Using latent class analysis to identify academic and behavioral risk status in elementary students.

Abstract

Identifying classes of children on the basis of academic and behavior risk may have important implications for the allocation of intervention resources within Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models. Latent class analysis (LCA) was conducted with a sample of 517 third grade students. Fall screening scores in the areas of reading, mathematics, and behavior were used as indicators of success on an end of year statewide achievement test. Results identified 3 subclasses of children, including a class with minimal academic and behavioral concerns (Tier 1; 32% of the sample), a class at-risk for academic problems and somewhat at-risk for behavior problems (Tier 2; 37% of the sample), and a class with significant academic and behavior problems (Tier 3; 31%). Each class was predictive of end of year performance on the statewide achievement test, with the Tier 1 class performing significantly higher on the test than the Tier 2 class, which in turn scored significantly higher than the Tier 3 class. The results of this study indicated that distinct classes of children can be determined through brief screening measures and are predictive of later academic success. Further implications are discussed for prevention and intervention for students at risk for academic failure and behavior problems. (PsycINFO Database Record

Empirically Derived Subclasses of Academic Skill Among Children at Risk for Behavior Problems and Association With Distal Academic Outcomes

“…Classes identified through these analyses were similar to those identified by King et al (2016) and Reinke et al (2008). