among program inputs, activities, outputs and outcomes. Inputs refer to the resources needed to deliver the program as intended. Activities refer to the actual program components that are expected to be related to a desired outcome. Outputs refer to the immediate products or results of activities that can be observed as evidence that the activity was actually completed. Outcomes refer to the desired benefits of the program that are expected to occur as a consequence of program activities. The authors’ logic model development was guided by four questions:

 

What are the essential desired outcomes of the ASCA National Model?

What are the essential activities of the ASCA National Model and how do these activities relate to its outputs?

What are the essential outputs of the ASCA National Model and how do these outputs relate to its desired outcomes?

What are the essential inputs of the ASCA National Model and how do these inputs relate to its activities?

 

Methods

All analyses in this study were based on the latest edition of the ASCA National Model (ASCA, 2012). In these analyses, every attempt was made to base inferences on the actual language of the model. In some instances (for example, when it was unclear which outputs were expected to be related to a given activity) the professional literature about the ASCA National Model was consulted.

Because the authors intended to develop a logic model from an existing program blueprint (rather than designing a new program), they began, according to recommended procedures (W. K. Kellogg Foundation, 2004), by first identifying outcomes and then working backward to identify activities, then outputs associated with activities and finally, inputs.

 

Identification of Outcomes

The authors independently reviewed the ASCA National Model (2012) and identified all elements in the model. The two authors’ lists of elements (e.g., vision statement, annual agreement with school leaders, indirect service delivery and curriculum results reports) were merged to create a common list of elements. The authors then independently created a series of if, then statements for each element of the model that traced the logical connections explicitly stated in the model (or in rare instances, stated in the professional literature about the model) between the element and a program outcome. In this way, both the desired outcomes of the ASCA National Model and the desired logical linkages between elements and outcomes were identified.

 

During this process, some ASCA National Model elements were included in the same logic sequence because they were causally related to each other. For example, both the vision statement and the mission statement were included in the same logic sequence because a strong vision statement was described as a necessary prerequisite for the development of a strong mission statement. Some ASCA National Model elements also were included in more than one logical sequence when it was clear that two different outcomes were intended to occur related to the same element. For example, it was evident that closing-the-gap reports were intended to result in intervention improvements, leading to better student outcomes and also to apprising key stakeholders of school counseling program results, in order to increase support and resources for the program.

 

Identification of Activities

Frechtling (2007) noted that the choice of the right amount of complexity in portraying the activities in a logic model is a critically important factor in a model’s utility. If activities are portrayed in their most differentiated form, the model can be too complex to be useful. If activities are portrayed in their most compact form, the model can lack enough detail to guide evaluation. Therefore, in the present study, the authors decided to construct several different logic models with different sets of activities that ranged from including all the previously identified ASCA National Model elements as activities to including only the four sections of the ASCA National Model (i.e., foundation, management system, delivery system and accountability system) as activities. As neither of the two extreme options proved to be feasible, the authors began clustering ASCA National Model elements and developed six activities, each of which represented a cluster of program elements.

 

Identification of Outputs Related to Activities

Outputs are the observable immediate products or deliverables of the logic model’s inputs and activities (Frechtling, 2007). After the authors identified an appropriate level for representing model activities, they generated the same level of program outputs. Reexamining the logic sequences, clustering products of identified activities and then creating general output categories from the clustered products accomplished this task. For example, the activity known as direct services contained several ASCA National Model products, such as the curriculum results report, the small-group results report and the closing-the-gap results report (among others), and the resulting output was finally categorized as student change. Ultimately, seven logic model outputs were identified through this process to help describe the outputs created by ASCA National Model activities.

 

Identifying the Connections Between Outputs and Outcomes

Creating connections between model outputs and outcomes was accomplished by linking the original logic sequences to determine how the ASCA National Model would conceive of outputs as being linked to outcomes. Returning to the above example, the output known as student change, which included such products as results reports, was connected to the outcome known as student achievement and gap reduction in several logic sequences. At the conclusion of this process, each output had straightforward links to one or multiple proposed model outcomes. Not only was this process useful in identifying links between outputs and outcomes, but it also functioned as an opportunity to test the output categories for conceptual clarity.

