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. Author manuscript; available in PMC: 2021 Dec 1.
Published in final edited form as: Child Youth Serv Rev. 2020 Oct 27;119:105623. doi: 10.1016/j.childyouth.2020.105623

Racial Equity in Academic Success: The Role of School Climate and Social Emotional Learning

Tiffany M Jones 1, Charles Fleming 2, Anne Williford 3; Research and Evaluation Team of Seattle Public Schools
PMCID: PMC7731917  NIHMSID: NIHMS1641360  PMID: 33311826

Abstract

Many schools and school districts have put significant effort into improving school climate and the social emotional learning (SEL) of students, as they have been shown to be related to improved academic success. Yet, little is known about whether these efforts will contribute to or ameliorate racial differences in academic outcomes. In a series of structural equation models adjusting for school clustering, this study examined mediating and moderating effects of student perceptions of school climate and their own SEL on race differences in self-reported grades. Asian and Latinx students reported slightly more positive perceived school climate while Multiracial students reported significantly less positive perceived school climate compared to White students. Compared to their White peers, all racial groups reported lower levels of SEL. Significant but small indirect standardized effects of race on grades through social emotional competence but not school climate suggest that SEL partially mediates the relationship of race with grades. The association of SEL with grades was significantly stronger for White students compared to other racial groups; the standardized effect size of the association was nearly twice as large for White students as for Black and Native students.

Keywords: Racial inequity, social emotional learning, school climate, achievement gap

Racial inequities in student performance are the outcome of a long history of the “education debt” whereby students of color have been systematically denied access to equal education through a variety of mechanisms: students of color attend schools with fewer resources, are more likely to be exposed to exclusionary discipline, and are less likely to be tracked into advanced classes (Carter, Skiba, Arredondo, & Pollock, 2016; Ladson-Billings, 2006). The education debt has manifested in racial inequities in students’ level of academic success, a long-standing problem in American schools. Historically, with the exception of students of East Asian descent, students of color have consistently experienced lower academic performance than their White counterparts (Kao & Thomson, 2003; Lee, 2002; Nitardy, Duke, Pettingell, & Borowsky, 2014). Lower educational attainment has long lasting effects over the life course, ranging from worse health outcomes (Ross & Wu, 1995) to lower income and less upward social mobility (Baum, Ma, & Payea, 2013), subsequently continuing intergenerational cycles of disadvantage. In this study, we attribute racial inequities in education outcomes to be the result of system failure, systemic racism, and the long history of the education debt. We problematize the language of achievement gaps, instead opting for the language of racial inequities. In doing so, we avoid contributing to deficit narratives that have been too often used to describe students of color.

Evidence of the education system failing students of color can be found across all markers of achievement, from test scores and grades to course-taking in high school, high school graduation, and educational attainment (Kao & Thomson, 2003). Federal programs and policies such as No Child Left Behind and Head Start have expressly aimed to reduce the inequities in academic success among students from different racial groups, but the gaps remain largely intractable (Paschall, Gershoff, & Kuhfeld, 2018). Given the extent of racial disparities in American schools and the long-standing education debt for students of color, it is important that schools and school districts understand the potential for oft-targeted levers of change – social emotional learning (SEL) and school climate – and the school-based interventions that target these levers can have on racial equity in student success. School climate and SEL represent intervention targets toward which schools and school districts are increasingly devoting instructional time and resources at all grade levels. These system-level or school-wide decisions may have important ramifications for racial equity in student academic success. Yet these programs, and the vast resources that they require, are rarely examined for their differential impact on racial (in)equity, and thus on the education debt.

Current research suggests that strategies to improve school climate (Voight, Hanson, O’Malley, & Adekanye, 2015) and individual SEL (Elias, White, & Stepney, 2014) could also improve the academic performance of students of color, thus contributing to more racially equitable academic outcomes. There are many research-based strategies to enhance school climate (for a review see Voight & Nation, 2016) and SEL (for a review see: CASEL, 2013, 2015). School climate is associated with academic success in elementary, middle and high school (Berkowitz, Moore, Astor, & Benbenishty, 2016; Wang & Degol, 2016), and though inconsistent, there is some evidence that suggests SEL is linked to academic success across these grade levels (Domitrovich, Durlak, Staley, & Weissberg, 2017; Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011; Elias, 2006). Yet, little is known about whether improvements in school climate and SEL contribute to racial equity in academic outcomes (Voight et al., 2015; Gregory & Fergus, 2017). Toward that end, the present study examines 1) race inequities in perceptions of school climate and SEL; 2) whether perceptions of school climate and SEL account for the relationship between race and academic success; and 3) whether school climate and SEL relate similarly to academic success for students of different racial groups. Importantly, we present the analysis contained in this study through a critical lens in order to evaluate associations between student reports of their SEL, their perceptions of the school climate and their grades.

School Climate

School climate refers to the “social, emotional and physical characteristics of a school community” (Voight & Hanson, 2017), and offers a window to understand students’ experience of school. From the students’ perspective, school climate is generally thought of as the collective experiences of students at a school that characterize the social environment of the school (Cohen McCabe, Michelli, & Pickeral, 2009). While many frameworks exist for defining and operationalizing school climate (Rudasill, Snyder, Levinson, & Adelson, 2017), school climate captures a range of different aspects of students’ school experiences, generally including relationships among adults and students and between students, students’ sense of physical and emotional safety, students’ sense of belonging to school, and students’ perspective on the quality of instruction (Wang & Degol, 2016; Cohen et al., 2009; Thapa, Cohen, Guffey, & Higgins-D’Alessandro, 2013). Positive school climates provide the necessary conditions for learning in elementary, middle, and high school (Cohen et al., 2009; Garibaldi, Ruddy, Kendziora, & Osher, 2015; Wang & Degol, 2016).

Multiple empirical studies and comprehensive reviews have found that school climate is positively associated with academic outcomes across grade levels (Bear, Gaskins, Blank, & Chen, 2011; Berkowitz et al., 2016; Kwong & Davis, 2015; Shukla, Konold, & Cornell, 2016; Thapa et al., 2013; Voight & Hanson, 2017; Wang & Degol, 2016). Findings from a comprehensive review indicated that school climate moderates the association of socioeconomic status and academic achievement such that the gap in achievement between low and high-income students was smaller in schools with more positive climates (Berkowitz et al., 2016). Positive school climates have also been associated with a number of other important student outcomes known to influence student achievement, ranging from reduced student psychopathology and reduced problem behaviors, to reduced school disorder (Gottfredson, Gottfredson, Payne, & Gottfredson, 2005; Wang & Degol, 2016; Way, Reddy, & Rhoades, 2007).

Although school climate is thought of as a characteristic of schools, evidence suggests that students within schools may have different experiences of the same school based on their race (Voight et al., 2015; Shirley & Cornell, 2011; De Pedro, Gilreath, & Berkowitz, 2015). School climate measures may capture racial inequities in students’ school experience, but only when disaggregated by race (Voight et al., 2015; Shirley & Cornell, 2011). For example, in studies of secondary school students, racial inequities have been found in various domains of school climate, where students of color feel less supported by their relationships with adults, have a lower sense of belonging, and feel that schools do not treat them fairly compared to their White peers (Anyon, Zhang, & Hazel., 2016; Bottiani, Bradshaw, & Mendelson, 2017). Numerous studies across grade levels have documented differences in perceptions of school climate based on students’ race, with students of color generally reporting worse school climate compared to White students (Anyon et al., 2016; Bottiani et al., 2017; De Pedro et al., 2015; Konold, Cornell, Shukla, & Huang, 2016; Koth, Bradshaw, & Leaf, 2008; Shirley & Cornell, 2011; Shukla et al., 2016; Voight et al., 2015). Only one study was found that investigated the association between racial disparities in student perceptions of school climate and race inequities in achievement. Voight and colleagues (2015) reported that racial differences in students’ perception of school climate was associated with racial inequities in grades. The association of school climate and racial disparities was larger in higher income schools.

