The Delaware Social-Emotional Competency Scale

Child Ind Res DOI 10.1007/s12187-016-9427-6

The Delaware Social-Emotional Competency Scale (DSECS-S): Evidence of Validity and Reliability Lindsey S. Mantz 1 & George G. Bear 1 & Chunyan Yang 2 & Angela Harris 1

Accepted: 11 October 2016 # Springer Science+Business Media Dordrecht 2016

Abstract The Delaware Social-Emotional Competency Scale (DSECS-S) was developed to provide schools with a brief, inexpensive, and psychometrically sound selfreport scale to assess students’ social-emotional competencies. Confirmatory factor analyses, conducted on a sample of 32,414 students from 126 public elementary, middle, and high schools in Delaware, showed that a second-order model consisting of four specific factors and one general factor (social-emotional competence) best represented the data. Those four factors are represented in the four subscales of the DSECS-S: Responsible Decision Making, Relationship Skills, Self-Management, and Social Awareness. The scale’s factor structure was shown to be consistent across grade levels (i.e., elementary, middle, and high school), racial–ethnic groups (i.e., White, Black, Hispanic/Latino, Asian, and Multi-racial), and gender. As evidence of the scale’s criterion-related validity, the total social-emotional competency score correlated significantly and positively with students’ self-reported cognitive, behavioral, emotional, and total engagement. At the school level, social-emotional competence correlated positively with school-level academic achievement and negatively with suspensions/ expulsions. Keywords Social-emotional competence . Social-emotional learning . CASEL . Program evaluation . Assessment

Electronic supplementary material The online version of this article (doi:10.1007/s12187-016-9427-6) contains supplementary material, which is available to authorized users.

* Lindsey S. Mantz [email protected]

1

School of Education, University of Delaware, Willard Hall Education Building, Newark, DE 19716, USA

2

Department of Counseling, Clinical, and School Psychology, University of California, Santa Barbara, CA 93106, USA

L.S. Mantz et al.

1 Introduction In recent years, many schools have increased their efforts to develop students’ socialemotional competencies through the adoption of social-emotional learning (SEL) programs (Dusenbury et al. 2014; Jones and Bouffard 2012). According to the Collaborative for Academic, Social, and Emotional Learning (CASEL 2012), there are five general social-emotional competencies targeted through SEL: responsible decision making, relationship skills, self-management, social awareness, and self-awareness. Although fostering social-emotional competencies is not a new educational aim (Zins et al. 2004), its increased focus may largely be attributed to accumulating studies showing that these competencies are associated with important student outcomes (Eisenberg et al. 2006; Kwon et al. 2012; Tangney et al. 2004). As a result of the increased efforts in social-emotional development, there is a clear need for instruments that assess these competencies and that are both psychometrically sound and of high practical utility to schools. Developing such an instrument was the primary focus of the current study. Before further discussing the need for the instrument, however, it is important to review the social-emotional competence construct and explore its relation to student outcomes. 1.1 CASEL’s Five Social-Emotional Competencies An extensive review on the five social-emotional competencies highlighted by CASEL and how they are interrelated is beyond the scope of this article. However, brief research summaries relating each of the five competencies to important student outcomes follow. 1.1.1 Responsible Decision Making Responsible decision making refers to the ability to make safe, respectful, and moral decisions about one’s behavior and interactions with others (CASEL 2012). It emphasizes social problem solving and moral reasoning—making decisions that prevent and resolve social problems, guided by responsible consideration of the needs of others, as opposed to only oneself. Students with stronger responsible decision making skills typically demonstrate greater empathy and sympathy (Eisenberg-Berg and Mussen 1978; Eisenberg et al. 2001), greater prosocial behavior (Eisenberg et al. 2006; Ongley et al. 2014) and stronger perspective-taking skills (Eisenberg et al. 2001). Relatedly, they also tend to demonstrate greater competence in peer interactions (Pettit et al. 1988), which may explain why they typically have more friends (Schonert-Reichl 1999) and are more popular among peers (Newcomb et al. 1993; Pakaslahti et al. 2002). 1.1.2 Relationship Skills Relationship skills refer to the ability to form and maintain healthy friendships, listen to others, work cooperatively, handle conflict constructively, and assist others (CASEL 2012). Studies examining relationship skills often use instruments that combine relationship skills with other social-emotional competencies, such as social awareness

The Delaware Social-Emotional Competency Scale...

skills or self-management. Nevertheless, these studies suggest that students with stronger relationship skills are more popular and accepted by peers and have more reciprocated friendships compared to students with weaker relationship skills (Kwon et al. 2012; Newcomb et al. 1993). Students with stronger skills in this area also tend to like school more, demonstrate greater school engagement, and display greater academic behaviors (Kwon et al. 2012). 1.1.3 Self-Management Self-management refers to skills in effectively regulating one’s thoughts, emotions, and behaviors (CASEL 2012). Greater skills in this area are associated with fewer behavior problems (Graziano et al. 2007), higher self-esteem (Tangney et al. 2004), less psychopathology (Tangney et al. 2004), and less cigarette, alcohol, and drug abuse later in life (Romer et al. 2010; Tangney et al. 2004). Students with greater self-management skills tend to exhibit greater interpersonal skills (Tangney et al. 2004) and stronger relationships (Tangney et al. 2004), including their relationships with teachers (Graziano et al. 2007). Self-management skills also are positively associated with academic achievement (Blair and Razza 2007; Duckworth et al. 2013; Tangney et al. 2004). 1.1.4 Social Awareness Social awareness refers to individuals’ ability to understand others’ behavior, take others’ perspective, and demonstrate empathy (CASEL 2012). Stronger skills in this area are associated with less aggression and externalizing behaviors (Li et al. 2015; Miller and Eisenberg 1988) and greater prosocial behavior (Cigala et al. 2014; Fitzgerald and White 2003). 1.1.5 Self-Awareness Self-awareness refers to skills in identifying one’s own emotions and thoughts and in understanding how thoughts and emotions impact one’s behavior (CASEL 2012; Zins and Elias 2006). Difficulty identifying one’s emotions is related to greater difficulty in expressing emotions (Ciarrochi et al. 2003), less social support (Ciarrochi et al. 2008), and less effective problem solving styles (Ciarrochi et al. 2003). Self-awareness also refers to self-efficacy and the ability to assess personal strengths and weaknesses (CASEL 2012; Zins and Elias 2006). There is greater support linking self-efficacy to valued educational outcomes than there is for self-concept. Research shows that students with greater self-efficacy tend to be happier and less lonely (Cheng and Furnham 2002) and more academically engaged (Multon et al. 1991; Pintrich and DeGroot 1990). Literature reviews of self-concept, which includes self-perceptions in academic and nonacademic domains and overall self-worth or self-esteem (Harter 2006), generally find it to be positively related to academic motivation and achievement, life satisfaction and happiness, and positive social relations; however, these associations are not as strong as commonly believed (Hattie 1992; Baumeister et al. 2003; Manning et al. 2006). For example, although researchers have reported a positive relation between self-concept and peer relations (e.g., Deković and Meeus 1997;

L.S. Mantz et al.

