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Social Emotional Learning and Academic Success 1

Social Emotional Learning as an Early Warning Indicator of Academic Success and Failure


ABSTRACT

This study evaluated the degree to which a range of social emotional learning skills – academic self-efficacy, academic motivation, social connections, importance of school, and managing psychological and emotional distress and academic stress – could be used as an indicator of future academic outcomes. Using a sample of 4,797 from a large urban school district, we found that high school students classified as performing in the lowest 25% of their grade reported lower social emotional skills than students classified in the top 25% of academic performers by the end of the 8th grade. Two variables, perceived importance of attending college and psychological and physical stress, accounted for nearly 26% of the variance in cumulative high school GPA after controlling for 9th-grade GPA. Finally, the results indicated that a combination of 5 social emotional learning subscales effectively discriminated between students making positive progress towards high school graduation and those identified as having dropped out or failed more than 14% of their courses.


Middle and high schools optimize youth development when educators personalize

educational and social emotional learning opportunities for the changing needs of youth

(Battistich, 2010; Deci & Ryan, 2002; Eccles & Roeser, 2010; Lapan, Gysbers, & Petroski,

2001). Social emotional learning is the ability to acquire and apply the knowledge and skills

necessary to comprehend, manage, and articulate social and emotional information that is

associated with social behaviors such as maintaining positive relationships, recognizing

emotions, and making responsible decisions (Crick & Dodge, 1994; Elias et al., 1997; Nowicki

& Mitchell, 1998). School-based efforts to promote and enhance social emotional learning skills

are built on an understanding that academic success and better school performance emerge in the

context of supportive relationships (Eisenberg, 2006; Guerra & Bradshaw, 2008; Masten &

Coatsworth, 1998; Weissberg & Greenberg, 1998). However, in response to an increasingly

depersonalized learning environment, many youth disengage from middle and high school,

evidenced by lower academic achievement, higher absenteeism, and increased behavior

disruptions, all of which too often culminate in leaving school (Alexander, Entwisle, & Kabbani,

2001; Balfanz, Bridgeland, Moore, & Fox, 2010; Eccles, Midgley, Buchanan, Wigfield, Reuman,

& Mac Iver, 1993; Rumberger, 1995).

In their longitudinal study of Philadelphia schools, Neild and her colleagues identified four

early-warning indicators of dropout risk (Neild, Balfanz, & Herzog, 2007). These indicators

included receiving a final grade of F in either Mathematics or English, attendance below 80%,

and evidence of disruptive behavior. Subsequent research in a large urban school district found

that creating an “on-time to graduate” indicator consisting of grades and credits accumulated was

a good predictor of who was likely to graduate with 93% of those identified in the ninth grade as

on track eventually graduating while 50% of those ninth graders identified as not on track


eventually dropped out of school (Carl, Richardson, Cheng, Kim, & Meyer, 2013). Carl et al.

(2013), however, identified two problems with the early warning indicator methodology. One

problem was that nearly 30% of the students who graduated received cumulative math grade

point averages below 1.0 and an additional 30% averaged 2.0 and below across their math

courses. Although these students did graduate from high school, they did not possess the college

and career readiness skills to enter and succeed in a postsecondary setting. The second issue was

that accounting for both credits and quality GPA as a “total quality credits” indicator, they

concluded that “predicting dropouts is more complex for some students, and may even be driven

by external or non-academic factors” (Carl et al., p. 21).

In a subsequent study, Neild and colleagues evaluated whether two nonacademic factors -

student’s self-reported academic and social engagement – contributed to models estimating

future dropout risk (Neild, Stoner-Eby, & Furstenberg, 2008). Although the results indicated

that academic, social, and teacher engagement were associated with dropout risk, a logistic

regression analysis indicated that once academic engagement was entered into the model, social

and teacher engagement were no longer significant predictors.