Despite the many studies that have explored race inequities in school climate, there are important gaps in this literature. We found no studies that explored whether school climate is associated with academic outcomes to the same extent for students from different racial groups. It may be that race moderates this association, with climate having a stronger or weaker relationship on academic outcomes for some racial groups (Konold et al., 2016). Although most studies in this area have found differences in perceptions of school climate comparing Black and Latinx students to White students across grade levels, we found no studies that examined the experiences of Multiracial, Asian, Pacific Islander, or Native American students.

Social Emotional Learning

School climate creates the social conditions for learning and applying social and emotional skills (Berg, Osher, Moroney, & Yoder, 2017). Consequently, SEL interventions have been recognized as an evidence-based method for improving school climate (Voight & Nation, 2016). SEL is the broad term used to describe the collection of skills beyond academic skills that are necessary for students to succeed in school. SEL has been broadly categorized into three interrelated sets of skills: cognitive processes, emotional processes, and interpersonal skills (Jones & Bouffard, 2012). Cognitive processes include the ability of students to shift mindsets and positive attitudes toward learning and their ability to learn, as well as executive functioning and attention skills. Emotional processes include emotion and behavioral regulation, emotion knowledge, and having empathy for others. Interpersonal skills include the ability to develop strong peer and teacher relationships, based on students’ skills at observing and responding to social cues, understanding emotions in others, and having positive social interactions (Jones & Bouffard, 2012). Notably, it is theorized that these SEL processes are consistent across developmental time periods (see CASEL, 2020; Panayiotou, Humphrey, & Wigelsworth, 2019), despite differences in developmental milestones present in childhood and adolescence. As a result, SEL interventions have been tailored to different grade levels; however, the core competencies targeted across grade levels remain the same. In addition, it is beneficial for SEL to be defined similarly across grade levels so that students receive consistent messages as they progress through different grade levels (Author, 2018). This consistency also aids the district by making reporting procedures consistent across schools, allowing for student SEL growth to be monitored over time. Here we focus on two related sets of skills representing sub-constructs of SEL-- social emotional competence (SEC) and learning mindset. These constructs are targets of specific intervention in many of the participating schools in the present study.

Social emotional competence is defined as the “knowledge, skills and attitudes … to understand and manage emotions, set and achieve positive goals, feel and show caring and concern for others, establish and maintain positive relationships, and make responsible decisions” (Weissberg et al., 2015, p. 6). The Collaborative on Social Emotional Learning (CASEL), a national organization devoted to policy and practice of SEL, has defined and operationalized five social emotional competencies, which include relationship skills, social awareness, self-awareness, self-management, and responsible decision-making (Weissberg, et al., 2015). Learning mindsets are defined as the “psychosocial attitudes or beliefs that one has about oneself in relation to academic work” (Farrington et al., 2012, p. 9) and are a crucial aspect of SEL specific to school success. Students with a positive learning mindset (also known as academic mindset) believe that their skills and intelligence can be developed with effort over time and, because of this, persist when faced with challenges and see effort as the path towards learning (Dweck, 2006). Learning mindset is a characteristic of students important to consider alongside SEC, as it characterizes the attitudes central to learning that motivate students’ self-management and decision-making with regard to their academic focus.

While there are multiple theoretical and conceptual studies describing the ways that SEL is essential for academic success (Elias, 2006; Jones & Bouffard, 2012; Osher, Kidron, & Brackett, 2016; Zins et al., 2004), the empirical evidence linking SEL with academic success is modest (Leighton, Guo, Chu, & Tang, 2018). Most evidence as to the connection between SEL and academics comes from intervention studies; Durlak and colleagues (2011) conducted a meta-analysis of SEL interventions and found that SEL interventions significantly improved academic performance. There are few empirical investigations of the association of student SEL and academic achievement, despite the large body of SEL-focused literature (Jones & Bouffard, 2012; Leighton et al., 2018). Inconsistent definitions of SEL have made it difficult to draw strong conclusions about the connection between SEL and academic outcomes.

While researchers have theorized that SEL may contribute to more racially equitable academic outcomes (Elias et al., 2014; Elias & Moceri, 2012), there is little empirical research examining whether the relationship between SEL and academics is of similar strength for students from different racial backgrounds. In addition, there is little empirical research as to the extent to which SEL is equally meaningful across racial groups. Garner and colleagues (2014) investigated whether students from different racial groups have different levels of SEC and found mixed results. For example, in one study cited in their review, Black students were found to have less SEC compared to White students (Elias & Haynes, 2008), while in another, no differences in prosocial behavior were found between Black and White students (Kistner, Metzler, Gatlin, & Risi, 1993), and in a third, students of color were found to have higher levels of prosocial competence (Garner, 2006). In a review, Rowe and Trickett (2017) examined the extent to which race moderates the intervention effects of SEL programs among students in kindergarten through 12th grade and found that there is not enough evidence to draw conclusions either way. Garner and colleagues (2014) found similar results with respect to SEL interventions and race – that there is not enough information available to know whether and to what extent SEL-focused interventions have similar effects across racial groups. In sum, there is modest empirical evidence linking SEL and academic outcomes, and even less evidence investigating race and its associations with SEL and academics.

While some researchers have written about how improving social and emotional competencies could contribute to more racially equitable academic outcomes (Elias et al., 2014), there is very little empirical evidence exploring this connection. Only one study was found that has tested this question empirically. Chain and colleagues (2017) found that teacher-rated SEC was related to academic achievement for all participants in their sample of 3rd through 8th grade students but was more strongly associated with academic achievement for Native students compared to White students and other students of color. This finding suggests that improving SEC might be more important for Native American students compared to White students or other students of color. However, student SEC was rated by their teachers who were predominately White. Moreover, researchers have questioned whether the underlying value system of SEL is relevant to the same extent for racial and ethnic minority students of color (Hoffman, 2009; Gregory & Fergus, 2017), and can in fact contribute to harmful narratives about students of color (Kaler-Jones, 2020; Simmons, 2017). SEL without a grounding in cultural difference and disconnected from systems of power, privilege and oppression is essentially colorblind (Gregory & Fergus, 2017) and can be used to label students whose behavior does not fit the implicit White cultural values of SEL as having SEL deficits (Kaler-Jones, 2020; Simmons, 2017). As such, is it critically important that research explore whether SEL as traditionally conceptualized finds racial differences that contribute to deficit narratives, and whether it is related to academic outcomes to the same extent for students from different racial groups.

The Present Study

The setting of the present study is a large urban public school district that serves more than 50,000 students. Evidence of the education debt is present in this school district, which was the fifth most unequal school district in academic achievement (as measured by test scores) in the nation in 2016 (Reardon et al., 2017). In this district, 86% of White students graduate in 4 years or less, compared to 83% of Asian students, 79% of Multiracial students, 79% of Pacific Islander students, 74% of African students, 70% of African American students, 64% of Latinx students, and 48% of Native American students ([PSD], 2017). Across the district, students furthest from educational justice (Black, Latinx, Native American, and Pacific Islanders) are suspended or expelled two to seven times more often than White students (Morton, 2018; Reardon et al., 2017; [PSD], 2017). The district is keenly focused on improving racial equity and is eager to study potential intervention targets within its locus of control. Since there are many school-based interventions with evidence of improving academic achievement focused on school climate and student SEL, the district is especially interested in learning about the possibility that these factors could contribute to racial equity.

Race is a social construct significantly impacting students of color’s school experience (Carter et al., 2016). We refer to students of color collectively throughout this paper in order to recognize that students from marginalized racial groups have different school experiences compared to those in the dominant racial group. This study is limited to the seven racial groups required for reporting to the federal government – Asian, Black, Latinx, Multiracial, Native American, Pacific Islander, and White. Despite the problems of categorization, it is imperative that researchers examine the differences in school experience for different groups of students of color using these categories as the best option available. The potential consequences of not doing so – implementing programs that worsen racial disparities – necessitate using these categories to empirically investigate whether and to what degree students’ perceptions of school climate or students’ SEL is related to racial inequities in academic outcomes.

We first examined whether there were racial inequities in students’ experiences of school as measured by school climate and whether there are racial inequities in student self-reports of SEL (research question 1). Racial inequities in school climate and SEL might, in turn, be related to race inequities in grades (research question 2). To test this, we examined the extent to which these factors explain the association between race and grades. Evidence for partial mediation would point to the degree that racial inequities in school climate and SEL account for racial inequities in grades (i.e., racial equity). The magnitude of the effect of racial inequities on SEL and school climate is represented by the indirect effects in a mediation model. Third, we investigated the extent to which the association between school climate and SEL varies based on students’ race (research question 3).