Connolly and Konarski 1994), they also have reported that self-esteem is unrelated to students’ bullying behaviors (Rigby and Slee 1991; Seals and Young 2003). Selfconcept also tends to be related more strongly to internalizing problems than to happiness (Sowislo and Orth 2013). Research is less supportive about the connection between self-esteem and important student outcomes. Although lower self-esteem has been linked to greater depression and anxiety (Sowislo and Orth 2013), Hattie (1992) showed there to be a weak relationship between self-esteem and academic achievement. As seen above, in general, research has found the social-emotional competencies identified by CASEL to be associated with important student outcomes; thus, research largely supports schools’ efforts to develop those competencies through SEL programs. Numerous studies have investigated the effectiveness of SEL programs and have generally found them to be associated with stronger social-emotional competencies, greater prosocial behaviors, fewer behavior problems, and less emotional distress for students (Durlak et al. 2011; Sklad et al. 2012). Despite these findings and the widespread implementation of SEL programs, there is a lack of instruments that assess social-emotional competencies that are psychometrically sound and of high practical utility to schools. As will be evidenced below, instruments are particularly lacking in their practical utility to schools. 1.2 Lack of Useful Instruments for Assessing CASEL’s Social-Emotional Competencies Several recent reviews have been conducted on the instruments commonly used to assess children’s social-emotional competence, which include not only rating scales but also direct assessments, observations, and interviews (see Denham 2015; Denham et al. 2010; Elliot et al. 2015; Haggerty et al. 2011; McKown et al. 2015). In those reviews, evidence is provided to support the validity and reliability of the instruments for the purposes they are used, which typically is for research on social-emotional competence or the identification of individual students lacking in social-emotional competence. Unfortunately, most reviews neglect to discuss the practical utility of these instruments in assessing the schoolwide SEL needs of students and the effectiveness of school-wide intervention programs. An exception, however, is when the instruments reviewed are those other than rating scales. For example, in questioning their practical utility, yet recognizing their usefulness for research purposes, Denham (2015) noted that Bthe time required for direct assessments, observations, and interviews, as well as resources required for training, establishing observer/coder reliability, and coding or scoring, make these tools most practical for research usage^ (p. 292). In light of the practical limitations of those instruments for use in schools, reviews focus primarily on social-emotional competency rating scales. Unfortunately, they also tend to provide little consideration of the practical utility the rating scales reviewed. To be of high practical utility, a rating scale needs to be brief, requiring little instruction time; inexpensive, costing little to administer to large numbers of students; and consist of items that align with the social-emotional competencies targeted in SEL programs. Rating scales included in the reviews noted above lack one or more of these qualities, as exemplified below.


Many rating scales assessing social-emotional competence consist of behavioral checklists completed by teachers or parents, such as the Devereux Student Strengths Assessment (LeBuffe et al. 2009) and the Behavioral and Emotional Rating Scale (Benner et al. 2008). When completed for each individual student to assess either program needs or program effectiveness, such checklists are very time consuming, especially when completed by teachers, and costly. For example, the Behavioral and Emotional Rating Scale takes approximately 10 minutes for a teacher to complete for each individual child and costs $198 to purchase, with additional rating forms costing $1.48 each (Pro-Ed Inc 2012). Thus, one school-wide administration to 500 students would cost $935, not including the cost of time and resources for scoring and reporting. Other instruments are self-report rating scales, completed by students, which have their advantages. First, student self-reports are more time efficient, often requiring less than 15 minutes for a class (or school) to complete. Secondly, student self-reports can provide insight into the social-emotional competencies they perceive are personal strengths and weaknesses, which can be helpful information for schools when planning social-emotional programming and interventions. Thirdly, student reports may be less biased with respect to reporting improvements attributed to SEL programs. Students are less personally invested in demonstrating program success – success (or lack thereof) that might reflect upon the teachers who complete the surveys. Lastly, in some areas students may be more accurate reporters of their social-emotional competency than are their teachers. That is, teachers are unlikely to observe all incidents in which students demonstrate prosocial behavior, empathy, and responsibility and therefore are likely to either under- or over-report students’ competencies. For example, research has shown that teachers report less frequent bullying and fewer students involved in bullying compared to students (Bradshaw et al. 2007; Stockdale et al. 2002). Perhaps a greater limitation of existing rating scales is that they fail to align with CASEL’s five social-emotional competencies, being either too broad or narrow in scope. On the one hand, several instruments simultaneously assess multiple dimensions of social-emotional competence (not aligned with CASEL’s competencies) while also including additional constructs, which makes the scale quite lengthy. This is seen in The Developmental Assets Profile (Search Search Institute 2014), which also assesses family and community support, The Comprehensive School Climate Inventory (National School Climate National School Climate Center 2016), which also assesses multiple aspects of school climate, and the Social Skills Improvement System (Gresham and Elliott 2007), which also assesses students’ academic competence. On the other hand, other instruments are too narrow in their scope and therefore fail to provide schools with a score that assesses one or two of CASEL’s five social-emotional competencies. For example, the Prosocial Tendencies Measure – Revised (Carlo et al. 2003) primarily assesses students’ altruistic behavior and the Assessment of Children’s Emotion Skills (Schultz et al. 2004) assesses students’ accuracy in emotional attribution. 1.3 Purpose of the Study Given the disadvantages of existing instruments, as noted above, there is a need for a brief, inexpensive (i.e., free to use), student self-report instrument that assesses CASEL’s five social-emotional competencies. This is the primary focus of this study.

L.S. Mantz et al.

This instrument, the Delaware Social-Emotional Competency Scale (DSECS-S), is described below. Evidence is presented confirming its factorial validity and measurement invariance across grade levels, racial-ethnic groups, and gender. In addition, evidence is presented of the instrument’s reliability and criterionrelated validity. With respect to criterion-related validity, it was hypothesized that students’ self-ratings of their social-emotional competency would correlate positively with their school engagement. Moreover, it was hypothesized that at the school level, social-emotional competence would correlate positively with academic achievement and negatively with school suspensions and expulsions. The hypothesized associations of social-emotional competence with school engagement, academic achievement, and suspensions and expulsions are well documented in the previous research literature. Numerous studies have shown students with greater social-emotional competence to demonstrate greater engagement in school (Kwon et al. 2012; Perdue et al. 2009) and greater academic achievement (Malecki and Elliot 2002; Wentzel 1993). This association may be due to students with stronger social-emotional competence having more positive relationships with their teachers (Luckner and Pianta 2011; Mashburn et al. 2008), which in turn is associated with having greater school engagement (Roorda et al. 2011) and academic achievement (Graziano et al. 2007). The hypothesized association between social-emotional competence and suspensions/expulsions is supported by studies showing that students with stronger social-emotional competence demonstrate fewer behavior problems (Bornstein et al. 2010). With fewer behavior problems, there clearly would be less of a need to suspend or expel students from school.

2 Method 2.1 Participants and Procedures The original sample consisted of 33,048 students enrolled in 126 schools in grades 3–12 in the state of Delaware. Among those students, 634 (2.0 % of the sample) were deleted because responses on demographic items (i.e., gender, race/ethnicity, and grade) were missing. Rather than estimating missing values, we deleted these cases because gender, race/ethnicity, and grade level were used as grouping variables in the multi-group confirmatory factor analyses. As shown in Table 1, the final participants in the study included 32,414 students from 126 public schools in Delaware, representing 73 % of general public education schools in the state. In this final sample, missing responses to individual items on the survey ranged from 1.0 % to 1.6 %. Based on data reported by the Delaware Department of Education (DDOE), 41.7 % of students in those schools qualified for free or reduced lunch, with percentages across schools ranging from 3.8 % to 86.5 %. Of the students in the study, 15,096 came from 79 elementary schools (Grades K-5), 10,805 came from 28 middle schools (Grades 6-8), and 6513 came from 19 high schools (Grades 9-12). Based on students’ reports on demographic items of the survey, 49.2 % of participants were male and 50.8 % were female; 47.0 % White, 26.1 %, Black, 12.9 % Hispanic/Latino, 3.7 % Asian,

The Delaware Social-Emotional Competency Scale... Table 1 Demographic information for students in the sample Student participants Elementary