Creating early warning systems that help schools more effectively support all youth to stay

on track toward graduation and prepare youth who graduate college- and career-ready (Achieve,

2010) has important economic implications. For society, the numbers of youth and the estimated

6.7 million out-of-school nonworking young adults aged 16-24 are expected to reduce the tax

base directly by $1.56 trillion and cost society, in aggregate, $4.75 trillion (Belfield, Levin, &

Rosen, 2012). The economic stakes of high school success and successfully entering

postsecondary educational or vocational training as a means to increasing both personal earnings

and occupational opportunities are paramount (Neild & Boccanfuso, 2010). Through this lens,


states are beginning to frame high school success as a foundation for their future economic

outlook as retirement numbers far outnumber the future supply of high school graduates

(Sullivan, 2012).

The purpose of this study was to investigate whether assessing nonacademic factors such as

social emotional learning skills could also offer an early warning system strategy for identifying

students who are currently or in the future likely to become at risk. Understanding whether and

how social emotional learning skills predict future academic performance and dropout risk,

schools are in a better position to use assessment strategies to design personalized intervention

strategies. Furthermore, the aim of this study was to evaluate whether middle school students’

ratings on a range of social emotional learning areas could serve as an early warning system for

differentiating high and low academic performers in high school. The remainder of the literature

review provides a rationale for the social emotional learning constructs being used in the study

and evidence for their construct, concurrent, and predictive validity.

Social Emotional Learning Constructs

Social emotional learning is the ability to acquire and apply the knowledge and skills

necessary to comprehend, manage, and articulate social and emotional information, associated

with social behaviors such as maintaining positive relationships, recognizing emotions, and

making responsible decisions (Bierman, 2004; Crick & Dodge, 1994; Elias et al., 1997; Nowicki

& Mitchell, 1998)). A recent meta-analysis of over 200 teacher-led schools found that classroom

strategies designed to promote social emotional learning skills contributed to higher academic

outcomes, better connections to school, and positive behavior (Durlak, Weissberg, Dymnicki,

Taylor, & Schellinger, 2011). Specific social emotional learning skills that were found to affect

educational outcomes included self-efficacy, decision-making and problem-solving skills, and


stress and health management. The six social emotional learning skill areas in this study include

self-efficacy, motivation, social connections, perceived importance or utility of education,

psychological and emotional distress, and academic stress (Bandura, 1986; Cohen & Wills,

1985; Dohrenwend & Dohrenwend, 1974; Eccles & Wigfield, 2002; Egeland, Carlson, & Sroufe,

1993; Hobfoll, 1989; Ryan & Deci, 2002; Werner & Smith, 1992).

Academic self-efficacy refers to perceived competence in performing a range of academic

activities needed to be successful within a school setting (Solberg, O’Brien, Villarreal, Kennel,

& Davis, 1993; Solberg et al., 1998). According to Bandura (1986; 1997), self-efficacy refers to

perceived ability to successfully perform a task or behavior. As a construct, academic self-

efficacy has been consistently found to be associated with a range of academic outcomes

(Schunk & Pajares, 2009) and meta-analysis has established an average medium effect size

between academic self-efficacy and school outcomes (Multon, Brown, & Lent, 1991; Schunk &

Pajares, 2009).

Academic motivation was defined with respect to whether students are motivated to attend

school because it is perceived as a relevant, meaningful, and/or enjoyable learning experience -

what is referred to as “autonomous motivation” (Close & Solberg, 2008; Ryan & Deci, 2002,

2008). Autonomous motivation refers to engaging in an activity because of its perceived

meaningfulness and relevance. It has been well established that youth develop self-determination

(i.e., intrinsic motivation and stronger competence beliefs) when educators create a personalized,

caring, and relational classroom environment in which the youth receive choices and engage in

autonomy-supportive interactions (Deci, Schwartz, Sheinman, & Ryan, 1981).

Social connections were conceptualized as the perceived availability of three relational

connections – family, peers, and teachers. From diathesis-stress research, it has been well


established that high levels of stress are associated with increases in psychological and emotional

distress that is mediated and moderated by social support systems (Cohen & Wills, 1985;

Dohrenwend & Dohrenwend, 1974). In a recent study of anxiety among African American

youth, Lewis, Byrd, & Ollendick (2012) found that stress was associated with increased anxiety

and availability of social support was associated with lower anxiety.