Method

Sample and Procedures

All students in grades 3 through 12 in the district’s 97 schools were invited to take the survey in 2016. The student survey consists of 51 items and takes about 10 minutes for most students to complete. The survey is administered every year in the Spring to students during class time by teachers or school staff. Schools are provided a protocol to read aloud to students about the survey, and each school determines the survey administration procedures. Schools select whether they want to take the survey on paper or on the computer. Seventy-one percent of the 41,430 students enrolled in 3rd through 12th grade took the survey in 2016, for a total sample size of 29,415 students. Students who responded to the survey are representative of the school district population, and questions on student race and gender were included on the survey. Demographic information is reported in Table 1. Students in the district originate from 149 countries and speak 146 different languages. This study was a secondary data analysis of data collected by the school district, and was deemed “not research” by the overseeing university’s Institutional Review Board.

Table 1.

Demographic characteristics of the sample

Race % N
Asian 16% 4706
Black 12% 3,530
Latinx 2% 588
Multiracial 17% 5,001
Native American 1.50% 441
Pacific Islander 2% 588
White 43% 12,648
Gender
Female 47.40% 13,913
Male 46.30% 13,619
Decline to State 6% 1883
Meets criteria for Free and Reduced Lunch (FRL)
FRL 34% 10,001
Grades
Mostly A’s 43.90% 12,923
Mostly B’s 33.70% 9,906
Mostly C’s 8.40% 2,471
Mostly D’s or E’s 2.30% 678
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Measures

The measures were originally created by the school district but have been subsequently validated in other studies (Author, 2018). These analyses determined that the survey shows evidence of construct validity, internal consistency and invariance across race, gender, and language spoken at home. Items were drawn from existing open source school climate and SEL measures, and additional details about the analysis and survey development process can be found elsewhere (Author, 2018). School climate consisted of 6 subscales: (1) healthy community, (2) belonging, (3) classroom environment, (4) school safety, (5) pedagogical effectiveness, and (6) motivation and inclusion. Response options for school climate items are “strongly disagree,” “disagree,” “neither agree nor disagree,” “agree,” and “strongly agree.” An example item from the safety subscale was “I feel safe at my school”. An example item from the classroom environment subscale is “Students in my class are focused on learning.” SEL was measured with two subscales: SEC and learning mindset. Examples of items in the learning mindset scale include “I can do most things if I try” and “I work hard to learn at school.” Examples of items in the SEC scale include “I am aware of my moods and feelings” and “I think before I act.” The response options for this set of scales are “Not like me at all,” “Not much like me,” “Somewhat like me,” “Mostly like me,” and “Very much like me.” SEL and school climate scales were adjusted to be non-invariant across race, gender, and home language. Scale measurement structure was confirmed with an adequate fitting confirmatory factor analysis (RMSEA= 0.042; CFI= 0.926; TLI=0.919), and scales were found to be internally consistent, with Cronbach’s alpha ranging from .72 to .87. Self-reported grades were the primary outcome. Modeled as ordered categorical, students selected whether they had “Mostly A’s,” “Mostly B’s,” “Mostly C’s,” or “Mostly D’s or E’s.” Race was self-selected by students from the list of seven racial categories determined by the federal government as necessary for reporting purposes (U.S. Department of Education, 2008): Asian, Black, Latinx, Multiracial, Native American, Pacific Islander, or White. Dummy variables for race were included in all models with White as the referent category. Schools were the primary grouping variable and students from 97 schools across the district responded to the survey. Students filled out the survey in the school at which they were enrolled in April 2016. All schools were included in the analysis: 60 elementary schools, 10 middle schools, 10 K-8 schools, 15 high schools, and 2 alternative schools. Students also self-reported their grade level (3–12) and gender (male, female, or decline to state gender). These variables were included as covariates.

Analysis Plan

Model strategies are described below according to research questions. All models were adjusted for students being nested in schools using the type=complex function in Mplus v7.4 (Muthén & Muthén, 2015) that adjusts standard errors for non-independence of students attending the same school (Muthén & Santorra, 1995). The outcome variable of self-reported grades was modeled as ordered categorical, necessitating a Weighted Least Squares Mean and Variances (WLSMV) estimator. Missing data were handled using Full-Information Maximum Likelihood (FIML) in Mplus. All models also included race as a series of dummy variables with White being the referent category. In addition, all endogenous variables in all models were regressed on control variables of student grade level and gender. The first research question was examined with models of race regressed on school climate and race regressed on SEL. The analysis for the second research question had three steps. First, self-reported grades were regressed on race to assess the magnitude of race inequities in self-reported grades (model 1). Then, in a series of structural equation models, pathways were added from race to school climate to self-reported grades (model 2), and separately, pathways were added from race to SEL to self-reported grades (model 3). Finally, both pathways were entered into the model at the same time, in order to ascertain how SEL and school climate influence may independently mediate associations between race and grades. Indirect effects were assessed in models 2 and 3. The third research question was investigated by including an interaction term between race dummy variables and school climate and between race dummy variables and SEL to assess whether race moderated the association between school climate and SEL on self-reported grades. These interactions were further probed through a series of multiple group models: 1) the multiple group model included separate groups based on the seven racial categories. 2) each model path for each racial group was constrained to be equal to White students. The decrements to fit, based on change in model chi-square and CFI, were then recorded and can be interpreted as showing where differences lie between White students and each racial group of students of color. Because the sample size for this study is large, change in chi-square can be substantial and statistically significant even in cases where differences between groups are small. The CFI provides a measure of the change in fit accounting for sample size. Here, we draw upon recommendations developed by Chen (2007) and Cheung and Rensvold (2002) who suggest that, using maximum likelihood estimation, a change in CFI of .01 is considered to be evidence of a substantial difference between racial groups. Since the WLSMV estimator has not been assessed for use in conjunction with WLSMV, fit statistics to determine differences between groups (Chen, 2007; Cheung & Rensvold, 2002), the change in CFI, change in chi-square, and the significance of interaction terms were all used to determine whether and where there were significant and substantial racial group differences in the effect of school climate and SEL on grades. As with previous analyses, SEL and climate were first considered separately and then together, and all models controlled for grade level and gender and accounted for school clustering.

Results

Descriptive Statistics

A summary of demographic information and the distribution of the outcome can be found in Table 1; correlations among model variables, means, and missingness can be found in Table 2. Students’ average grades were in the “Mostly A’s” range with a mean of 3.35, with a standard deviation of .76. Most variables had little missing data, though 9% of students did not select a racial category. The racial composition of the sample was reflective of the racial distribution of students in the district. Less than 4% was missing on all other variables. An analysis of the magnitude of race inequities in self-reported grades is reported in Model 1 of Table 2. Model 1 regressed dummy variables for race on grades. Controlling for grade level and gender, we found significant race inequities and all racial groups, except for Asian students, reported lower grades compared to White students. Standardized coefficients are reported.

Table 2.

Correlations Among Model Variables, Means, and Missingness

1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 Self-reported grades
2 Climate 0.15
3 SEL 0.36 0.52
4 Asian 0.10 0.02 −0.01
5 Black −0.13 −0.01 −0.03 −0.17
6 Latinx −0.14 0.01 −0.04 −0.12 −0.10
7 Multiracial −0.04 −0.01 −0.01 −0.20 −0.17 −0.12
8 Native American −0.07 0.01 −0.04 −0.06 −0.05 −0.04 −0.06
9 Pacific Islander −0.03 −0.01 −0.02 −0.06 −0.05 −0.04 −0.06 −0.02
10 White 0.15 −0.01 0.07 −0.40 −0.34 −0.25 −0.40 −0.11 −0.12
11 Grade level −0.04 −0.25 −0.06 0.05 0.01 0.03 −0.09 −0.06 0.03 0.02
12 Female 0.14 0.05 0.11 0.01 0.02 0.00 0.01 −0.03 −0.01 −0.02 0.02
13 Male −0.12 −0.01 −0.07 0.00 −0.01 0.01 −0.03 0.02 0.01 0.02 0.02 −0.88
14 Decline to state gender −0.05 −0.08 −0.10 −0.02 −0.03 −0.03 0.06 0.02 0.00 0.00 −0.09 −0.25 −0.24
Mean/percentage 0 0 0 16.3 12.2 7.2 16.3 1.5 1.8 40.8 6.7 47.4 46.3 6.4
Percentage missing 0.00 0.02 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.03 0.04 0.04 0.04
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Note. Climate and SEL success factor scale scores are in standard deviation units. All bolded correlation coefficients are significant at the p<.05 or less.