 

Identification of Inputs and Connections Between Inputs and Activities

The authors reviewed the ASCA National Model to determine which inputs were necessary to include in the logic model. They identified two essential types of inputs: foundational elements (conceptual underpinnings described in the foundation section of the ASCA National Model) and program resources (described throughout the ASCA National Model). The authors determined that these two types of inputs were necessary for the effective operation of all six activities.

 

Identifying Other Connections Within the Logic Model

     After the inputs, activities, outputs, outcomes and the connections between these levels were mapped, the authors again reviewed the logic sequences and the ASCA National Model to determine if any additional linkages needed to be included in the logic model (see Frechtling, 2007). They evaluated the need for within-level linkages (e.g., between two activities) and feedback loops (i.e., where a subsequent component influences the nature of preceding components). The authors determined that two within-level and one recursive linkage were needed.

 

Results

 

Outcomes

A total of 65 logic sequences were identified for the ASCA National Model sections: foundation (n = 7), management system (n = 30), delivery system (n = 7) and accountability system (n = 21). Table 1 contains sample logic sequences.

 

Table 1

 

Examples of Logic Sequences Relating ASCA National Model Elements to Outcomes

 

National Model

Section

Logic Sequence

Foundation a. If counselors go through the process of creating a set of shared beliefs, then they will establish a level of mutual understanding.b. If counselors establish a level of mutual understanding, then they will be more successful in developing a shared vision for the program.c. If counselors develop a shared vision for the program, then they can develop an effective vision statement.d. If counselors create a vision statement, then they will have the clarity of purpose that is needed to develop a mission statement.e. If counselors create a mission statement, then the program will be more focused.f. If the program is better focused, counselors will create a set of program goals, which will enable counselors to specify how the attainment of the goals should be measured.

g. If counselors specify how the attainment of goals should be measured, then effective program evaluation will be conducted.

h. If effective program evaluation is conducted, then the program will be continuously improved.

i. If the program will be continuously improved, then improved student achievement will result.

Management System a. If school counselors create annual agreements with the leader in charge of the school, then the goals and activities of the counseling program will be more aligned with the goals of the school.b. If the goals and activities of the counseling program are more aligned with the goals of the school, then school leaders will recognize the value of the school counseling program.c. If school leaders recognize the value of the school counseling program, then they will commit resources to support the program.
Delivery System a. If school counselors engage in indirect services (e.g., consultation and advocacy), then school policies and processes will improve.b. If school policies and processes improve, then teachers will develop more competency, and systemic change and school improvement will occur.
Accountability System a. If counselors complete curriculum results reports, then they will have the information they need to demonstrate the effectiveness of developmental and preventative curricular activities.b. If counselors have the information they need to demonstrate the effectiveness of developmental and preventative curricular activities, then they can communicate their impact to school leaders.c. If school leaders are aware of the impact of developmental and preventative curricular activities, then they will recognize their value.d. If school leaders recognize the value of developmental and preventative curricular activities, then they will commit resources to support them.

 

 

 

Forty of these logic sequences terminated with an outcome related to increased student achievement or (relatedly) to a reduction in the achievement gap. Twenty-two sequences terminated with an outcome related to an increase in program resources. Only three sequences terminated with an outcome related to systemic change in the school. From this analysis, the authors concluded that the primary desired outcomes of the ASCA National Model are increased student achievement/gap reduction and increased school counseling program resources. They also concluded that systemic change and school improvement is another desired outcome of the ASCA National Model.

 

Activities

Based on a clustering of ASCA National Model elements identified previously, six activities were developed for the logic model. These activities included the following: direct services, indirect services, school counselor personnel evaluation, program management processes, program evaluation processes and program advocacy processes. Each of these activities represents a cluster of elements within the ASCA National Model. For example, the activity known as direct services includes the school counseling core curriculum, individual student planning and responsive services. Consequently, the direct services activity represents the spectrum of services that would be delivered to students in an ASCA National Model school counseling program.