Importance of school was used to indicate the degree to which students perceive their

current school and future college education as having strong utility for helping them achieve

their desired life and career goals (Eccles & Wigfield, 2002). Perceived importance of school is

conceptualized as a subjective task value that contributes to both expectations for school success

and achievement related actions and choices (Wigfield, Tonks, & Klauda, 2009). Research has

demonstrated that youth gain better course outcomes when they perceive the subject matter as

relevant and supportive of future life goals (Eccles, 2005; Harackiewicz, Durik, Barron,

Linnenbrink-Gracia, & Tauer, 2008; Hulleman, Godes, Hendricks, & Harackiewicz, 2010;

Hulleman & Harackiewicz, 2009). Perceived relevance and importance of the subject matter are

not only important in motivating youth to succeed in their coursework, but also are malleable to

classroom-based interventions. For example, Hulleman and Harackiewicz (2009) randomly

assigned high school students to receive a utility enhancing intervention whereby they would

explore how science courses are important to helping them achieve desired life goals. They

found that students with a history of low academic performance in science reported higher

perceived relevance and end of term grades than youth in either the control condition or who

entered the class with a better science academic history.

Psychological and emotional distress is conceptualized as an absence of positive affect or the

presence of unpleasant emotions, which affect level of functioning (Suldo, Gormley, DuPaul, &


Anderson-Butcher, 2014). Literature has highlighted the risk of coexistence among emotional,

psychological, and academic dimensions in which a student with problems in one domain is

more inclined to concurrently have difficulties in other domains such as academic performance.

In contrast, youth with positive academic outcomes and minimal psychological distress tend to

be better adjusted (Valdez, Lambert, & Ialongo, 2011). Furthermore, literature has demonstrated

that students with low stress were more likely to continue their education and had a higher GPA

than those that experienced higher levels of stress (Dornbusch, Mont-Reynaud, Ritter, Chen, &

Steinberg, 1991; Gillock & Reyes, 1999; Windle & Windle, 1996). A subsequent longitudinal

study, in which student mental health was examined by well-being and psychological distress

indicators, found that students with a combination of low well-being and elevated psychological

distress were most at risk for descending GPA (Suldo, Thalji, & Ferron, 2011).

Academic stress is conceptualized according to Hobfoll’s (1989) conservation of resources

model. This model posits that an activity is perceived as stressful by people who do not feel they

have the skills, emotional, or time resources needed to effectively manage a given activity.

Academic stress was operationalized in terms of the perceived difficulty youth reported having

experienced in completing a range of academic activities.

In previous research, these social emotional learning constructs were evaluated in relation to

one another and academic outcomes using structural equation modeling. Close and Solberg

(2008) demonstrated that students reporting stronger relational connections with teachers and

peers reported school as being more relevant, meaningful, and enjoyable. Perceived relevance

was associated with higher reported academic self-efficacy, which was subsequently associated

with better reported health management. The combination of academic motivation, academic

self-efficacy, and health management was associated with recording better academic outcomes


during their initial entry into the ninth grade. Gillis (2011) extended this research by showing

that academic self-efficacy mediates the relationship between motivation and academic stress

and that perceived importance of education was predicted by a combination of academic self-

efficacy and motivation. This study extends this previous research by examining whether and

how social emotional learning skills can serve as an early warning indicator for future academic

success. Specifically, this study assessed whether: (a) social emotional learning skills assessed at

the end of middle school can effectively predict future high school academic success and (b) a

subset of social emotional learning skills could serve as an early warning indicator for future

dropout risk. The potential significance of this study is that social emotional learning skills may

offer schools an early warning indicator of whether and why students may become at risk for

future school failure.

Method

Participants

A total of 4,797 students were surveyed during the summer prior to beginning the ninth

grade as part of a summer transition-to-high-school program in a large western urban school

district. The total sample represents three annual cohorts. A total of 1,830 students participated

in Cohort 1 beginning in 2007, 1,728 participated in Cohort 2 beginning in 2008, and 1,214

participated in Cohort 3 beginning in 2009. A total of 35% of the students were Latino, 11%

were African American, 10% White, 1% American Indian, 2% Asian or Pacific Islander, and the

remaining were either not identified or identified as more than one race or ethnicity. For gender,

51% were identified as female.