Middle

n

%

n

%

n

%

n

%

15,096

46.6 %

10,805

33.3 %

6513

20.1 %

32,414

100 %

Male

7478

49.5 %

5308

49.1 %

3161

48.5 %

15,947

49.2 %

Female

7618

50.5 %

5497

50.9 %

3352

51.5 %

16,467

50.8 %

Caucasian

7018

46.5 %

5039

46.6 %

3166

48.6 %

15,223

47.0 %

African American

3909

25.9 %

2756

25.5 %

1784

27.4 %

8449

26.1 %

Hispanic/ Latino

2005

13.3 %

1444

13.4 %

729

11.2 %

4178

12.9 %

Asian

557

3.7 %

365

3.4 %

276

4.2 %

1198

3.7 %

Multi-Racial

1607

10.6 %

1201

11.1 %

558

8.6 %

3366

10.4 %

Full Sample

High

Total

Gender

Race/Ethnicity

and 10.4 % Multi-racial 1. Comparing those racial/ethnic group results to those reported by the DDOE, the percentages for racial/ethnic groups reflected many of the overall percentages in the state at the time of the study (46.6 % White, 15.3 % Hispanic/Latino, and 3.6 % Asian). However, Black students were slightly underrepresented (26.1 % vs 31.2 %) and Multi-racial students were overrepresented (10.4 % vs 2.8 %). This discrepancy could partly be attributed to the different individuals reporting the data. Whereas students reported their race/ethnicity in the current study, their parents/guardians reported the students’ race/ethnicity in the data from DDOE. All public schools in Delaware were invited to participate in the survey by the DDOE, and their participation was voluntary. To ensure a sufficient sample size per school, schools were asked to survey 100 % of students in grade 3 and above in elementary school, 50 % of students in middle and high schools with more than 600 students, and 100 % of students in schools serving 300 or fewer students. Schools were asked to select students randomly for participation and were provided with guidance on how to do so. Each school’s survey response rates (i.e., the number of students in a school who were included in the final sample divided by the number of eligible students enrolled in the school) ranged from 13.9 % to 97.4 % (mean = 63.1 %; median = 70.1 %; average number of respondents per school = 257). Schools were able to choose to survey their students with either an online Qualtrics survey or a paper Scantron version. When completing the survey, students were asked to not indicate their name on their survey to ensure anonymity, but were asked to report their gender, grade, and race/ethnicity.

1

Due to low sample sizes, American Indian/Alaskan Native (1.9 % of total sample) and Hawaiian students (0.3 % of total sample) were not included in analyses.

L.S. Mantz et al.

2.2 Social-Emotional Competency Measure The DSECS-S was designed to be a separate scale as part of the Delaware School Surveys – Student Version (DSS-S). The DSS-S is a survey administered to students from grades 3-12 in public schools throughout the state of Delaware and assesses students’ perceptions of the climate in their school, the classroom management techniques used by their teachers, their school engagement, and the frequency to which they experience bullying victimization. More information about the DSS-S can be found in the survey’s technical manual (Bear et al. 2016). Although the DSECS-S was designed to be part of the larger DSS-S survey, and was used as such in the current study, it can be administered independent of the other DSS-S scales. This allows schools the flexibility in choosing what measures they would find most helpful in selecting prevention and intervention programs. The DSECS-S consists of a total of 12 items, with three items assessing each of the four factors: responsible decision making, relationship skills, self-management, and social awareness. The four factors were viewed as underlying the more general factor or construct of social-emotional competence. Students respond to each item using a 4point Likert-type scale of 1 = not like me at all, 2 = not much like me, 3 = somewhat like me, and 4 = very much like me. A total social-emotional competency score is derived by summing raw scores across all 12 items. The four factors of the DSECS-S (responsible decision making, relationship skills, self-management, and social awareness) represent four of the five previously described social-emotional competencies presented in the CASEL (2012) framework. The fifth competency, self-awareness, was excluded from the DSECS-S for three primary reasons. First, this competency emphasizes skills in recognizing one’s emotions and assessing personal strengths and weaknesses, which would likely include students’ feelings of self-esteem and emotional well being (and lack thereof, such as feelings of depression). Assessing those areas would require a higher level of parental approval for students to complete the surveys. Second, assessing students’ self-awareness skills raises some ethical issues, such as whether or not students should be identified for reporting low self-esteem (or high levels of depression) and whether or not further assessment or mental health services should be provided to these students. Although mental health screening is certainly important, it is not the focus of the DSECS-S. Lastly, research often shows that self-esteem, which is included under the selfawareness construct, is often unrelated to valued academic and social outcomes (other than depression) and that programs that target improving self-esteem and self-concept rarely improve those outcomes (Baumeister et al. 2003; Davies and Brember 1999; Hoge et al. 1995; Manning et al. 2006). As a result, providing schools with information about skills they are not likely to change does not seem practical. The 12 items of the DSECS-S were drafted, reviewed, and refined by a committee consisting of a university professor with expertise in SEL and test development, two additional university staff with expertise in implementing SEL programs and schoolwide positive behavioral interventions and supports (SWPBIS), two graduate research assistants, and a representative of the DDOE (with expertise in SWPBIS and school climate). Based on a comprehensive review of the research literature on measures of social-emotional competencies identified by CASEL, the committee reviewed items on existing measures, including those measures cited previously and additional ones used


by researchers to assess social and emotional competence. Next, 12 items were developed by the committee, written with a readability at or below a third grade level, with three items designed to assess each of the four social-emotional competencies targeted and as described in CASEL’s (2012) framework. Only three items were developed for each subscale, as requested by the DDOE, in order to keep the scale as brief as possible for practical reasons (i.e., limited time away from instruction). Three items, and no fewer, also were developed per subscale because this was the minimum number found in previous research on the DSS-S to provide alpha coefficients of adequate reliability for a subscale (i.e., above .70; Bear et al. 2016). Multiple revisions to items occurred based on readability, discussion among committee members, and review by several teachers. 2.3 Measures Used for Criterion-Related Validity 2.3.1 School Engagement Students’ engagement in school was assessed with the Delaware Student Engagement Scale (DSES-S), which is part of the DSS-S. This scale consists of three subscales that each assesses a distinct type of school engagement: cognitive (e.g., BI try my best in school^; BI turn my homework in on time^), behavioral (e.g., BI pay attention in class^; BI follow the rules at school^), and emotional (e.g., BI feel happy in school^: BMy school is a fun place to be^). Each subscale contains four items to which students respond on a 4-point Likert-type scale of 1 = disagree a lot, 2 = disagree, 3 = agree, and 4 = agree a lot. Summing raw scores across all 12 items derives a total student engagement score. Results from CFA on the DSES-S can be found in the DSS technical manual (Bear et al. 2016). These results supported a three-factor second-order model, yielding adequate fit indices: χ 2 = 3790.31 (41, N = 32,412), p < .001; CFI = .979, RMSEA = .053 [.052, .055], and SRMR = .032. Measurement invariance was also supported across grade levels (elementary, middle, and high school), racial/ethnic groups, and gender. In the current study, alpha coefficients for total engagement for elementary, middle, and high school students were .87, .89, and .87, respectively. 2.3.2 Academic Achievement School-level aggregated academic achievement data were obtained from the DDOE school profile website for each school. These data included the percentage of students passing the English Language Arts (ELA) and Mathematics portions of the Smarter Balanced Assessment given in Spring 2015. Smarter Balanced Assessment is a measure of students’ academic progress aligned with Common Core standards. The score used in this study reflects the percentage of students receiving a passing score or above on the assessment. 2.3.3 Suspensions and Expulsions This consisted of the percentage of students suspended or expelled (non-duplicated count) in each school during the 2014-2015 school year, as reported by the DDOE.

L.S. Mantz et al.