 

Activities Related to Outputs

Based on the clustering of the ASCA National Model products or deliverables around the related logic model activities, seven outputs were identified. These outputs included the following: student change, parent involvement, teacher competence, school policies and processes, school counselor competence, school counseling program improvements, and administrator support. The outputs represent all of the ASCA National Model products generated by model activities and help to collect evidence and determine to what degree an activity was successfully accomplished. In essence, for evaluation purposes, these outputs represent the intermediate outcomes (Dimmitt et al., 2007) of an ASCA National Model program. Activities should result in measurable changes in outputs, which in turn should result in measurable changes in outcomes. For example, the output known as student change reflects student changes such as increased academic motivation, increased problem-solving skills, enhanced emotional regulation and better interpersonal problem-solving skills; these changes lead to the longer-term outcome of student achievement and gap reduction.

 

Connections Between Outputs and Outcomes

Connecting the seven ASCA National Model outputs to its outcomes strengthens the logic model by identifying the hypothesized relationships between the more immediate changes that result from school counseling program activities (i.e., outputs) and the more distal changes that result from the operation of the program (i.e., outcomes). As described earlier, two primary outcomes (student achievement and gap reduction and increased program resources) and one secondary outcome (systemic change and school improvement) were identified within the ASCA National Model. Three of the seven outputs (student change, parent involvement and administrator support) were connected to only one outcome. Three other outputs (teacher competence, school policies and processes, and school counselor competence) were connected to two outcomes. One output (administrator support) was connected to all three outcomes. Interpreting these linkages is useful in understanding the implicit theory of change of the ASCA National Model and consequently in designing appropriate evaluation studies. The authors’ logic model, for example, indicates that student changes (related to both direct and indirect services of an ASCA National Model program) are expected to result in measurable increases in student achievement and a reduction in the achievement gap.

 

It also is helpful to scan backward in the logic model to identify how changes in outcomes are expected to occur. For example, student achievement and gap reduction is linked to six model outputs (student change, parent involvement, teacher competence, school policies and processes, school counselor competence, and school counseling program improvements). Student achievement and gap reduction is multiply determined and is the major focus of the ASCA National Model. Increased program resources are connected to three model outputs (school counselor competence, school counseling program improvements and administrator support). Systemic change and school improvement also can be connected to three outputs (teacher competence, school policies and processes, and school counseling program improvements).

 

Inputs and Connections Between Inputs and Activities

Based on an analysis of the ASCA National Model, two inputs were identified for inclusion in the logic model: foundational elements (which include the elements in the ASCA National Model’s foundation section considered important for program planning and operation) and program resources (which include elements essential for effective program implementation such as counselor caseload, counselor expertise, counselor professional development support, counselor time-use and program budget). Both of these inputs were identified as being important in the delivery of all six activities.

 

Additional Connections Within the Logic Model

Based on a final review of the logical sequences and another review of the ASCA National Model, three additional linkages were added to the authors’ logic model. The first linkage was a unidirectional arrow leading from management processes to program evaluation in the activities column. This arrow was intended to represent the tight connection between management processes and evaluation activities that is evident in the ASCA National Model. Relatedly, a unidirectional arrow leading from the school counseling program evaluation activity to the program advocacy activity was added. This arrow was intended to represent the many instances of the ASCA National Model suggesting that program evaluation activities should be used to generate essential information for program advocacy. The final additional link was a recursive arrow leading from the increased program resources outcome to the program resources input. This linkage was intended to represent the ASCA National Model’s concept that investment of additional resources resulting from successful implementation and operation of an ASCA National Model program will result in even higher levels of program effectiveness and eventually even better outcomes.

 

The Logic Model

Figure 1 contains the final logic model for the ASCA National Model for School Counseling Programs. Logic models portray the implicit theory of change underlying a program and consequently facilitate the evaluation of the program (Frechtling, 2007). Overall, the theory of change for an ASCA national program could be described as follows: If school counselors use the foundational elements of the ASCA National Model and have sufficient program resources, they will be able to develop and implement a comprehensive program characterized by activities related to direct services, indirect services, school counselor personnel evaluation, management processes, program evaluation and (relatedly) program advocacy. If these activities are put in place, several outputs will be observed, including the following: student changes in academic behavior, increased parent involvement, increases in teacher competence in working with students, better school policies and processes, increased competence of the school counselors themselves, demonstrable improvements in the school counseling program, and increased administrator support for the school counseling program. If these outputs occur, then the following outcomes should result: increased student achievement and a related reduction in the achievement gap, notable systemic improvement in the school in which the program is being implemented, and increased program support and resources. If these additional resources are reinvested in the school counseling program, the effectiveness of the program will increase.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1. Logic Model for ASCA National Model for School Counseling Programs

 

 

Discussion

 

Logic models can be used for a number of purposes including the following: enhancing communication among program team members, managing the program, documenting how the program is intended to operate and developing an approach to evaluation and related evaluation questions (Frechtling, 2007). The present study was conducted in order to develop a logic model for ASCA National Model programs so that these programs could be more readily evaluated, and based on the results of these evaluations, the ASCA National Model could then be improved.