Measures


Academic self-efficacy. This measure is a 22-item scale that assesses the degree to which an

individual believes he or she can successfully perform a number of academic and school-related

tasks (Gillis & Sedivy, 2008). Responses range from 1 (not at all confident) to 5 (extremely

confident), with positive scores indicating greater belief in the ability to perform academic and

school-related tasks. Principal components analysis using a national sample of students (Gillis &

Sedivy, 2008) identified three subscales: (a) social, (b) classroom, and (c) test taking. Internal

consistency coefficients for the subscales and the total scale using Cronbach’s alpha were .79,

.83, .69, and .91, respectively.

Academic Motivation. This measure is a 15-item scale that assesses the reasons students

attend school (Close & Solberg, 2008). It was derived using self-determination theory (Deci &

Ryan, 1985), which delineates two dimensions of internal motivation: performing a task because

it is perceived as meaningful and performing a task because it is deemed enjoyable. Students

indicate the extent to which they agree with a series of statements using a scale ranging from 1

(strongly disagree) to 5 (strongly agree). Principal components (Gillis & Sedivy, 2008) and

confirmatory factor analysis (Solberg, Howard, Gresham, & Carter, 2012) using a national

sample of students identified two internal control subscales: (a) attending school because it is

deemed enjoyable (enjoy school), and (b) attending school because it is deemed meaningful

(meaningfulness of school). Solberg et al. (2012) report internal consistency was .64 (enjoy

school) and .77 (meaningfulness of school), and the total scale was .79.

Social Connections. This measure consists of 16 items that relate to the perceived

availability of support from teachers, family, and peers. The items were modeled after the Social

Provision Scale (Russell & Cutrona, 1984), which assesses perceived availability of family

support. Students evaluated the degree to which they perceived availability of support using a


scale that ranging from 1 (strongly disagree) to 5 (strongly agree). Principal components

analysis using a national sample of students (Gillis & Sedivy, 2008) identified three subscales:

(a) family support, (b) teacher connections, and (c) peer connections. The three subscales and the

scale composite score were found to have adequate internal consistency, with coefficients of .80,

.81, .76, respectively, and .84 for the total scale.

Importance of School. This measure consists of 10 items that ask about individuals’ beliefs

about the importance of education in achieving their life goals. Students rated the importance of

each item using a scale ranging from 1 (strongly disagree) to 5 (strongly agree). The items were

analyzed using principal components analysis (Gillis & Sedivy, 2008) and two subscales were

identified: (a) importance of school, and (b) importance of college. The two subscales and the

scale composite score were found to have adequate internal consistency: .84 (school), .90

(college), and .92 (total scale).

Psychological and emotional distress. This 23-item scale assesses how often an individual

experiences emotional-psychological and physical health–related concerns (Gillis & Sedivy,

2008). Individuals provide responses on this measure to a series of statements indicating how

often they have experienced each health-related concern during the past month, using a scale

ranging from 1 (never) to 5 (always). Principal components analysis using a national sample of

students (Gillis & Sedivy, 2008) identified five subscales: (a) agitation, (b) eating problems, (c)

feeling blue, (d) sleeping problems, and (e) physical problems. Each of the subscales and the

total composite scale demonstrated adequate reliability, with alpha coefficients of .86, .83, .85,

.86, .75, and .95, respectively.

Academic stress. This 22-item scale assesses how often an individual experiences

difficulties completing a variety of academic-related tasks (Gillis & Sedivy, 2008). On this


measure, students evaluate the stressfulness of a series of tasks by indicating the degree of

difficulty they experience, with scores ranging from 1 (not at all difficult) to 5 (extremely

difficult). Principal components analysis using a national sample of students (Gillis & Sedivy,

2008) identified three subscales: (a) academic, (b) social, and (c) financial. The three subscales

and the scale composite score were found to have adequate internal consistency, with

coefficients of .86, .86, .88, and .94, respectively.