2.4 Data Analyses Confirmatory Factor Analysis (CFA) was conducted in Mplus 7.31 (Muthén and Muthén 1998-2015) to confirm the proposed second-order model consisting of four lower-order factors of responsible decision making, relationship skills, selfmanagement, and social awareness, and a higher-order factor of socialemotional competence. Missing data analysis was performed using the full information maximum likelihood (FIML) estimator in Mplus. FIML is a recommended procedure for estimating parameters with incomplete data. Because students were nested within schools, group mean centering was used to address the clustering issue by removing school mean differences from the item responses. This method was used instead of multi-level CFA given the small number of middle and high schools in the study. For cross-validation purposes, the sample was randomly divided into two subsamples, with the first subsample examining model fit for the hypothesized second-order model and the second subsample verifying the model. Goodness of model fit was evaluated using the Comparative Fit Index (CFI), the Standardized Root Mean-Square Residual (SRMR), and the Root Mean-Square Error of Approximation (RMSEA). Values for these indices range from 0 to 1. Generally, CFI values close to or greater than .95, SRMR values close to or less than .08, and RMSEA values close to or less than .06 reflect adequate fit (Hu and Bentler 1998). When used in combination, instead of independently, these indices provide a more conservative and reliable evaluation of model fit (Brown 2015). Measurement invariance was tested in a hierarchical sequence with increasingly restrictive steps to investigate whether the factor structure of the final model was statistically equivalent across gender, grade level and race-ethnicity groups. In doing so, we followed five steps suggested by Chen and colleagues (2005): (a) configural invariance (Models 1), (b) first-order factor loading invariance (Models 2), (c) first- and second-order factor loading invariance (Models 3), (d) first- and second-order factor loading and intercepts of measured variables invariance (Models 4), and (e) first- and second-order factor loadings, and intercepts of measured variables and first-order factors invariance (Models 5). To compare the fit for the nested models, the Satorra–Bentler scaled chisquare difference (Asparouhov and Muthén 2010) and goodness-of-fit indexes (Cheung and Rensvold 2002) were used. However, because the performance of the chi-square difference test is also affected by non-normality and large sample size, we followed the recommendation by Cheung and Rensvold (2002) and considered a difference of larger than .01 in the change of CFI as an indication of a meaningful change in model fit when testing measurement invariance. In addition to CFA, correlations among factors and reliability of factor and total social-emotional competency scores were examined. To provide evidence of criterion-related validity: (a) individual students’ total social-emotional competency scores were correlated with their self-reported school engagement, and (b) aggregated school-level social-emotional competency scores were correlated with school-level academic achievement and suspensions/expulsions.


3 Results 3.1 Results of Confirmatory Factor Analyses 3.1.1 Confirming Fit of Second-Order Model CFA conducted on the first subsample yielded adequate fit indices for the proposed second-order model: χ 2 = 1357.93 (50, N =16,205), p < .001; CFI = .957, RMSEA = .040 [.038, .042], and SRMR = .030. Confirmatory factor analyses on the second randomly-split approximately half of the sample also generated robust fit statistics: χ2 = 1380.51 (50, N =16,205), p < .001; CFI = .959, RMSEA = .041 [.039, .043], and SRMR = .029. When the completely standardized factor loadings were compared, factor loadings were similar across the two samples, as shown in Table 2. A summary of the fit statistics for the second-order model with full sample and subsamples in Table 3 suggested there were no appreciable differences in the fit indices across all subsamples. Thus, all subsequent analyses were run with the full sample. The

Table 2 Confirmatory factor analysis of the second-order model (DSECS-S) Item

Sample 1 Loading

Sample 2 SE

z

Loading

SE

z

Higher-order Factor: Social Emotional Competency (SEC) Responsible Decision Making

.98

.01

124.08

.99

.01

136.79

Relationships Skills

.92

.01

81.77

.91

.01

99.34

Self-Management

.96

.01

131.89

.96

.01

13.02

Social Awareness

.71

.01

77.72

.71

.01

83.61

.34

.01

29.47

.34

.01

27.04

5. I feel responsible for how I act.

.68

.01

72.44

.70

.01

8.69

9. I am good at deciding right from wrong.

.69

.01

65.67

.69

.01

69.34

First-order Factor 1: Responsible Decision Making (RDM) 1. I blame others when I’m in trouble.

First-order Factor 2: Relationship Skills (RS) 4. I am good at solving conflicts with others.

.62

.01

74.47

.62

.01

73.71

8. I get along well with others.

.65

.01

73.88

.65

.01

63.21

12. I have one or more close friends.

.40

.02

26.67

.40

.02

26.37

First-order Factor 3: Self-Management (SM) 3. I can control how I behave.

.62

.01

54.88

.62

.01

6.19

7. I think before I act.

.71

.01

103.30

.70

.01

101.48

11. I am good at waiting for what I want.

.55

.01

59.72

.55

.01

68.32

2. I think about how others feel.

.79

.01

121.41

.78

.01

11.62

6. I care about how others feel.

.86

.01

155.07

.85

.01

153.44

10. What others think is important to me.

.40

.01

41.39

.40

.01

43.23

First-order Factor 4: Social Awareness (SA)

L.S. Mantz et al. Table 3 Fit statistics between groups for second-order model (DSECS-S) Model

N

χ2

df

CFI

SRMR

RMSEA 95 % [CI]

Full Sample

32,157

2790.66

50

.965

.030

.041 [.040, .043]

Elementary

14,935

1125.54

50

.964

.028

.038 [.036, .040]

Middle

10,736

1236.23

50

.963

.033

.047 [.045, .049]

High

5176

966.97

50

.950

.037

.053 [.050, .056]

Male

15,795

1664.24

50

.954

.033

.045 [.043, .047]

Female

16,362

1,28.66

50

.969

.028

.039 [.037, .041]

White

15,147

1428.88

50

.962

.031

.043 [.041, .045]

Black

8348

857.73

50

.954

.032

.044 [.041, .047]

Hispanic

4142

417.85

50

.964

.031

.042 [.038, .046]

Asian

1191

144.53

50

.970

.031

.040 [.032, .048]

Multi-Racial

3329

266.47

50

.970

.028

.036 [.032, .040]

Note. χ2 = Chi-square statistic; df = degrees of freedom; CFI = Comparative Fit Index; SRMR = Standardized Root Mean- Square Residual; RMSEA = Root Mean-Square Error of Approximation

standardized factor loadings for the second-order model with full sample size are illustrated in Fig. 1. 3.1.2 Testing Measurement Invariance Across Gender, Grade Level, and Race/Ethnicity As discussed previously, five sets of models were compared in testing measurement invariance across gender, grade level, and race/ethnicity groups. As shown in Table 4,

Fig. 1 Standardized factor loadings for the second-order model in the full sample

The Delaware Social-Emotional Competency Scale... Table 4 Fit statistics for confirmatory factor analysis of second-order model testing measurement invariance across gender, grade level and race/ethnicity groups χ2

df

CFI SRMR RMSEA

Model Comparison

ΔSBχ2

Δdf ΔCFI

Invariance Test across Gender Model 1a 2714.88* 100 .956 .030

.040 [.039, .042]

Model 2 b 2774.55* 108 .955 .031

.039 [.038, .040] 2 vs. 1

10.83

8

- .001

Model 3 c 2777.01* 111 .955 .032

.039 [.037, .040] 3 vs. 2

2.14

3

.000

Model 4 d 2976.66* 119 .952 .032

.039 [.037, .040] 4 vs. 3

0.27

8

- .003

Model 5 e 3051.51* 122 .951 .032

.039 [.037, .040] 5 vs. 4

0.01

3

- .001

145.06

16

- .002

Invariance Test across Grade Level Model 1a 3314.59* 150 .954 .031

.044 [.043, .046]