Research Question 1: Racial Inequity in SEL and School Climate

We found significant racial inequities in students’ perceptions of school climate and SEL. Results are reported in Figure 1, which shows effect sizes in terms of standard deviation unit differences in outcomes associated with each race dummy variable effect. Asian and Latinx students reported significantly more positive perceptions of climate compared to White students, while differences between White and Black, Native American, or Pacific Islander students were not statistically significant. Multiracial students reported that school climate was significantly worse than White students, though the effect size was quite small at .05. As measured, all racial groups reported significantly lower SEL compared to that of White students. Compared to White students, Black students reported scores that were .18 standard deviation units lower, Latinx and Pacific Islander students reported scores .22 standard deviation units lower, and Native American students reported scores .35 standard deviation units lower.

Figure 1.

Figure 1.

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Research Question 1—Standardized effects of race inequities in climate and SEL compared to White students. Coefficients represent standard deviation unit differences compared to White students, represented at 0, controlling for gender and grade, accounting for school nesting. SEL=social emotional learning. *Significantly different than White students.

Research Question 2: Role of SEL and Climate on Race Inequities in Grades

In the first analysis, we examined the association of race on grades and found evidence for race inequities in student grades as shown in the estimates for Model 1 in Table 4. Model 2 includes pathways from race to climate to self-reported grades. Climate was significantly associated with self-reported grades (standardized ES = .13, p>.001), but the indirect effect of race inequities on climate was not significant. In Model 3, a significant direct effect of SEL on grades (ES .32, p<.001) and significant indirect effects of race on grades through SEL are shown. The effect sizes of the indirect effects indicate the degree to which SEL accounts for race inequities in grades. For Black, Latinx, Multiracial, and Pacific Islander students, the effect size is negligible, ranging from −.03 to −.07. The effect size for Native American students was −.11, suggesting a small but meaningful effect of SEL on the difference in grades (Cohen, 1992). Asian students report higher grades than White students, thus the effect is in the opposite direction. We also estimated a model with both climate and SEL. This model indicated that SEL is the more salient variable, as evidenced by the fact that the role of climate turns negative, representing a suppressor effect. Climate and SEL are positively correlated (r=.52, p<.001). These results were not reported in Table 4 since they did not add any new information, other than the salience of SEL to grades compared to climate.

Table 4.

Results of Interaction Tests and Multiple Group Models Testing Race Moderation of School Climate and SEL Effect on Student Self-Reported Grades

Interaction terms Multiple group models
Model 4 Model 5 Model 6 Climate Model 7 SEL
Climate → Grades SEL → Grades Δ Chi-square Δ CFI Δ Chi-square Δ CFI
Asian −0.14* −0.14* −2.35 0 −16.38* 0.01
Black −0.03 −0.17* −2.16 0 −103.03* 0.05
Latinx −0.01 −0.07* −0.82 0 −2.39 0
Multiracial −0.01 −0.10* −0.85 0 −29.93* 0.01
Native American −0.10 −0.19* −1.57 0 −8.20* 0
Pacific Islander 0.02 −0.07* −0.08 0 −0.79 0
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Note.

*

Significance level p<.05. Note: Models 4–5 are in standard deviation units. Chi-square tests used 1 degree of freedom. SEL= social emotional learning.

Research Question 3: Race Moderation

Table 4 reports on the series of models used to probe the interaction of race and climate and race and SEL on grades. Models 4 and 5 show the significance of interaction terms that were added to the models as shown in Table 3. Here, in model 4 the only significant moderation was for Asian students, for whom there was a significantly weaker association between climate and grades. For SEL in model 5, significant interaction terms are found for all racial groups, suggesting that the association for SEL with grades is significantly weaker for all racial groups compared to White students.

Table 3.

Models Examining the Extent to Which SEL and School Climate Account for the Association Between Race and Grades

Model 1
 Model 2
 Model 3
β S.E. β S.E. β S.E.
Asian → Grades 0.05 0.05 0.04 0.05 0.08 0.05
Black→ Grades −0.51* 0.05 −0.51* 0.05 −0.45* 0.05
Latinx→ Grades −0.67* 0.05 −0.68* 0.05 −0.60* 0.04
Multiracial→ Grades −0.26* 0.03 −0.25* 0.03 −0.23* 0.03
Native American → Grades −0.69* 0.06 −0.69* 0.06 −0.58* 0.06
Pacific Islander→ Grades −0.38* 0.08 −0.38* 0.08 −0.31* 0.08
Asian → Climate 0.07* 0.04
Black → Climate −0.01 0.05
Latinx→ Climate 0.06 0.04
Multiracial→ Climate −0.05 0.03
Native American → Climate −0.02 0.06
Pacific Islander→ Climate 0.02 0.05
Asian → SEL −0.10* 0.02
Black → SEL −0.18* 0.03
Latinx→ SEL −0.22* 0.03
Multiracial→ SEL −0.11* 0.02
Native American→ SEL −0.35* 0.04
Pacific Islander→ SEL −0.22* 0.05
Climate→ Grades 0.13* 0.01
SEL→ Grades 0.32* 0.01
Model Indirect Effects
Asian → Climate → Grades 0.01 0.01
Black → Climate → Grades 0.00 0.01
Latinx → Climate → Grades 0.01 0.01
Multiracial → Climate → Grades −0.01 0.00
Native American→ Climate → Grades 0.00 0.01
Pacific Islander → Climate → Grades 0.00 0.01
Asian → SEL → Grades −0.03* 0.01
Black → SEL → Grades −0.06* 0.01
Latinx → SEL → Grades −0.07* 0.01
Multiracial→ SEL → Grades −0.03* 0.01
Native American→ SEL → Grades −0.11* 0.01
Pacific Islander → SEL → Grades −0.07* 0.02
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Note. Estimates are in standard deviation units. Control variables not shown. SEL= social emotional learning.

To clarify the interpretation of interaction terms, multiple group modeling was employed. In the first step of multiple group modeling, we found evidence of significant racial inequities in the omnibus test that assessed the decrease in model fit when all pathways were held the same (CFI=.91, df=30, χ2=214.97). Models 6 and 7 of Table 4 show the results of changes in fit in a series of multiple group models holding the pathway between climate and grades equal to White students (Model 6), and SEL and grades equal to White students (Model 7). These tests largely confirmed the results of significant interaction terms with some notable exceptions. Where there was a small magnitude in the effect size of the interaction term, the CFI and chi-square difference tests did not provide evidence of race moderation; this was the case for Latinx and Pacific Islander students, for whom the effect size of the interactions was .07. For Native American students there were differences between the three different test results. Significant and large interaction terms were found that decreased the association of SEL on grades, yet, these results were confirmed by the chi-square difference tests but not by the change in CFI. This may be due to the small sample size of Native American students (N=347). The largest interaction term was for Black students, and the moderating effect was confirmed across tests. Figure 2 shows the magnitude of the independent effect sizes of SEL and climate on grades, for each racial group, in the order of the smallest to largest effect of SEL. This graph shows that the association between SEL and grades for White students is stronger compared to all other racial groups. The effect of these skills on grades for White students was nearly double what it was for Native American and Black students (a difference of .20 and .18, respectively).

Figure 2.

Figure 2.

Open in a new tab

Research Question 3—Standardized effects of SEL and climate on self-reported grades for each racial group. Model shown controlled for grade level and gender. +All tests agreed that the effect on grades was significantly different compared to White students.

