Latent Growth Model Analysis of SAEBRS Scores: Establishing Typical Rate of Growth and Predictive Validity of Academic Outcomes

Author

Thomas Koza, University of South Florida

Graduation Year

2024

Document Type

Ed. Specalist

Degree

*Ed.S.

Degree Name

Education Specialist (Ed.S.)

Degree Granting Department

Curriculum, Instruction, and Learning

Major Professor

Nathaniel von der Embse, Ph.D.

Committee Member

Shannon Suldo, Ph.D.

Committee Member

Eunsook Kim, Ph.D.

Keywords

Universal Screening, Latent Growth Modeling, Social Emotional Well-Being

Abstract

There are rising rates of student social-emotional and behavioral (SEB) concerns for students in the United States. Universal screening tools, such as the mySAEBRS (student self-report) and SAEBRS (teacher report), can be used to help identify students who may need additional SEB support. The current study uses Latent Growth Modeling (LGM) as a novel approach to identify growth trajectories of student mySAEBRS and SAEBRS domain scores (i.e., social domain, academic domain, and emotional domain) and aReading scores to provide insight into typical SEB growth rates and the ability of these domain scores to predict reading achievement. The study uses a sample of 1,479 students across grades 2-6 with scores across four time points (i.e., four school semesters) for mySAEBRS and SAEBRS domain score growth trajectories and three time points for aReading score growth trajectories (i.e., three school semesters). Results of the study indicate that there were significant positive slope means across all three domains for both the mySAEBRS and SAEBRS. There were also significant slope variances found across the majority of three domains for both screening tools, suggesting that students varied in the amount that they grew in scores over time. When evaluating the predictive validity of mySAEBRS and SAEBRS scores on reading achievement growth, all but two domains (i.e., mySAEBRS social and mySAEBRS emotional), were found to be significant predictors of aReading score growth. The findings from the study provide valuable insight into typical growth rates of SEB well-being with the use of mySAEBRS and SAEBRS domain scores and the ability of these scores to predict reading achievement.

Scholar Commons Citation

Koza, Thomas, "Latent Growth Model Analysis of SAEBRS Scores: Establishing Typical Rate of Growth and Predictive Validity of Academic Outcomes" (2024). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10816