Model 2 b 3412.35* 166 .952 .034

.043 [.041, .044] 2 vs. 1

c

3447.83* 172 .952 .035

.042 [.041, .043] 3 vs. 2

24.24

6

.000

Model 4 d 3767.62* 188 .947 .035

.042 [.041, .043] 4 vs. 3

0.00

16

- .005

Model 5 e 3907.55* 195 .945 .035

.042 [.041, .043] 5 vs. 4

0.06

7

- .002

Model 3

Invariance Test across Race/Ethnicity Model 1a 2923.33* 250 .959 .031

.041 [.039, .042]

Model 2 b 3022.86* 282 .958 .033

.039 [.038, .040] 2 vs. 1

9.18

32

- . 001

Model 3 c 3089.25* 294 .957 .034

.038 [.037, .040] 3 vs. 2

4.94

12

- . 001

Model 4 d 3424.53* 326 .950 .034

.038 [.037, .040] 4 vs. 3

0.83

32

- .007

Model 5 e 3581.76* 341 .950 .034

.038 [.037, .040] 5 vs. 4

0.04

15

.000

Note. a Model 1: configural invariance; b Model 2: first-order factor loadings invariant; c Model 3: first- and second-order factor loadings invariant; d Model 4: first- and second-order factor loadings and intercepts of measured variables invariant; e Model 5: first- and second-order factor loadings, and intercepts of measured variables and first-order factors invariant χ2 = Chi-square statistic; df = degrees of freedom; CFI = Comparative Fit Index; SRMR = Standardized Root Mean-Square Residual; RMSEA = Root Mean-Square Error of Approximation; ΔS-Bχ2 = Satorra–Bentler scaled chi-square difference, Δdf = Change of Degrees of Freedom; ΔCFI = change of Comparative Fit Index. *p < .001

Models 1 all yielded adequate model fit indices across each of these groups. The difference between test statistics for the invariance of first-order factor loadings (Models 2) and configural invariance (Models 1) indicated invariance of first-order factor loadings across gender (ΔS-Bχ2 = 10.83, Δdf = 8, ns; ΔCFI = - .001), grade level (ΔS-Bχ2 = 145.06, Δdf = 16, p < .001; ΔCFI = - .002), and race/ethnicity: (ΔS-Bχ2 = 9.18, Δdf = 32, ns; ΔCFI = - .001). The difference between test statistics for the models testing invariance of first- and second-order factor loadings (Models 3) and invariance of first-order factor loadings (Models 2) indicated invariance of second-order factor loadings across gender (ΔS-Bχ2 = 2.14, Δdf = 3, ns; ΔCFI = .000), grade level (ΔS-Bχ2 = 24.24, Δdf = 6, ns; ΔCFI = .000), and race/ethnicity (ΔS-Bχ2 = 4.94, Δdf = 12, ns; ΔCFI = - .001). The difference between test statistics for the models testing invariance of invariance of firstand second-order factor loading and intercepts (Models 4) and invariance first- and secondorder factor loadings (Models 3) indicated that invariance of intercepts across gender (ΔSBχ2 = 0.27, Δdf = 8, ns; ΔCFI = - .003), grade level (ΔS-Bχ2 = 0.00, Δdf = 16, ns;

L.S. Mantz et al.

ΔCFI = - .005), and race/ethnicity: (ΔS-Bχ2 = 0.83, Δdf = 32, ns; ΔCFI = - .007). The difference between test statistics for the models testing invariance of first- and second-order factor loadings and intercepts of measured variables and first-order latent factors (Models 5) and invariance of first- and second-order factor loading and intercepts (Models 4) indicated invariance of first-order latent factors across gender (ΔS-Bχ2 = 0.01, Δdf = 3, ns; ΔCFI = .001), grade level (ΔS-Bχ2 = 0.06, Δdf = 7, ns; ΔCFI = - .002), and race/ethnicity: (ΔSBχ2 = 0.04, Δdf = 15, ns; ΔCFI = .000). 3.2 Correlations Among Factors and Internal Consistency To examine the relative independence of the scores, as well as the relationship between the subscale scores and the total score, correlations of latent means were calculated between subscale and scale scores. As seen in Table 5, moderate to high correlations between subscales were found, with coefficients ranging from .65 to .95 and a median of .80. When correlations between factors using manifest scores of total scale and subscales were calculated, all correlations were also significant at .001 level, with correlation coefficients ranging from .47 to .82, and a median of .52. These correlations indicate that the four factors are interrelated and share a large portion of common characteristics of the general construct of social emotional competency. Internal consistency coefficients were calculated to demonstrate reliability of the subscale scores. As seen in Table 5, for three of the four subscales the alpha coefficients were below the minimally accepted criterion of .70, which can largely be attributed to having only three items per subscale. Given these low coefficients, it is not recommended that subscale scores be used. As expected, reliability was much higher for the total social-emotional competency score. For all students combined, the total scale alpha coefficient was .84, with coefficients ranging from .83 to .85 across gender, grade level, and race/ethnicity groups. 3.3 Student- and School-Level Correlations with Engagement, Academic Achievement, and Suspensions/Expulsions Consistent with previous research on the relation of social-emotional competence with academic achievement and student behavior, the total social-emotional competency score correlated significantly and positively with individual students’ selfTable 5 Internal consistency coefficients and correlation coefficients between subscale and total socialemotional competency scores 1

2

3

4

1. Responsible Decision Making

(.58)

2. Relationship skills

.91

(.58)

3. Self-Management

.95

.88

(.65)

4. Social Awareness

.71

.65

.69

(.69)

5. Total Social-Emotional Competence

.99

.91

.96

.72

5

(.84)

Note. Values in parentheses are coefficients of internal consistency (Cronbach’s alpha) for each subscale. All correlations are significant at p < 0.001


reported cognitive, behavioral, emotional, and total engagement. As seen in Table 6, correlations were slightly higher for elementary compared to middle or high school students. The total DSECS-S score also correlated significantly and positively with school-level academic achievement and correlated significantly and negatively with suspensions/expulsions. The DSECS-S score correlated .69 with English Language Arts, .65 with math, and -.60 with suspensions/expulsions in elementary schools; .63, .58, and -.62, respectively, in middle schools; and .59, .71, and -.76, respectively, in high schools.

4 Discussion The purpose of this study was to develop the DSECS-S, confirm the scale’s factor structure and its invariance across student groups, and provide evidence of reliability and criterion-related validity. CFA supported a second-order factor structure comprised of four lower order factors and one higher order factor with configural, weak factorial, and strong invariance found across three grade levels, five racial/ethnic groups, and gender. Although coefficients of internal reliability for individual subscales were below the minimally accepted criterion due to the limited number of items per subscale, the alpha coefficient of .84 for the total scale was satisfactory, thus providing evidence of the total scale’s reliability. Therefore, it is recommended that scores only be reported for the total social-emotional competency scale and not the individual subscales. Student-level social-emotional competence was positively correlated with school engagement, which aligns with studies previously described (Kwon et al. 2012; Perdue et al. 2009). Thus, results indicate that students with greater social decision making, relationship, self-management, and social awareness skills are more engaged in school. As noted earlier, correlations were stronger for elementary students compared to middle or high school students, which suggests greater variability in social-emotional competence for secondary students. This study also showed school-level aggregated socialemotional competence to be correlated positively with academic achievement and negatively with suspensions/expulsions. As such, schools characterized by students reporting greater social decision making, relationship, self-management, and social awareness skills have greater overall academic achievement and fewer suspensions/ expulsions. This finding is consistent with studies showing a positive association of social-emotional competence with students’ academic achievement (Malecki and Elliot 2002; Wentzel 1993) and studies showing that students with greater social-emotional Table 6 Correlations between DSECS-S total score and student engagement Elementary