Discussion

The present study aimed to examine the potential role of school climate and SEL in promoting racially equitable academic outcomes. We found that students of color reported significantly lower levels of SEL compared to their White peers and modest differences in perceived school climate appeared across racial groups (RQ1). Contrasting with previous literature, only Multiracial students perceived the climate of their schools to be worse than White students did, while Asian and Latinx students reported slightly better perceived climate (RQ1). However, the effect sizes of these differences are negligible. SEL accounted for a negligible portion of the racial inequity in student reported grades (RQ2). School climate did not attenuate the association between race and grades, though it was consistently related to grades for students from different racial groups (RQ3). Importantly, we also found that the association between SEL and grades was moderated by race, suggesting that SEL was significantly more strongly associated with grades for White students compared to Native American and Black students (RQ3).

Social Emotional Learning

Two explanations for the finding that all groups of students of color reported significantly lower SEL skills compared to White students deserves further probing. In discussing these inequities, it is critical that we identify this finding as evidence that our measure insufficiently captures the strengths and assets of students of color and is not evidence of deficits among these students. At a minimum, this finding demonstrates the insufficiency of our SEL measure in capturing the aspects of social connection and emotional expression that are culturally relevant. More likely, it is evidence of a deeper problem. Scholars have argued that current conceptualizations of SEL are colorblind, thus they may not capture the skills or ways of knowing relevant to students of color from diverse cultural groups (Gregory & Fergus, 2017; Hoffman, 2009). SEL as currently defined is culturally congruent with students’ understanding of social interaction and emotional expression, is inclusive of the cultural wealth of specific communities (Yosso, 2005), or encompasses the differences in power and privilege experienced by students of color (Gregory & Fergus, 2017; Hoffman, 2009). Further, SEL that does not encompass awareness of power, privilege, oppression or culture can serve to perpetuate systems of oppression by contributing to deficit narratives and biased appraisal of students’ behavior. In fact, some authors have critiqued SEL as another way that students of color, especially Black students, are policed. Students whose behavior is not judged by the predominately White teaching force as sufficiently socially and emotionally competent are punished, as evidenced by the longstanding and large racial disparities in the use of exclusionary discipline (Gregory & Fergus).

Second, this finding may also represent another manifestation of education debt (Ladson-Billings, 2006), since access to high quality SEL programming and supportive school environments are not equally distributed (Berg et al., 2017; Carter, et al., 2016) as students of color are more likely to attend lower-resourced schools (Carter et al., 2016; Ladson-Billings, 2006). As such, we must see this finding in the context of the structural inequities that have reinforced systemic racism that impacts students on a number of fronts. Students’ differential reports of SEL might be a result of an accumulation of risk exposures that serve to undermine students’ ability to develop SEL skills or limit their opportunities for skill development. Some examples of potential risk exposures to which students of color are more likely to be subjected include higher rates of exclusionary discipline, which leads to loss of instructional time (Gregory, Skiba, & Noguera, 2010), the stress of microaggressions and stereotyping that is associated with being a member of an oppressed group (Berg et al., 2017; Howard, 2010), or experiencing discriminatory tracking into special education or out of advanced-learning opportunities (Carter et al., 2016).

The findings herein suggest that SEL is less strongly associated with student grades for Black, Native American, Asian, and Multiracial students compared to White students. Chain and colleagues (2017) found that SEL was more important for Native American students compared to White students or other students of color. However, our findings diverge from their study. We found that the effect of SEL on grades was the largest for Native Americans compared to other racial groups. However, SEL was also connected to grades for Native American students to a lesser degree compared to other student racial groups. This difference might be due to the reporter. Teachers rated SEL in the Chain et al. (2017) study, whereas students rated themselves in ours. Rater bias might have a significant impact on the results of each study, as the degree of SEL skills of a given student is interpreted through the lens of the rater, whose race and culture likely influence their interpretation. By contrast, the present study found that for some racial groups of color, especially Native American students, SEL is less strongly associated with academic success, which points to the importance of deepening our understanding of cultural interpretations of SEL (Hoffman, 2009), as well as the role of power and privilege (Gregory & Fergus, 2017). Future research should consider measuring SEL in a manner that is inclusive of culturally diverse ways knowing, and racial and cultural identity may be an important aspect of SEL and particularly relevant for students from marginalized backgrounds (Howard, 2010) that has been largely ignored in SEL research to date.

The findings of this study suggest that SEL was differentially associated with grades for many racial groups, these findings suggest that SEL, in its current conceptualization, may not contribute to racial equity in student academic success. Focusing on the commonalities among students ignores the racialized experiences of students of color, such that the areas of focus in SEL may not be relevant to students from different racial and cultural backgrounds (Gillborn, 2005). This is not to say that social relationships or emotional expression and understanding are not generalizable concepts cross-culturally, but that the way that socializing or expressing (or not expressing) emotions is known to vary across cultural groups and cultural values (Hoffman, 2009). Approaches that focus on commonalities alone have been called colorblind, as they ignore important differences in students’ privilege and cultural values (Bonilla-Silva, 2014; Gregory & Fergus, 2017). SEL is rooted in cultural expectations of what positive socialization looks like, and how students should (or should not) express their emotions. The extent to which a student adheres to dominant (i.e., White middle-class majority) American cultural values is likely to influence the degree to which SEL, as conceived of here, is related to academic success (Blanco-Vega, Castro-Olivo, & Merrell, 2007).

School Climate

The current study’s results on race inequities in perceptions of school climate represent a departure from previous studies, most of which report that, compared with White students, students of color perceive the climate to be worse at their school (Konold et al., 2017; Voight et al., 2015). We found that Asian and Latinx students report slightly better school climate than White students (ES .07 and .06 respectively) yet previous studies found that Black and Latinx students experience significantly worse school climate compared to White students (Konold et al., 2017; Voight et al., 2015). In this study, school climate was not related to racial inequities in student-reported grades, nor were there racial inequities in the association of climate with student grades. School climate was related to grades in all analyses when it was entered independently, but when SEL was included, it was no longer significant, possibly due to the larger correlation with SEL and SEL being the more proximal effect. The results indicate that school climate is viewed relatively positively for all students and has a small association with grades. However, perceptions of school climate at the individual level were not related to racial inequities in student grades. Furthermore, it may be that current conceptualizations of school climate may not adequately represent the racialized experience of students of color in schools. Future research may benefit from 1) examining school climate at the school level, as the culture of the school community may get at the ways in which climate can impact students’ school experience at a more structural level as opposed to an individual level; and 2) considering aspects of the school environment that capture the racialized experience of students of color that may be more culturally-relevant measures of school climate. Qualitative research to inquire about student of color’s racialized experiences of school climate is needed to design a survey that can provide information to school leaders on how to create school environments that foster racial equity. This may mean incorporating contributors to racial equity into all survey constructs or creating a specific racial equity construct.

Implications and Future Directions

While this analysis found that the effect of SEL and school climate on grades was positive, we found important racial inequities in the strength of these associations. These findings have implications for the resources currently supporting the use of SEL and climate improvement programs. The finding that the strength of the relationship of SEL with grades differs based on race highlights the need for additional research. Researchers and practitioners must investigate assumptions of sameness and question whether SEL has been measured in ways that capture important racial and cultural differences in the meaning of SEC. Given these current findings and the result of reviews of SEL interventions (Rowe & Trickett, 2018; Garner et al., 2014) showing the lack of evidence as to the effect of SEL interventions for students from different racial groups, researchers need to investigate the potential for interventions to contribute to racial equity or inequity. If programs targeting SEL are differentially meaningful for students, there is potential for interventions to worsen inequalities (Frohlich & Potvin, 2008; Lorenc, Petticrew, Welch, & Tugwell, 2012).

There are a number of directions this work can take to improve the knowledge of the relationship between school climate, SEL, and racial equity in academic success. School practitioners can learn from these results by approaching SEL and school climate reform with a critical perspective on how school practices may or may not uplift the strengths and assets of the students of color furthest from educational justice as well as examine their own privilege and how this may impact the lens through which they view students’ strengths and behaviors. On a grand scale, all intervention programs being implemented in diverse schools characterized by racial inequities in academic success need to be evaluated as to whether they improve or exacerbate the racial inequities in academic success. This means framing research questions in terms of racial equity rather than looking only at mean effects.