 

Evaluations can focus on the question of whether or not a program or components of a program actually result in intended changes. At the most global level, an evaluation can focus on discovering the extent to which the program as a whole achieves its desired outcomes. At a more detailed level, an evaluation can focus on discovering the extent to which the components (i.e., activities) of the program achieve their desired outputs (with the assumption that achievement of the outputs is a necessary precursor to achievement of the outcomes).

 

In both types of evaluations, it is important to use a design that allows some form of comparison. In the simplest case, it would be possible to compare outputs and outcomes before and after implementation of the ASCA National Model. In more complex cases, it would be possible to compare outputs and outcomes of programs that have implemented the ASCA National Model with programs that have not. In these cases, it is essential to control for the confounding effects of extraneous variables (e.g., the affluence of students in the school) by the use of matching or covariates. If the level of implementation of the ASCA National Model program as a whole can be measured, it is even possible to use multivariate correlation approaches to examine whether the level of implementation of the program is related to desired outcomes while simultaneously controlling statistically for potential confounding variables. These same correlational procedures can be used to examine the relationships between the more discrete activities of the program and their corresponding outputs.

 

At the most global level, it is important to evaluate the extent to which the implementation of the ASCA National Model results in the following: increases in student achievement (and associated reductions in the achievement gap), measurable systemic change and school improvements, and increases in resources for the school counseling program. At present, there is some evidence that implementation of the ASCA National Model is related to achievement gains (Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, & Stevens, 2012). No evaluations to date have examined whether ASCA National Model implementation results in systemic change and school improvement or in an increase in program resources.

 

It also is important to evaluate the extent to which specific program activities achieve their desired outputs. Table 2 contains a list of sample evaluation questions for each activity. Within these questions, evaluation is focused on whether or not components of the program result in overall benefits. No evaluation study to date has evaluated the impact of ASCA National Model implementation on these factors.

 

Table 2

 

Sample Evaluation Questions for ASCA National Model Activities

 

Activities

Evaluation Questions

Direct Services Does organizing and delivering school counseling direct services in accordance with ASCA National Model principles result in an increase in important aspects of students’ school behavior that are related to academic achievement?
Indirect Services Does organizing and delivering school counseling indirect services in accordance with ASCA National Model principles result in an increase in parent involvement?
Does organizing and delivering school counseling indirect services in accordance with ASCA National Model principles result in an increase in teachers’ abilities to work effectively with students?
Does organizing and delivering school counseling indirect services in accordance with ASCA National Model principles result in improvements in school policies and procedures that support student achievement?
School Counselor Personnel Evaluation Does the implementation of personnel and processes recommended by the ASCA National Model result in increases in the professional competence of school counselors?
Management Processes Does the implementation of the management processes recommended by the ASCA National Model result in demonstrable improvements in the school counseling program?
Program Evaluation Does the implementation of program evaluation processes recommended by the ASCA National Model result in demonstrable improvements in the school counseling program?
Program Advocacy Does the implementation of the program advocacy practices recommended by the ASCA National Model result in increases in administrator support for the program?

 

 

 

In addition to examining program-related change, it is important to evaluate whether a basic assumption of the ASCA National Model bears out in reality. The major assumption is that school counselors who use the foundational elements of the ASCA National Model (e.g., vision statement, mission statement) and have access to typical levels of program resources can develop and implement all the activities associated with an ASCA National Model program (e.g., direct services, indirect services, school counselor personnel evaluation, management processes, program evaluation and program advocacy). Qualitative evaluations of the relationships between inputs and quality of the activities are necessary to determine what levels of inputs are necessary for full implementation. While full evaluation studies of this type have yet to be undertaken, Martin and Carey (2012) have recently reported the results of a two-state qualitative comparison of how statewide capacity-building activities to promote school counselors’ competence in evaluation were used to promote the widespread implementation of ASCA National Model school counseling programs. More studies of this type that focus on the relationships between a broader range of program inputs and school counselors’ ability to fully implement ASCA National Model program activities are needed.