Dependent Variables. Three indicators of academic progress in high school were employed

as dependent variables. Cumulative grade point average (GPA), measured on a scale of 1 to 5,

corresponded to traditional course grades in all subjects (1= F, 4 = A, 5 = Advanced Placement

A). Second, a composite index of high school performance, academic success, was created

based on early warning indicators by Balfanz et al. (2010), using district-provided data for

attendance, suspension reports, and cumulative grade point averages for students across each of

the three cohorts. Each indicator was standardized separately by cohort, and the average of the

non-missing standardized values was computed in order that all three cohorts could be combined.

Because the indicator for behavior reports is a negative value with higher numbers indicating

more incidents, the average for behavior reports was multiplied by -1 in order to create a score

that reflected better behavior. Once average standardized ratings were computed separately for

attendance, behavior reports, and grades, the average of these three composites was generated to

create a single indicator of high school performance, with higher z-scores representing more

success. Students from the 2007 cohort had standardized scores from 3 academic years

averaged, the 2008 cohort had 2 years of standardized scores averaged, and the 2009 cohort had

1 year of standardized scores averaged. Finally, the academic-success indicator was divided into

quartiles with the upper 25% of the distribution comprising the high-success group and students


falling in the lower 25% of the distribution comprising the low-success group. Dividing the high

school success indicator into quartiles resulted in a total of 835 students scoring below the 25th

percentile (low-success Group) and 1001 students scoring above the 75th percentile (high-

success group).

A dichotomous dependent variable, progress toward graduation, was generated using the

ratio of credits earned to credits attempted. Students were classified as not making progress

toward graduation if they had failed 14% or more of their courses or if the district had identified

them as having dropped out. A total of 4.1% of the sample was identified by the district as

having dropped out. The total number of students classified as not making progress towards

graduation was 2,096 or 45.6% of the total sample.

Missing Data. Approximately 8% of cases were missing values for one or more

dependent variables. Missing values were not imputed for dependent variables; cases with

missing data for a dependent variable were dropped from the analysis involving that dependent

variable. Missing items within social emotional subscales ranged from 0.8% to 3.7% of cases,

depending upon the subscale. In those cases, if two or more items were answered, the mean of

the answered items was substituted for missing items within the subscale. If fewer than two of

the items for a scale were complete, the case was dropped from analysis.

Results

Social Emotional Learning Skills Predicting Academic Success

Table 1 reports the means and correlations among the variables. In order to evaluate

whether a student’s reported measures on the Success Highways (Gillis & Sedivy, 2008) 18

social emotional learning subscales were associated with established measures of academic

success, a one-way multivariate analysis of variance was conducted using high or low academic


success as the independent variable and the 18 social emotional learning subscales as the

dependent variable. First, a significant main effect was found (Wilks’ Lambda = .835, F [18,

1817] = 19.88, p < .000, eta2 = .17). The univariate tests reported in Table 2 indicated that

students in the high-success Group achieved significantly higher social emotional learning skills

than students in the low-success Group for all 18 subscales. The results indicated, therefore, that

as an early warning indicator of future academic performance, each of the 18 social emotional

learning subscales was able to effectively differentiate between students classified in the high

and low academic-success groups.

The second analysis employed multiple regression and path analysis to determine the

percent of variance in cumulative GPA attributable to the measured social emotional variables.

Bivariate correlations among the independent variables (Table 1) were examined to identify

variables with correlations greater than r = .3 that would contribute to multicollinearity if used

together in a regression analysis. The data set was then randomly divided into two equal-sized

sets, A and B, with A designated the exploratory data set and B the confirmatory data set. In the

exploratory data set, two variables, importance of college, and psychological and emotional

stress, emerged as the highest social-emotional bivariate correlates of cumulative GPA (r = .24, r

= .18 respectively). The remaining independent variables were omitted to avoid multicollinearity.