Limitations

There are a number of limitations to this analysis that limit the generalizability and usefulness of findings. First, the use of self-reported grades is a problematic outcome as it is subject to student knowledge of their own grades and their truthfulness in reporting grades accurately. Although grades are still one of the most common academic achievement indicators across grade levels, including elementary school (e.g., Cvencek, Fryberg, Covarrubias, & Meltzoff, 2018), they take on a different level of significance at higher grade levels, especially in high school. Nonetheless, future studies would benefit from considering other academic outcomes, including academic efficacy, academic competence, and academic self-concept as these indicators are becoming more prevalent in education research today. In addition, SEL and school climate experiences may vary across grade levels. Although SEL processes and indicators of positive school climate are consistent across grade levels (CASEL, 2020; Panayiotou et al., 2019; Wang & Degol, 2016), it may be beneficial in future studies to consider developmental timing across childhood and adolescence and how the transition from elementary to middle to high school may influence these experiences. Next, the school climate data are anonymous, and as such, cannot be linked to school records, such as student achievement scores or disciplinary records. This is an important limitation to consider as objective measures of achievement may be more accurate reflections of student performance. It is also possible that disproportionate disciplinary practices play a role in students’ perceptions of school climate, SEL, and their grades, but are unable to account for this possibility in the data we have. We conducted analyses of school level disparities in grades in an attempt to provide some connection between student self-reported grades with more objective measures of achievement. We found that the model of self-reported grades showed smaller mean differences between racial groups than are present in achievement scores at the school level. The fact that there are smaller gaps suggests that these findings would be amplified if other academic outcome data were available. There are also a number of contextual factors such as school racial composition and socioeconomic status, and school-level school climate that are important to consider in future research on this topic.

Another important limitation is our reliance on racial groupings that were created by the dominant culture to describe marginalized groups. Racial categories are inherently problematic for many reasons; to name a few - there are larger differences within racial groups than between them (Betancourt & Lopez, 1993), and these categories lack the ability to capture student culture, ethnicity, religion, or other marginalized identities. Many find racial categories to be further marginalizing, and racial categories do not account for the experiences of multiracial individuals (Powell, 2012). In addition, the experiences of multiracial students are extremely diverse since it depends greatly on which racial groups constitute their background and how they visually present to others. This survey does not allow students to report more than one racial group, which makes it challenging to draw any conclusions about findings for multiracial students.

All of the present analyses rely on student self-report of their perceptions of climate and their own skill set. Students may have a more limited ability to report on their own social emotional skills, since part of the skill set that students are asked to rate themselves on requires a certain ability to be self-observant. There are also issues of reference bias with students’ self-reports of SEL, where social comparison is necessary to determine their own skill level. This has implications for generalizing SEL findings across schools (Duckworth & Yeager, 2015; West et al., 2015). Finally, the data in this study are structured in such a way that the study was limited to a cross-sectional design, limiting the conclusions to be correlational in nature. Given the interrelated nature of school climate and academics, it is important that future research examine these factors longitudinally. It is likely that the relationship between academic success and school climate is reciprocal, making longitudinal research on change in climate and academic success of students over time all the more important.

Highlights.

  • Large racial inequities were found in SEL but not school climate

  • SEL nor school climate accounted for racial inequities in grades

  • SEL was significantly more strongly related to grades for White students

  • SEL and climate must be critically evaluated for their contribution to racial (in)equity

Acknowledgments

This research was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number TL1 TR002318. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This research was presented at the annual meeting of the Society for Social Work and Research in San Francisco in January 2019 and was part of a doctoral dissertation.

Footnotes

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Contributor Information

Tiffany M. Jones, Colorado State University

Charles Fleming, University of Washington.

Anne Williford, Colorado State University.