 

Limitations and Future Directions

 

Constructing a logic model retrospectively is inherently challenging and complex. This is especially true when the program for which the logic model is being created was not initially developed with reference to an explicit, coherent theory of action. In the present study, the authors approached the work systematically and are confident that others following similar procedures would generate similar results. With that said, a limitation of this work is that the logic model was created based on the authors’ analyses of the written description of the ASCA National Model (2012) and literature surrounding the ASCA National Model. Engaging individuals who were involved in the development and implementation of the ASCA National Model in dialogue might have resulted in a richer logic model with even more utility in directing evaluation of the ASCA National Model. As a follow-up to the present study, the authors intend to continue this inquiry by asking key individuals involved with the ASCA National Model to evaluate the present logic model and to suggest revisions and extensions. Even given this limitation, the current study has potential immediate implications for improving practice that go beyond its role in providing focus and direction for ASCA National Model evaluation.

 

A potentially fertile testing ground for the implementation of the logic model is present within the RAMP Award process. As aforementioned, RAMP awards are given to exemplary schools that have successfully implemented the ASCA National Model. Currently, schools provide evidence (data) and create narratives regarding how they have successfully met RAMP criteria. Twelve independent rubrics are scored and totaled to determine whether a school receives a RAMP Award. At least two contributions of the logic model for improving the RAMP process seem feasible. First, practitioners can use the logic model to help construct narratives that better articulate how ASCA National Model activities/outputs relate to model outcomes. Second, the logic model may also help improve the RAMP process by highlighting clearer links between activities, outputs and outcomes. In future revisions of the RAMP process, more attention could be paid to the documentation of benefits achieved by the program in terms of both outputs (i.e., the immediate measurable positive consequences of program activities) and outcomes (i.e., the longer-term positive consequences of program operation). In this vein, the authors hope that the logic model developed in this study will help to improve the RAMP process for both practitioners and RAMP evaluators.

 

Retrospective logic models map a program as it is. In that sense, they are very useful in directing the evaluation of existing programs. Prospective logic models are used to design new programs. Using logic models in program design (or redesign) has some distinct advantages. “Logic models help identify the factors that will impact your program and enable you to anticipate the data and resources you will need to achieve success” (W. K. Kellogg Foundation, 2004, p. 65). When programs are planned with the use of a logic model, greater opportunities exist to explore foundational theories of change, to explore issues or problems addressed by the program, to surface community needs and assets related to the program, to consider desired program results, to identify influential program factors (e.g., barriers or supports), to consider program strategies (e.g., best practices), and to elucidate program assumptions (e.g., the beliefs behind how and why the strategies will work; W. K. Kellogg Foundation, 2004). The authors hope that logic modeling will be incorporated prospectively into the next revision process of the ASCA National Model. Basing future editions of the ASCA National Model on a logic model that comprehensively describes its theory of action should result in a more elegant ASCA National Model with a clearer articulation between its components and its desired results. Such a model would be easier to articulate, implement and evaluate. The authors hope that the development of a retrospective logic model in the present study will facilitate the prospective use of a logic model in subsequent ASCA National Model revisions. The present logic model provides a map of the current state of the ASCA National Model. It is a good starting point for reconsidering such questions as how the model should operate, whether the outcomes are the right outcomes, whether the activities are sufficient and comprehensive enough to lead to the desired outcomes, and whether the available program resources are sufficient to support implementation of program activities.

 

 

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of

interest or funding contributions for

the development of this manuscript.

 

 

 

References

 

American School Counselor Association. (2003). The ASCA national model: A framework for school counseling programs. Alexandria, VA: Author.

American School Counselor Association. (2005). The ASCA national model: A framework for school counseling programs. (2nd ed.). Alexandria, VA: Author.

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

American School Counselor Association. (2013). Past RAMP Recipients. Retrieved from http://www.ascanationalmodel.org/learn-about-ramp/past-ramp-recipients

Astramovich, R. L., & Coker, J. K. (2007). Program evaluation: The accountability bridge model for counselors. Journal of Counseling & Development, 85, 162–172. doi:10.1002/j.1556-6678.2007.tb00459.x

Astramovich, R. L., Coker, J. K., & Hoskins, W. J. (2005). Training school counselors in program evaluation. Professional School Counseling, 9, 49–54.