Middle and High

Full Sample

Behavioral Engagement

.592

.582

.599

Cognitive Engagement

.466

.461

.484

Emotional Engagement

.479

.406

.426

Total Engagement

.615

.543

.587

Note. All correlations are significant at p < 0.001

L.S. Mantz et al.

competence display fewer behavior problems (Bornstein et al. 2010). Interestingly, the correlation between social-emotional competence and suspensions/expulsions was stronger at the middle and high school levels compared to the elementary school level. Future research should examine what factors, age-related and other [e.g., socioeconomic status (SES), relations and support from teachers, peers, and home], might account for these differences. Given that individual-level SES data were unavailable in the current study, this potential impact could not be examined. Regardless, the current study supports the validity of DSECS-S scores when used to predict school engagement, achievement, and suspensions. The DSECS-S was developed due to a lack of assessment instruments that are (a) aligned with the social-emotional competencies identified by CASEL (2012), (b) can be completed by students in elementary school through high school, (c) are inexpensive to administer, and (d) require a small amount of time to administer. Because the DSECS-S accounts for these limitations, the instrument serves as a practical and useful tool for educators and researchers in their SEL program development and evaluation efforts. Administering this instrument school-wide can help educators understand the degree to which students report demonstrating important social-emotional competencies. Schools finding that students report they are lacking social-emotional competencies should consider increasing or strengthening their SEL programming efforts. Beyond examining the data at a school-wide level, schools also should consider using DSECS-S scores to examine particular groups of students (e.g., students at a particular grade) who report lower levels of competency and subsequently deliver additional supports to foster students’ skills in these areas, such as providing small group SEL instruction, increasing the frequency of SEL lessons, implementing booster SEL lessons, or including these students in other social-emotional development programs. Although the DSECS-S provides schools with a practical and useful tool for assessing students’ social-emotional competence, its limitations should be noted, as well as limitations of the study. First, the DSECS-S is a self-report instrument that relies upon students’ perceptions of their social-emotional competence. A primary limitation of self-reports is informant bias, particularly social desirability bias, and it is unknown if social desirability influenced students’ ratings of their social-emotional competence. Unbiased observations of students and classrooms would be ideal for a valid assessment of these competencies. However, student and classroom observations present their own limitations, including the effect of observers or cameras on students and teachers, as well as the cost of conducting observations and coding data. Moreover, although observations may provide a more objective and accurate evaluation of students’ social-emotional competence, research shows that behavior is often influenced more by one’s perceptions of the environment than by reality (Bandura 1986, 1997; Bronfenbrenner 1979). A second limitation to the DSECS-S is the number of items contributing to each subscale. Although the short length of each subscale makes it brief and quick to complete and thereby only takes a limited amount of instructional time for students to finish, having only three items per subscale resulted in lower subscale reliability coefficients than commonly recommended (i.e., above .70). As a result, scores should only be reported for overall social-emotional competency and not the individual subscale scores. Schools using the DSECS-S would therefore only be able to obtain information on their students’ general social-emotional competency. This prevents schools from seeing the relative strengths and weaknesses of students in separate social-emotional domains.


The sampling procedure used in the study also could be viewed as a limitation. Despite schools in the study greatly ranging in their percent of students qualifying for free or reduced lunch (i.e., a proxy for measuring SES), and including urban, suburban, and rural schools, all schools were located in one state in the mid-Atlantic region of the United States, Delaware. Schools volunteered for participation in the survey rather than being randomly assigned, which also may have affected the survey’s sample. Additionally, although schools were provided with guidance on how to randomly select students within the school to complete the study, the exact methods used by schools to sample students is unknown. Therefore, one could argue that the students included for participation may not have been a truly random sample of public school students in Delaware. However, given the similar racial/ethnic composition of the study’s participants and students overall in Delaware, it seems likely that the students in the study were a representative sample. The DSECS-S includes items assessing four of the social-emotional competencies described by CASEL (2012), while excluding the fifth competency of self-awareness. This may be perceived as a limitation. However, this competency was omitted for what we believe are five good reasons. Firstly, assessing students’ emotional well being and self-esteem would require a higher level of parental consent than is typically required by the DSS. Therefore, the inclusion of this competency in the DSECS-S measure would have substantially decreased the student sample size in the study. Secondly, previous research has suggested that programs targeting self-esteem and self-concept often are ineffective in improving these outcomes (Baumeister et al. 2003; Davies and Brember 1999; Hoge et al. 1995; Manning et al. 2006). Because the self-awareness construct includes both self-esteem and self-concept, it does not seem constructive to provide schools with information about student competencies that they are unlikely to change. Thirdly, the self-awareness competency was excluded for possible ethical reasons. That is, one might well argue that if schools identify students with low self-esteem or high depressive symptoms, they also should provide those students with additional mental health services—services not readily available in many schools. Fourthly, although skills in recognizing or identifying emotions (i.e., skills that are included in the self-awareness domain) are typically included in State SEL standards in preschool and early elementary school, they are seldom included afterwards (e.g., Zinsser et al. 2013). Because the DSS surveys students in third grade and above and uses the same items across all grade levels, it would not be very useful to ask high school students if they recognize or are aware of emotions such as anger or joy. Fifthly, the decision to exclude items assessing emotional awareness is supported by others who collapse the five general social emotional competencies into fewer ones and exclude emotional awareness. For example, the State SEL standards for Illinois and Pennsylvania target three general social emotional competencies or domains: self-awareness and self-management; social-awareness and interpersonal relationships; and decision-making skills and responsible behavior (Zinsser et al. 2013). Examination of the standards specific to the self-awareness and selfmanagement domains shows that most standards for self-awareness appear in preschool and early elementary school. For example, recognizing and identifying emotions are skills that children are generally expected to develop before or during early elementary school. Thus, assessing these skills for children in third grade and above on the DSECS-S does not seem advantageous.

L.S. Mantz et al.

Future studies are needed to provide additional evidence of the criterion-related validity of the DSECS-S at the student level. Although correlations were calculated between students’ social-emotional competence, academic achievement, and suspensions/expulsions at the school level, providing evidence of these correlations at the student level would lend additional support for the scale. Correlations also should be examined between DSECS-S scores and other student behaviors and outcomes, such as internalizing and externalizing behaviors, relationship quality with peers and teachers, and peer acceptance. Future studies also are needed to support the scale’s discriminant validity (e.g., investigating if greater social-emotional competence levels discriminate between students and schools with high or low engagement, achievement, and suspensions/expulsions), convergent validity (e.g., correlating the DSECS-S with other social-emotional competency scales), and predictive validity (e.g., predicting changes in engagement or achievement over time). Finally, increasing the reliability of the subscales by adding items should be considered, especially if the scale is used to assess the four separate competencies, and when time constraints are not a primary concern. Despite its limitations, the DSECS-S has many advantages. It comes at no monetary cost to users, accounts for students’ perspectives, and is relatively brief in its number of items, which makes the measure useful to administer school-wide to students. Given these advantages, the DSECS-S should serve as a practical and useful tool for both educators and researchers seeking to increase students’ social-emotional competence. Acknowledgments This research was supported in part by a School Climate Transformation Grant awarded to the Delaware Department of Education by the United States Department of Education.