References

  1. Anyon Y, Zhang D, & Hazel C. (2016). Race, exclusionary discipline, and connectedness to adults in secondary schools. American Journal of Community Psychology, 57(3–4), 342–352. doi: 10.1002/ajcp.12061 [DOI] [PubMed] [Google Scholar]
  2. Baum S, Ma J, & Payea K. (2010). Education pays, 2010: The benefits of higher education for individuals and society Trends in Higher Education Series. College Board Advocacy & Policy Center. [Google Scholar]
  3. Bear G, Gaskins C, Blank J, & Chen F. (2011). Delaware School Climate Survey—Student: Its factor structure, concurrent validity, and reliability. Journal of School Psychology, 49(2), 157–174. 10.1016/j.jsp.2011.01.001 [DOI] [PubMed] [Google Scholar]
  4. Berg J, Osher D, Moroney D, & Yoder N. (2017). The intersection of school climate and social emotional development. Washington DC: American Institutes for Research. [Google Scholar]
  5. Berkowitz R, Moore H, Astor RA, & Benbenishty R. (2016). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Review of Educational Research, 87(2), 425–469. doi: 10.3102/003465431666982 [DOI] [Google Scholar]
  6. Betancourt H, & López S. (1993). The study of culture, ethnicity, and race in American psychology. American Psychologist, 48(6), 629. doi: 10.1037/0003-066x.48.6.629 [DOI] [Google Scholar]
  7. Blanco-Vega C, Castro-Olivo S, & Merrell K. (2008). Social–emotional needs of Latino immigrant adolescents: A sociocultural model for development and implementation of culturally specific interventions. Journal of Latinos and Education, 7(1), 43–61. doi: 10.1080/15348430701693390 [DOI] [Google Scholar]
  8. Bonilla-Silva E. (2014). Racism without racists: Color-blind racism and the persistence of racial inequality in the United States. Plymouth, UK: Rowman and Littlefield. [Google Scholar]
  9. Bottiani J, Bradshaw C, & Mendelson T. (2017). A multilevel examination of racial disparities in high school discipline: black and white adolescents’ perceived equity, school belonging, and adjustment problems. Journal of Educational Psychology, 109(4), 532–545. [Google Scholar]
  10. Carter P, Skiba R, Arredondo M, & Pollock M. (2016). You can’t fix what you don’t look at: Acknowledging race in addressing racial discipline disparities. Urban Education, 52(2), 207–235. [Google Scholar]
  11. CASEL. (2013). 2013 CASEL Guide: Effective social and emotional learning programs - preschool and elementary school edition. Chicago, IL: Collaborative for Academic, Social and Emotional Learning. [Google Scholar]
  12. CASEL. (2015). 2015 CASEL Guide: Effective Social and emotional learning programs - middle and high school edition. Chicago, IL: Collaborative for Academic, Social, and Emotional Learning. [Google Scholar]
  13. Chain J, Shapiro V, LeBuffe P, Bryson A, & Committee A. (2017). Academic achievement of American Indian and Alaska Native students: Does social emotional competence reduce the impact of poverty? American Indian and Alaska Native Mental Health Research (Online), 24(1), 1–29. doi: 10.5820/aian.2401.2017.1 [DOI] [PubMed] [Google Scholar]
  14. Chen F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. doi: 10.1080/10705510701301834 [DOI] [Google Scholar]
  15. Cheung G, & Rensvold R. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. doi: 10.1207/s15328007sem0902_5 [DOI] [Google Scholar]
  16. Cohen J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. [DOI] [PubMed] [Google Scholar]
  17. Cohen J, McCabe L, Michelli N, & Pickeral T. (2009). School climate: Research, policy, practice, and teacher education. Teachers College Record, 111, 180–213. [Google Scholar]
  18. Cvencek D. Fryberg SA, Covarrubias R. & Meltzoff AN (2018). Self-concepts, self-esteem, and academic achievement of minority and majority North American elementary school children. Child Development, 89(4), 1099–1109. 10.1111/cdev.12802 [DOI] [PubMed] [Google Scholar]
  19. De Pedro K, Gilreath T, & Berkowitz R. (2016). A latent class analysis of school climate among middle and high school students in California public schools. Children and Youth Services Review, 63, 10–15. doi: 10.1016/j.childyouth.2016.01.023 [DOI] [Google Scholar]
  20. Debnam K, Johnson S, Waasdorp T, & Bradshaw C. (2014). Equity, connection, and engagement in the school context to promote positive youth development. Journal of Research on Adolescence, 24(3), 447–459. doi: 10.1111/jora.12083 [DOI] [Google Scholar]
  21. Denham S, & Brown C. (2010). “Plays Nice With Others”: Social–Emotional Learning and Academic Success. Early Education & Development, 21(5), 652–680. doi: 10.1080/10409289.2010.497450 [DOI] [Google Scholar]
  22. Domitrovich C, Durlak J, Staley K, & Weissberg R. (2017). Social-emotional competence: An essential factor for promoting positive adjustment and reducing risk in school children. Child Development, 88(2), 408–416. doi: 10.1111/cdev.12739 [DOI] [PubMed] [Google Scholar]
  23. Duckworth A, & Seligman M. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16(12), 939–944. doi: 10.1111/j.1467-9280.2005.01641.x [DOI] [PubMed] [Google Scholar]
  24. Duckworth A, & Yeager D. (2015). Measurement matters. Educational Researcher, 44(4), 237–251. doi: 10.3102/0013189x15584327 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Durlak JA, Weissberg RP, Dymnicki AB, Taylor RD, & Schellinger KB (2011). The impact of enhancing students’ social and emotional learning: a meta‐analysis of school‐based universal interventions. Child Development, 82(1), 405–432. 10.1111/j.1467-8624.2010.01564.x [DOI] [PubMed] [Google Scholar]
  26. Dweck CS (2006). Mindset: The new psychology of success. New York, NY: Random House Incorporated. [Google Scholar]
  27. Elias MJ (2006). The connection between academic and social-emotional learning In Elias MJ & Arnold H. (Eds.). The Educator’s guide to emotional intelligence and academic achievement (pp. 4–14). Thousand Oaks, CA: Corwin Press. [Google Scholar]
  28. Elias MJ, & Haynes NM (2008). Social competence, social support, and academic achievement in minority, low-income, urban elementary school children. School Psychology Quarterly, 23(4), 474–495. [Google Scholar]
  29. Elias MJ, & Moceri DC (2012). Developing social and emotional aspects of learning: the American experience. Research Papers in Education, 27(4), 423–434. [Google Scholar]
  30. Elias M, White G, & Stepney C. (2014). Surmounting the challenges of improving academic performance: Closing the achievement gap through social-emotional and character development. Journal of Urban Learning, Teaching, and Research, 10, 14–24. [Google Scholar]
  31. Farrington CA, Roderick M, Allensworth E, Nagaoka J, Keyes T, Johnson DW, & Beechum NO (2012). Teaching adolescents to become learners: The role of noncognitive factors in shaping school performance--A critical literature review. Chicago, IL: University of Chicago Consortium on Chicago School Research; https://ccrscenter.org/products-resources/resource-database/teaching-adolescents-become-leaders-role-noncognitive-factors [Google Scholar]
  32. Frohlich K, & Potvin L. (2008). Transcending the known in public health practice: the inequality paradox: the population approach and vulnerable populations. American Journal of Public Health, 98(2), 216–221. doi: 10.2105/AJPH.2007.114777 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Fryberg SA, & Leavitt PA (2014). A sociocultural analysis of high-risk Native American children in schools In Burack JA & Schmidt LA (Eds.), Cultural and contextual perspectives on developmental risk and well-being (pp. 57–80). Cambridge, UK: Cambridge University Press. [Google Scholar]
  34. Garibaldi M, Ruddy S, Kendziora K, & Osher D.. (2015). Assessment of climate and conditions for learning In Durlak JA, Domitrovich CP, Weissberg RE, & Gullotta TP (Eds.). Handbook of social and emotional learning: research and practice (pp. 348–360). New York, NY: Guilford Press. [Google Scholar]
  35. Garner PW (2006). Prediction of prosocial and emotional competence from maternal behavior in African American preschoolers. Cultural Diversity and Ethnic Minority Psychology, 12(2), 179–198. doi: 10.1037/1099-9809.12.2.179 [DOI] [PubMed] [Google Scholar]
  36. Garner P, Mahatmya D, Brown E, & Vesely C. (2014). Promoting desirable outcomes among culturally and ethnically diverse children in social emotional learning programs: A multilevel heuristic model. Educational Psychology Review, 26(1), 165–189. doi: 10.1007/s10648-014-9253-7 [DOI] [Google Scholar]
  37. Gillborn D. (2005). Education policy as an act of white supremacy: whiteness, critical race theory and education reform. Journal of Education Policy, 20(4), 485–505. doi: 10.1080/0268093050013234 [DOI] [Google Scholar]
  38. Gottfredson G, Gottfredson D, Payne A, & Gottfredson N. (2005). School climate predictors of school disorder: Results from a national study of delinquency prevention in schools. Journal of Research in Crime and Delinquency, 42(4), 412–444. doi: 10.1177/0022427804271931 [DOI] [Google Scholar]
  39. Gregory A, Skiba RJ, & Noguera PA (2010). The achievement gap and the discipline gap: Two sides of the same coin?. Educational Researcher, 39(1), 59–68. [Google Scholar]
  40. Gregory A, & Fergus E. (2017). Social and emotional learning and equity in school discipline. The Future of Children, 27, 117–136. [Google Scholar]
  41. Hoffman. (2009). Reflecting on social emotional learning: A critical perspective on trends in the United States. Review of Educational Research, 79(2), 533–556. 10.3102/0034654308325184 [DOI] [Google Scholar]
  42. Howse RB, Calkins SD, Anastopoulos AD, Keane SP, & Shelton TL (2003). Regulatory contributors to children’s kindergarten achievement. Early Education and Development, 14(1), 101–120. [Google Scholar]
  43. Howard TC (2010). Why race and culture matter in schools: Closing the achievement gap in America’s classrooms (Vol. 39). New York, NY: Teachers College Press. [Google Scholar]
