|Title:||Validation of a Measure to Assess the Social-Emotional Health of Secondary Students|
|Principal Investigator:||Furlong, Michael||Awardee:||University of California, Santa Barbara|
|Program:||Social and Behavioral Context for Academic Learning [Program Details]|
|Award Period:||4 years (7/1/2016-6/30/2020)||Award Amount:||$1,364,134|
Co-Principal Investigators: Karen Nylund-Gibson, Erin Dowdy
Purpose: In this study, the research team will refine and further validate the Social Emotional Health Survey (SEHS), a measure designed to assess the social-emotional assets of high school students. The transition into high school is characterized by increasing academic demands, increasingly more diverse and complex social interactions/relationships, and increasing pressure associated with the looming transition into adult life and related responsibilities. This assessment meaningfully measures key elements of high school students' social-emotional health that are correlated with positive academic, social, behavioral, and quality of life outcomes. These key elements are essential for educational success and set the foundation for the successful transition into adulthood.
Project Activities: Researchers will refine the SEHS and administer the SEHS and other key measures to cross-sectional and prospective, longitudinal samples to establish substantive, structural, and external characteristics of the SEHS (interpretation and usability aims).
Products: The products of this project include validity and usability information about the SEHS presented in a practitioner-focused manual and peer-reviewed publications.
Setting: The research will take place with high school students across California. Researchers will collect data for the cross-sectional sample from approximately 90 high schools using a stratified, two-stage, cluster sampling design from 3 geographic regions of California. In addition, researchers will collect data longitudinally from four high schools in California.
Sample: The target population is high school students (Grades 9–12). The research team will use three samples of high school students: (a) a cross-sectional sample drawn from 90 high schools throughout California in years 2 and 3 (N=135,000); (b) a longitudinal sample drawn from four California high schools during years 2–4 (N=5,000); and (c), a short-term stability sample (N=320) drawn from four high schools in year 2 (these students not included in longitudinal analyses).
Assessment: The SEHS is a measure designed to assess the social-emotional assets of high school students and fits within multi-tiered systems of support and response-to-intervention frameworks that are regularly employed in schools for the identification and care of students with learning or social-emotional needs. One use of the SEHS is for whole school assessment (Tier 1) to monitor the status of all students' social emotional health. Another primary use of the SEHS is to assess the social-emotional health of individual students (Tier 2, 3) The SEHS is a 36-item self report measure that includes 12 subscales (positive social-emotional components that are measured directly) that are linked to 4 first-order latent factors (positive-social domains: belief-in-self, belief-in-others, emotional competence, and engaged living). Researchers will provide validity and usability information and will examine relations between the SEHS and educational outcomes such as test scores, grades, attendance, credit earned, and disciplinary referrals concurrently and longitudinally.
Research Design and Methods: The researchers will refine and validate SEHS in six steps. Specifically, the researchers will: (1) Refine the content and format of the SEHS for use in high schools; (2) Verify the construct validity of the SEHS for use in high schools; (3) Investigate the criterion validity of the scores obtained from the SEHS; (4) Investigate the consistency and stability of student responses to the SEHS (5) Investigate strategies for evaluating the credibility of SEHS self-reports to facilitate interpretation and appropriate use by high schools; and (6) Investigate students' SEHS responses for the presence of empirically-defined interpretation subtypes or classes. The research team will embed cross-sectional data collection within the existing California Healthy Kids Survey (CHKS) administration process already in place with districts and schools. They will use a stratified, two-stage, cluster sampling design. For the longitudinal data collection, researchers will partner with four high schools to administer via an SEHS online format. To evaluate the short-term stability of the SEHS, researchers will randomly select 80 students in each of the four longitudinal sample schools.
Control Condition: There is not a control condition for this project.
Key Measures: The SEHS is the focal instrument is built upon a developmentally-grounded theoretical model and has preliminary evidence of sound psychometric properties across groups of diverse students. The SEHS is a 36-item self report measure that includes 12 positive social-emotional components subscales (such as optimism, peer support, and self-awareness) that are linked to 4 first-order latent factors (belief-in-self, belief-in-others, emotional competence, and engaged living), which are hypothesized to load on to a second-order general factor called covitality. Researchers will provide validity and usability information including information about how the SEHS assesses the intended constructs of social/emotional health, relates to other social/emotional factors that support or diminish learning (e.g., personal distress, school satisfaction, school connectedness, student learning strategies, subjective well-being), and how it can be used in schools.
Data Analytic Strategy: The primary data analytic methods to accomplish the psychometric, interpretation, and usability aims for this study include: exploratory and confirmatory factor analysis, measurement invariance analysis, internal consistency, correlational reliability and validity analyses, analysis of variance, latent profile analysis, and latent transition analysis.