In combination in a multiple-regression analysis, they explained 6.9% of the variance in

cumulative GPA (R = .263, R2 = .069, p < .001). Path analysis was used to determine the unique

variance in cumulative GPA associated with importance of college and psychological and

emotional stress, controlling for first-year GPA (Figure 1). The path coefficients were .000 for

stress, and .375 (p. < .001) for importance of college, the latter accounting for 14.1% of variance

in cumulative GPA not associated with Year 1 GPA or stress. The same analysis conducted with


the confirmatory data set yielded path coefficients shown in parentheses in Figure 1. Both

importance of college and stress were significant predictors of cumulative GPA after controlling

for first-year GPA, and together accounted for 25.9% of the variance in cumulative GPA (R =

.509, R2 = .259, p < .001).

Social Emotional Learning Skills Predicting Progress Toward Graduation

Discriminant analysis was used to determine whether a subset of social emotional

learning skills could effectively predict whether students would be likely to make successful

progression towards graduation or whether they would be become at risk for dropping out of

high school. As in the previous analysis, the sample was randomly divided in half with one half

designated as Sample A for exploratory purpose and the other as Sample B for confirmatory

cross-validation. As part of the exploratory phase, an examination of the correlation matrix was

conducted with Sample A to reduce the number of subscales and the multicollinearity among

them prior to conducting the discriminant analysis, but a decision was made to retain one

subscale from each of the six constructs in order to test the full theoretical model. The subscale

from each of the six social emotional learning skills with the highest correlation with the

dependent variable (progress towards graduation) was included in the discriminant analysis. A

confirmatory cross-validation analysis was then conducted by applying the discriminant equation

generated from Sample A with Sample B.

In the exploratory phase, the six subscales that were selected for entry into the

discriminant analysis included: importance of college, meaningful motivation to attend school,

classroom confidence, family connections, physical symptoms of distress (negative), and

academic stress (negative). The initial discriminant analysis with Sample A indicated that family

connections was negatively associated with progress towards graduation which is not consistent


with previous research and was therefore deemed to have resulted from multicollinearity. Family

connections was therefore dropped from the analysis and a five-predictor discriminant analysis

was conducted which yielded a small effect size (canonical correlation = .228 explaining 5.2% of

the variance in progress towards graduation) and a significant fit (Wilks’ Lambda = .948, χ2 =

90.753, p < .001), indicating that the model including these five variables was able to

significantly discriminate between the two groups. A positive cutoff score of 0.10 was selected

to yield a higher hit rate for failing students (balanced by a lower hit rate for students on track for

graduation).

The unstandardized discriminant function generated in the exploratory phase was then

used to compute a predicted score for each student in Subsample B:

Ŷ = .816 (imp of college) + .335 (meaningful motivation) + .205 (classroom conf) - .252

(physical symptoms) -.508 (academic stress) – 4.124

The same cutoff score of 0.10 was then used in a confirmatory analysis used to predict

membership in progress toward graduation and correctly predicted 60.5% of students not making

progress and 56.5% of students making progress. This provides strong evidence that the

prediction formula is stable, and not dependent on a particular sample of students (χ2 = 47.82, p

< .001).

Discussion

Early detection of students who may become at risk for performing poorly in high school,

who may be at risk for dropping out of school, or both, continues as a critical issue with recent

estimates that nearly 39 million youth aged between 16 and 24 years old are out of school and

not employed (Belfield, Levin & Rosen, 2012). This study evaluated the degree to which social


emotional learning skills could be used as an indicator of future academic outcomes in three

complementary ways. First, a composite of academic success was created using indicators

identified by Balfanz et al. (2010) related to attendance, behavior, and course performance. We

found that eighth-grade indicators of social emotional learning subsequently differentiated

between students in the top 25% and bottom 25% academically in high school. Second, path

analysis was employed to predict cumulative high school GPA by first separating the entire data

set into two equal-sized random samples, and using the first to conduct an exploratory analysis

based on observed correlations and the second as a confirmatory analysis involving the same

variables. In both analyses, perceived importance of college was a significant predictor of

cumulative GPA after controlling for GPA in the first year of high school. Third, a measure of

progress towards graduation was generated using a combination of the percentage of high school

courses passed and whether the district had designated the student had dropped out of school.