References Asparouhov, T., & Muthén, B. (2010). Weighted least squares estimation with missing data. Mplus Technical Appendix. Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory. Upper Saddle River, NJ: Prentice-Hall. Bandura, A. (1997). Self-efficacy: the exercise of control. New York: Freeman. Baumeister, R. F., Campbell, J. D., Krueger, J. I., & Vohs, K. D. (2003). Does high self-esteem cause better performance, interpersonal success, happiness, or healthier lifestyles? Psychological Science in the Public Interest, 4, 1–44. doi:10.1111/1529-1006.01431. Bear, G., Yang, C., Harris, A., Mantz, L., Hearn, S., & Boyer, D. (2016). Technical Manual for 2016 Delaware School Survey: Scales of School Climate; Bullying Victimization; Student Engagement; Positive, Punitive, and Social Emotional Learning Techniques; and the Delaware Social and Emotional Competencies Scale. Newark, DE: Center for Disabilities Studies. Benner, G. J., Beaudoin, K., Mooney, P., Uhing, B. M., & Pierce, C. D. (2008). Convergent validity with the BERS-2 teacher rating scale and the Achenbach teacher’s report form: a replication and extension. Journal of Child and Family Studies, 17, 427–436. doi:10.1007/s10826-007-9156-z. Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663. doi:10.1111 /j.1467-8624.2007.01019.x. Bornstein, M. H., Hahn, C.-S., & Haynes, O. M. (2010). Social competence, externalizing, and internalizing behavioral adjustment from early childhood through early adolescence: developmental cascades. Development and Psychopathology, 22, 717–735. doi:10.1017/S0954579410000416. Bradshaw, C. P., Sawyer, A. L., & O’Brennan, L. M. (2007). Bullying and peer victimization at school: perceptual differences between students and school staff. School Psychology Review, 36(3), 361–382.

The Delaware Social-Emotional Competency Scale... Bronfenbrenner, U. (1979). The ecology of human development. Cambridge: Harvard University Press. Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York: Guilford Publications. Carlo, G., Hausmann, A., Christiansen, S., & Randall, B. A. (2003). Sociocognitive and behavioral correlates of a measure of prosocial tendencies for adolescents. The Journal of Early Adolescence, 23, 107–134. doi:10.1177/0272431602239132. Chen, F. F., Sousa, K. H., & West, S. G. (2005). Teacher’s corner: testing measurement invariance of secondorder factor models. Structural Equation Modeling, 12, 471–492. doi:10.1207/s15328007sem1203_7. Cheng, H., & Furnham, G. (2002). Personality, peer relations, and self -confidence as predictors of happiness and loneliness. Journal of Adolescence, 25, 327–339. doi:10.1006/jado.2002.0475. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. doi:10.1207/S15328007SEM0902_5. Ciarrochi, J., Wilson, C. J., Deane, F. P., & Rickwood, D. (2003). Do difficulties with emotions inhibit helpseeking in adolescence? the role of age and emotional competence in predicting help-seeking intentions. Counselling Psychology Quarterly, 16, 103–120. doi:10.1080/0951507031000152632. Ciarrochi, J., Heaven, P. C., & Supavadeeprasit, S. (2008). The link between emotion identification skills and socio-emotional functioning in early adolescence: a 1-year longitudinal study. Journal of Adolescence, 31, 565–582. doi:10.1016/j.adolescence.2007.10.004. Cigala, A., Mori, A., & Fangareggi, F. (2014). Learning others’ point of view: perspective taking and prosocial behaviour in preschoolers. Early Child Development and Care, 185, 1199–1215. doi:10.1080 /03004430.2014.987272. Collaborative for Academic, Social, and Emotional Learning (CASEL). (2012). 2013 CASEL guide: effective social and emotional programs—Preschool and elementary. Chicago: Author. Connolly, J. A., & Konarski, R. (1994). Peer self-concept in adolescence: analysis of factor structure and of associations with peer experience. Journal of Research on Adolescence, 4, 385–403. doi:10.1207 /s15327795jra0403_3. Davies, J., & Brember, I. (1999). Reading and Mathematics attainments and self-esteem in years 2 and 6-an eight-year cross-sectional study. Educational Studies, 25, 145–157. doi:10.1080/03055699997873. Deković, M., & Meeus, W. (1997). Peer relations in adolescence: effects of parenting and adolescents’ selfconcept. Journal of Adolescence, 20, 163–176. doi:10.1006/jado.1996.0074. Denham, S. A. (2015). Assessment of SEL in educational contexts. In J. A. Durlak, C. E. Domitrovich, R. P. Weissberg, & T. P. Gullotta (Eds.), Handbook of social and emotional learning (pp. 285–300). New York, NY: Guilford. Denham, S.A., Ji, P., & Hamre, B. (2010). Compendium of preschool through elementary school social-emotional learning and associated assessment measures. Retrieved from http://static1.squarespace.com/static/513f79f9 e4b05ce7b70e9673/t/527815bbe4b057bbfd8adb2e/1383601595064/compendium-of-preschool-throughelementary-school-social-emotional-learning-and-associated-assessment-measures.pdf. Duckworth, A. L., Tsukayama, E., & Kirby, T. (2013). Is it really self-control? Examining the predictive power of the delay gratification task. Personality and Social Psychology Bulletin, 39, 843–855. doi:10.1177 /0146167213482589. Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). Enhancing students’ social and emotional development promotes success in school: results of a meta-analysis. Child Development, 82, 405–432. doi:10.1111/j.1467-8624.2010.01564.x. Dusenbury, L., Weissberg, R. P., Goren, P., & Domitrovich, C. (2014). State standards to advance social and emotional learning: findings from casel’s state scan of social and emotional learning standards, preschool through high school. Chicago: Collaborative for Academic, Social, and Emotional Learning. Eisenberg, N., Zhou, Q., & Koller, S. (2001). Brazilian adolescents’ prosocial moral judgment and behavior: relations to sympathy, perspective taking, gender‐role orientation, and demographic characteristics. Child Development, 72, 518–534. doi:10.1111/1467-8624.00294. Eisenberg, N., Fabes, R. A., & Spinrad, T. L. (2006). Prosocial behavior. In W. Damon, R. M. Learner, & N. Eisenberg (Eds.), Handbook of child psychology: Vol. 3. Social, emotional, and personality development (6th ed., pp. 646–718). New York: Wiley. Eisenberg-Berg, N., & Mussen, P. (1978). Empathy and moral development in adolescence. Developmental Psychology, 14, 185–186. doi:10.1037/00121649.14.2.185. Elliot, S. N., Frey, J. R., & Davies, M. (2015). Systems for assessing and improving students’ social skills to achieve academic competence. In J. A. Durlak, C. E. Domitrovich, R. P. Weissberg, & T. P. Gullotta (Eds.), Handbook of social and emotional learning (pp. 301–319). New York: Guilford. Fitzgerald, D. P., & White, K. J. (2003). Linking children’s social worlds: perspective-taking in parent-child and peer contexts. Social Behavior and Personality, 31, 509–522. doi:10.2224/sbp.2003.31.5.509.