  44. Jones DE, Greenberg M, & Crowley M. (2015). Early social-emotional functioning and public health: The relationship between kindergarten social competence and future wellness. American Journal of Public Health, 105(11), 2283–2290 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Jones SM, & Bouffard SM (2012). Social and emotional learning in schools: From programs to strategies Social Policy Report. Volume 26, Number 4. Society for Research in Child Development; Retrieved from https://eric.ed.gov/?id=ED540203. [Google Scholar]
  46. Kaler-Jones C, (2020) When SEL Is used as another form of policing. Medium. https://medium.com/@justschools/when-sel-is-used-as-another-form-of-policing-fa53cf85dce4. [Google Scholar]
  47. Kao G, & Thompson J. (2003). Racial and ethnic stratification in educational achievement and attainment. Sociology, 29(1), 417–442. doi: 10.1146/annurev.soc.29.010202.100019 [DOI] [Google Scholar]
  48. Kistner J, Metzler A, Gatlin D, & Risi S. (1993). Classroom racial proportions and children’s peer relations: Race and gender effects. Journal of Educational Psychology, 85(3), 446–452. [Google Scholar]
  49. Konold T, Cornell D, Shukla K, & Huang F. (2016). Racial/ethnic differences in perceptions of school climate and its association with student engagement and peer aggression. Journal of Youth and Adolescence, 46(6), 1289–1303. doi: 10.1007/s10964-016-0576-1 [DOI] [PubMed] [Google Scholar]
  50. Koth C, Bradshaw C, & Leaf P. (2008). A multilevel study of predictors of student perceptions of school climate: The effect of classroom-level factors. Journal of Educational Psychology, 100(1), 96–104. doi: 10.1037/0022-0663.100.1.96 [DOI] [Google Scholar]
  51. Kwong D, & Davis J. (2015). School climate for academic success: A multilevel analysis of school climate and student outcomes. Journal of Research in Education, 25, 68–81. [Google Scholar]
  52. Ladson-Billings G. (2006). From the achievement gap to the education debt: Understanding achievement in US schools. Educational Researcher, 35(7), 3–12. [Google Scholar]
  53. Lee J. (2002). Racial and ethnic achievement gap trends: Reversing the progress toward equity? Educational Researcher, 31(1), 3–12. doi: 10.3102/0013189X031001003 [DOI] [Google Scholar]
  54. Leighton J, Guo Q, Chu M-W, & Tang W. (2018). A pedagogical alliance for academic achievement: Socio-Emotional effects on assessment outcomes. Educational Assessment, 23(1), 1–23. doi: 10.1080/10627197.2017.1411188 [DOI] [Google Scholar]
  55. Liew J, McTigue EM, Barrois L, & Hughes JN (2008). Adaptive and effortful control and academic self-efficacy beliefs on achievement: A longitudinal study of 1st through 3rd graders. Early Childhood Research Quarterly, 23(4), 515–526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Lorenc T, Petticrew M, Welch V, & Tugwell P. (2013). What types of interventions generate inequalities? Evidence from systematic reviews. Journal of Epidemiology and Community Health, 67(2), 190–193. doi: 10.1136/jech-2012-201257 [DOI] [PubMed] [Google Scholar]
  57. Mattison E, & Aber M. (2007). Closing the achievement gap: The association of racial climate with achievement and behavioral outcomes. American Journal of Community Psychology, 40(1–2), 1–12. doi: 10.1007/s10464-007-9128-x [DOI] [PubMed] [Google Scholar]
  58. Morton N. (2018, January 12). Racial equity in Seattle schools has a long frustrating history- and it’s getting worse. Retrieved from: https://www.seattletimes.com/education-lab/racial-equity-in-seattle-schools-has-a-long-frustrating-history-and-its-getting-worse/ [Google Scholar]
  59. Muthén LK & Muthén BO (2010). Mplus User’s Guide (et al. ). Los Angeles, CA [Google Scholar]
  60. Muthén BO, & Satorra A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267–316. [Google Scholar]
  61. Nitardy CM, Duke NN, Pettingell SL, & Borowsky IW (2015). Racial and ethnic disparities in educational achievement and aspirations: Findings from a statewide survey from 1998 to 2010. Maternal and Child Health Journal, 19(1), 58–66. [DOI] [PubMed] [Google Scholar]
  62. Osher D, Kidron Y, & Brackett M. (2016). Advancing the science and practice of social and emotional learning: Looking back and moving forward. Review of Research in Education. 40(1), 644–668. doi: 10.3102/0091732X16673595 [DOI] [Google Scholar]
  63. Panayiotou M, Humphrey N, & Wigelsworth M. (2019). An empirical basis for linking social and emotional learning to academic performance. Contemporary Educational Psychology, 56, 193–204. 10.1016/j.cedpsych.2019.01.009 [DOI] [Google Scholar]
  64. Paschall KW, Gershoff ET, & Kuhfeld M. (2018). A Two decade examination of historical race/ethnicity disparities in academic achievement by poverty status. Journal of Youth and Adolescence, 47, 1164–1177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Paunesku D, Walton GM, Romero C, Smith EN, Yeager DS, & Dweck CS (2015). Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science, 26(6), 784–793. [DOI] [PubMed] [Google Scholar]
  66. Powell JA (2012). Racing to justice: Transforming our conceptions of self and other to build an inclusive society. Bloomington, IN: Indiana University Press. [Google Scholar]
  67. Reardon SF Ho AD, Shear BR, Fahle EM, Kalogrides D, & DiSalvo R. (2017). Stanford Education Data Archive (Version 2.0). Retrieved from: http://purl.stanford.edu/db586ns4974 [Google Scholar]
  68. Ross CE, & Wu CL (1995). The links between education and health. American Sociological Review, 60, 719–745. [Google Scholar]
  69. Rowe H, & Trickett E. (2018). Student diversity representation and reporting in universal school-based social and emotional learning programs: Implications for generalizability. Educational Psychology Review, 30, 559–583. doi: 10.1007/s10648-017-9425-3 [DOI] [Google Scholar]
  70. Rudasill K, Snyder K, Levinson H, & Adelson J. (2017). Systems view of school climate: A theoretical framework for research. Educational Psychology Review, 30(1), 35–69. doi: 10.1007/s10648-017-9401-y [DOI] [Google Scholar]
  71. Public School District (2017). Masked for review. [Google Scholar]
  72. Shapiro V, Kim E, Accomazzo S, & Roscoe J. (2016). Predictors of rater bias in the assessment of social-emotional competence. International Journal of Emotional Education, 8(2), 25–44. [Google Scholar]
  73. Shirley E, & Cornell D. (2012). The contribution of student perceptions of school climate to understanding the disproportionate punishment of African American students in a middle school. School Psychology International, 33(2), 115–134. doi: 10.1177/0143034311406815 [DOI] [Google Scholar]
  74. Shukla K, Konold T, & Cornell D. (2016). Profiles of student perceptions of school climate: Relations with risk behaviors and academic outcomes. American Journal of Community Psychology, 57(3–4), 291–307. doi: 10.1002/ajcp.12044 [DOI] [PubMed] [Google Scholar]
  75. Simmons D, (2017). How to change the story about students of color. Greater Good Magazine. https://greatergood.berkeley.edu/article/item/how_to_change_story_of_students_of_color [Google Scholar]
  76. Taylor R, Oberle E, Durlak J, & Weissberg R. (2017). Promoting positive youth development through school‐based social and emotional learning interventions: A meta‐analysis of follow‐up effects. Child Development, 88(4), 1156–1171. doi: 10.1111/cdev.12864 [DOI] [PubMed] [Google Scholar]
  77. Thapa A, Cohen J, Guffey S, & Higgins-D’Alessandro A. (2013). A review of school climate research. Review of Educational Research, 83, 357–385. [Google Scholar]
  78. U.S. Department of Education. (2008). New race and ethnicity guidance for the collection of federal education data. Retrieved from: https://www2.ed.gov/policy/rschstat/guid/raceethnicity/index.html
  79. Voight A, Hanson T, O’Malley M, & Adekanye L. (2015). The racial school climate gap: Within-school disparities in students’ experiences of safety, support, and connectedness. American Journal of Community Psychology, 56(3–4), 252–267. doi: 10.1007/s10464-015-9751-x [DOI] [PubMed] [Google Scholar]
  80. Voight A, & Hanson T. (2017). How are middle school climate and academic performance related across schools and over time? REL 2017–212. San Francisco, CA: Regional Educational Laboratory West; Retrieved from https://eric.ed.gov/?id=ED572366 [Google Scholar]
  81. Voight A, & Nation M. (2016). Practices for improving secondary school climate: A systematic review of the research literature. American Journal of Community Psychology, 58(1–2), 174–191. doi: 10.1002/ajcp.12074 [DOI] [PubMed] [Google Scholar]
  82. Wang M-T, & Degol J. (2016). School climate: A review of the construct, measurement, and impact on student outcomes. Educational Psychology Review, 28(2), 315–352. doi: 10.1007/s10648-015-9319-1 [DOI] [Google Scholar]
  83. Way N, Reddy R, & Rhodes J. (2007). Students’ perceptions of school climate during the middle school years: associations with trajectories of psychological and behavioral adjustment. American Journal of Community Psychology, 40(3–4), 194–213. doi: 10.1007/s10464-007-9143-y [DOI] [PubMed] [Google Scholar]
  84. Weissberg RP, Durlak JA, Domitrovich CE, & Gullotta TP (2015) Social and emotional learning: Past, present and future In Durlak JA, Domitrovich CP, Weissberg RE, & Gullotta TP (Eds.). Handbook of social and emotional learning: research and practice (pp. 348–360). New York, NY: Guilford Press. [Google Scholar]
  85. West MR, Kraft MA, Finn AS, Martin RE, Duckworth AL, Gabrieli C, & Gabrieli J. (2015). Promise and paradox: Measuring students’ non-cognitive skills and the impact of schooling. Educational Evaluation and Policy Analysis, 38(1), 148–170. doi: 10.3102/0162373715597298 [DOI] [Google Scholar]
  86. Yosso TJ (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8(1), 69–91. Taylor & Francis. doi: 10.1080/1361332052000341006 [DOI] [Google Scholar]
  87. Zins J, Bloodworth M, Weissberg R, & Walberg H. (2007). The Scientific base linking social and emotional learning to school success. Journal of Educational and Psychological Consultation, 17(2–3), 191–210. doi: 10.1080/10474410701413145 [DOI] [Google Scholar]

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