We found that a combination of five social emotional learning subscales effectively

discriminated between students making positive progress towards high school graduation and

those identified as having dropped out or failed more than 14% of their courses. These social

emotional learning subscales included importance of college, meaningful motivation, classroom

self-efficacy, physical symptoms, and academic stress.

Each of the social emotional learning skills selected for the study are malleable and

therefore one of the implications of this research is that response to intervention (Duffy, 2007)

strategies could use social emotional learning skills assessment to tailor the types of tiered

prevention efforts being offered to students. The underlying logic is that improving social

emotional skills will enhance academic performance by helping students become more self-

regulated and engaged learners (Zimmerman, 2011) who perceive the relevance of their


education for achieving future goals (Hulleman et al., 2010; Hulleman & Harackiewicz, 2009).

In particular, the study supports the importance of helping students to articulate future goals

including attending college. The study also supports a strategy of assessing the social emotional

learning of middle and high school students and offering tailored intervention strategies to

address areas identified as low. Such opportunities could be implemented in small-group

settings unless additional information indicates that one-on-one services are advisable.

Caution should be made in generalizing from the results of this correlational study. The

sample represents one school district, and, although the district did find that the program

participants matched were representative of the school population, replication with other school

districts is needed. Also, social emotional learning was assessed using self-report data and

therefore all limitations associated with traditional survey research are warranted. Future

research could also evaluate whether the same or a different pattern of social emotional learning

subscales are predictive of progress toward graduation for students from different racial and

ethnic backgrounds, for students from different income levels, and for students with disabilities.


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Table 1

Summary of Intercorrelations, Means, and Standard Deviations for Scores on Social Emotional

Learning Measures and Progress Towards On-Time Graduation

Measure 1 2 3 4 5 6 1. Importance of College ---- 2. Meaningful

Motivation .439** --- 3. Classroom

Confidence .490** .404** ---

4. Physical Symptoms -.123** -.071** -.180** --- 5. Academic Stress -.194** -.132** -.320** .474** --- 6. Progress towards

Graduation .157** .120** .155** -.102** .142** --- M 4.43 3.73 4.14 1.92 2.22 0.55 SD 0.70 0.64 0.59 0.83 0.79 0.50

Note. * p < .05, ** p< .01, *** p < .001


Table 2

Univariate Tests for Scores on Social Emotional Learning Measures for the Low and High Success Group

Dependent Variable

Success Groups

F Statistic Sig.

Eta Squared

Low Mean Std. Error

High Mean Std. Error

School Importance

Importance of School 4.448 0.02 4.648 0.019 52.18 0.00 0.03

Importance of College 4.259 0.024 4.608 0.022 117.79 0.00 0.06

Academic Self-Efficacy

Social 3.908 0.022 4.035 0.02 18.84 0.00 0.01

Classroom 4.021 0.02 4.274 0.018 89.13 0.00 0.05

Test-Taking 3.93 0.023 4.085 0.021 24.1 0.00 0.01

Social Connections

Family Support 4.071 0.024 4.349 0.022 70.83 0.00 0.04

Teacher Connections 3.664 0.025 3.885 0.023 41.41 0.00 0.02

Peer Connections 4.279 0.025 4.455 0.023 26.73 0.00 0.01

Academic Stress

Academic Stress 2.401 0.027 2.021 0.025 107.08 0.00 0.06

Social Stress 2.161 0.027 1.888 0.024 57.51 0.00 0.03

Financial Stress 1.884 0.029 1.557 0.026 71.19 0.00 0.04

Distress

Sleeping Problems 2.279 0.035 2.025 0.032 29.15 0.00 0.02

Feeling Blue 2.187 0.034 1.918 0.031 34.73 0.00 0.02

Agitation 2.283 0.029 1.838 0.027 124.82 0.00 0.06


Eating Problems 2.057 0.032 1.773 0.029 42.322 0.00 0.02

Physical Symptoms 2.053 0.028 1.77 0.026 54.23 0.00 0.03

Motivation

Enjoy School 3.467 0.026 3.691 0.024 40.42 0.00 0.02

Meaningfulness 3.581 0.021 3.855 0.02 88.41 0.00 0.05

Note. * p<0.05, **p<0.01, ***p<0.001

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