Carey, J., Harrington, K., Martin, I., & Hoffman, D. (2012). A statewide evaluation of the outcomes of ASCA National Model school counseling programs in rural and suburban Nebraska high schools. Professional School Counseling, 16, 100–107.

Carey, J. C., Harrington, K., Martin, I., & Stevens, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA National Model school counseling programs in high schools in Utah. Professional School Counseling, 16, 89–99.

Clemens, E. V., Carey, J. C., & Harrington, K. M. (2010). The school counseling program implementation survey: Initial instrument development and exploratory factor analysis. Professional School Counseling, 14, 125–134.

Dimmitt, C., Carey, J. C., & Hatch, T. A. (2007). Evidence-based school counseling: Making a difference with data-driven practices. New York, NY: Corwin Press.

Frechtling, J. A. (2007). Logic modeling methods in program evaluation. New York, NY: Wiley & Sons.

Gysbers, N. C., & Henderson, P. (2000). Developing and managing your school guidance program (3rd ed.). Alexandria, VA: American Counseling Association.

Heppner, P. P., Kivlighan, D. M., Jr., & Wampold, B. E. (1999). Research design in counseling (2nd ed.). Belmont, CA: Wadsworth.

Isaacs, M. L. (2003). Data-driven decision making: The engine of accountability. Professional School Counseling, 6, 288–295.

Johnson, C. D., & Johnson, S. K. (2001). Results-based student support programs: Leadership academy workbook. San Juan Capistrano, CA: Professional Update.

Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A statewide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x

Lapan, R. T., Gysbers, N. C., & Sun, Y. (1997). The impact of more fully implemented guidance programs on the school experiences of high school students: A statewide evaluation study. Journal of Counseling & Development, 75, 292–302. doi:10.1002/j.1556-6676.1997.tb02344.x

Loesch, L. C. (2001). Counseling program evaluation: Inside and outside the box. In D. C. Locke, J. E. Myers, & E. L. Herr (Eds.), The handbook of counseling (pp. 513–525). Thousand Oaks, CA: Sage.

Lusky, M. B., & Hayes, R. L. (2001). Collaborative consultation and program evaluation. Journal of Counseling & Development, 79, 26–38. doi:10.1002/j.1556-6676.2001.tb01940.x

Martin, I., & Carey, J. C. (2012). Evaluation capacity within state-level school counseling programs: A cross-case analysis. Professional School Counseling, 15, 132–143.

Martin, I., Carey, J. C., & DeCoster, K. (2009). A national study of the current status of state school counseling models. Professional School Counseling, 12, 378–386.

Martin, P. J. (2002). Transforming school counseling: A national perspective. Theory Into Practice, 41, 148–153. doi:10.1207/s15430421tip4103_2

Myrick, R. D. (2003). Developmental guidance and counseling: A practical approach (4th ed.). Minneapolis, MN: Educational Media Corporation.

Schmidt, J. J. (1995). Assessing school counseling programs through external interviews. School Counselor, 43, 114–123.

Sexton, T. L., Whiston, S. C., Bleuer, J. C., & Walz, G. R. (1997). Integrating outcome research into counseling practice and training. Alexandria, VA: American Counseling Association.

Trevisan, M. S. (2000). The status of program evaluation expectations in state school counselor certification requirements. American Journal of Evaluation, 21, 81–94. doi:10.1177/109821400002100107

Trevisan, M. S. (2002). Evaluation capacity in K-12 school counseling programs. American Journal of Evaluation, 23, 291–305. doi:10.1016/S1098-2140(02)00207-2

W. K. Kellogg Foundation. (2004). Logic model development guide. Battle Creek, MI: Author.

 

Ian Martin is an assistant professor at the University of San Diego. John Carey is a professor at the University of Massachusetts, Amherst, and the Director of the Ronald H. Fredrickson Center for School Counseling Outcome Research and Evaluation. Correspondence can be addressed to: Ian Martin, 5998 Alcala Park, San Diego, CA 92110, imartin@sandiego.edu.