L.S. Mantz et al. Graziano, P., Reavis, R., Keane, S., & Calkins, S. (2007). The role of emotion regulation and the studentteacher relationship in children’s academic success. Journal of School Psychology, 45, 3–19. doi:10.1016 /j.jsp.2006.09.002. Gresham, F., & Elliott, S. N. (2007). Social skills improvement system (SSIS) rating scales. San Antonio: Pearson Education Inc. Haggerty, K., Elgin, J., & Woolley, A. (2011). Social-emotional learning assessment measures for middle school youth. Seattle: Social Development Research Group, University of Washington. Retrieved from http://raikesfoundation.org/Documents/SELTools.pdf. Harter, S. (2006). The self. In W. Damon, R. M. Learner, & N. Eisenberg (Eds.), Handbook of child psychology: Vol. 3. Social, emotional, and personality development (6th ed., pp. 505–570). New York: Wiley. Hattie, J. (1992). Self-concept. Hillsdale: Lawrence Erlbaum Associates. Hoge, D. R., Smit, E. K., & Crist, J. T. (1995). Reciprocal effects of self-concept and academic achievement in sixth and seventh grade. Journal of Youth and Adolescence, 24, 295–314. doi:10.1007/BF01537598. Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. doi:10.1037/1082989X.3.4.424. Jones, S. M., & Bouffard, S. M. (2012). Social and emotional learning in schools: from programs to strategies. Social Policy Report. Volume 26, Number 4. Society for Research in Child Development. Kwon, K., Kim, E. M., & Sheridan, S. M. (2012). A contextual approach to social skills assessment in the peer group: who is the best judge? School Psychology Quarterly, 27, 121–133. doi:10.1037/a0028696. LeBuffe, P. A., Shapiro, V. B., & Naglieri, J. A. (2009). Devereux student strengths assessment. Lewisville: Kaplan Early Learning Company. Li, X., Bian, C., Chen, Y., Huang, J., Ma, Y., Tang, L., & Yu, Y. (2015). Indirect aggression and parental attachment in early adolescence: examining the role of perspective taking and empathetic concern. Personality and Individual Differences, 86, 499–503. doi:10.1016/j.paid.2015.07.008. Luckner, A. E., & Pianta, R. C. (2011). Teacher–student interactions in fifth grade classrooms: relations with children’s peer behavior. Journal of Applied Developmental Psychology, 32, 257–266. doi:10.1016/j. appdev.2011.02.010. Malecki, C. K., & Elliot, S. N. (2002). Children’s social behaviors as predictors of academic achievement: a longitudinal analysis. School Psychology Quarterly, 17, 1–23. doi:10.1521/scpq.17.1.1.19902. Manning, M. A., Bear, G. G., & Minke, K. M. (2006). Self-concept and self-esteem. In G. G. Bear & K. M. Minke (Eds.), Children’s needs III: development, prevention, and intervention (pp. 341–356). Bethesda: National Association of School Psychologists. Mashburn, A. J., Pianta, R. C., Barbarin, O. A., Bryant, D., Hamre, B. K., Downer, J. T., & Howes, C. (2008). Measures of classroom quality in prekindergarten and children’s development of academic, language, and social skills. Child Development, 79, 732–749. doi:10.1111/j.1467-8624.2008.01154.x. McKown, C., Russo-Ponsaran, N. M., Johnson, J. K., Russo, J., & Allen, A. (2015). Web-based assessment of children’s social-emotional comprehension. Journal of Psychoeducational Assessment. doi:10.1177 /0734282915604564. Advance online publication. Miller, P. A., & Eisenberg, N. (1988). The relation of empathy to aggressive and externalizing/antisocial behavior. Psychological Bulletin, 103, 324–344. doi:10.1037/0033-2909.103.3.324. Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes: a meta-analytic investigation. Journal of Counseling Psychology, 18, 30–38. doi:10.1037/00220167.38.1.30. Muthén, L.K., & Muthén, B.O. (1998-2015). Mplus user’s guide (Seventh edition). Los Angeles, CA: Muthén & Muthén. National School Climate Center. (2016). The comprehensive school climate inventory (CSCI). Retrieved from http://www.schoolclimate.org/programs/csci.php. Newcomb, A. F., Bukowski, W. M., & Pattee, L. (1993). Children’s peer relations: a meta-analytic review of popular, rejected, neglected, controversial, and average sociometric status. Psychological Bulletin, 113, 99–128. doi:10.1037/0033-2909.113.1.99. Ongley, S. F., Nola, M., & Malti, T. (2014). Children’s giving: moral reasoning and moral emotions in the development of donation behaviors. Frontiers in Psychology, 5, 458. doi:10.3389/fpsyg.2014.00458. Pakaslahti, L., Karjalainen, A., & Keltikangas-Järvinen, L. (2002). Relationships between adolescent prosocial problem-solving strategies, prosocial behavior, and social acceptance. International Journal of Behavioral Development, 26, 137–144. doi:10.1080/01650250042000681. Perdue, N. H., Manzeske, D. P., & Estell, D. B. (2009). Early predictors of school engagement: exploring the role of peer relationships. Psychology in the Schools, 46, 1084–1097. doi:10.1002/pits.20446.

The Delaware Social-Emotional Competency Scale... Pettit, G. S., Dodge, K. A., & Brown, M. M. (1988). Early family experience, social problem solving patterns, and children’s social competence. Child Development, 59, 107–120. doi:10.2307/1130393. Pintrich, R. R., & DeGroot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33–40. doi:10.1037/0022-0663.82.1.33. Pro-Ed Inc. (2012). Behavioral and emotional rating scale–second edition (BERS-2). Retrieved from http://www.proedinc.com/customer/productView.aspx?ID=3430. Rigby, K., & Slee, P. T. (1991). Bullying among Australian school children: reported behavior and attitudes to victims. Journal of Social Psychology, 11, 615–627. doi:10.1080/00224545.1991.9924646. Romer, D., Duckworth, A. L., Sznitman, S., & Park, S. (2010). Can adolescents learn self-control? delay of gratification in the development of control over risk taking. Prevention Science, 11, 319–330. doi:10.1007 /s11121-010-0171-8. Roorda, D. L., Koomen, H. M. Y., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher-student relationships on students’ school engagement and achievement: a meta-analytic approach. Review of Educational Research, 81, 493–529. doi:10.3102/0034654311421793. Schonert-Reichl, K. A. (1999). Relations of peer acceptance, friendship adjustment, and social behavior to moral reasoning during early adolescence. The Journal of Early Adolescence, 19, 249–279. doi:10.1177 /0272431699019002006. Schultz, D., Izard, C. E., & Bear, G. (2004). Children’s emotion processing: relations to emotionality and aggression. Development and Psychopathology, 16(02), 371–387. doi:10.1017/S0954579404044566. Seals, D., & Young, J. (2003). Bullying and victimization: prevalence and relationship to gender, grade level, ethnicity, self-esteem, and depression. Adolescence, 38(152), 735–747. Search Institute. (2014). Developmental assets profile (DAP). Retrieved from http://www.search-institute. org/surveys/dap. Sklad, M., Diekstra, R., De Rittter, M., Ben, J., & Gravesteijn, C. (2012). Effectiveness of school-based universal social, emotional, and behavioral programs: do they enhance students’ development in the area of skill, behavior, and adjustment? Psychology in the Schools, 49, 892–909. doi:10.1002/pits.21641. Sowislo, J. F., & Orth, U. (2013). Does low self-esteem predict depression and anxiety? a meta-analysis of longitudinal studies. Psychological Bulletin, 139, 213–240. doi:10.1037/a0028931. Stockdale, M. S., Hangaduambo, S., Duys, D., Larson, K., & Sarvela, P. D. (2002). Rural elementary students’, parents’, and teachers’ perceptions of bullying. American Journal of Health Behavior, 26, 266–277. doi:10.5993/AJHB.26.4.3. Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72, 271–324. doi:10.1111 /j.0022-3506.2004.00263.x. Wentzel, K. R. (1993). Does being good make the grade? social behavior and academic competence in middle school. Journal of Educational Psychology, 85, 357–364. doi:10.1037/0022-0663.85.2.357. Zins, J. E., & Elias, M. J. (2006). Social and emotional learning. In G. G. Bear & K. M. Minke (Eds.), Children’s needs III: development, prevention, and intervention (pp. 1–13). Bethesda: National Association of School Psychologists. Zins, J., Bloodworth, M., Weissberg, R., & Walberg, H. (2004). The scientific base linking social and emotional learning to school success. In J. Zins, R. Weissberg, M. Wang, & H. J. Walberg (Eds.), Building academic success on social and emotional learning: what does the research say? (pp. 1–22). New York: Teachers Press, Columbia University. Zinsser, K. M., Weissberg, R. P., & Dusenbury, L. (2013). Aligning preschool through high school social and emotional learning standards: A critical (and doable) next step. Chicago: Collaborative for Academic, Social and Emotional Learning.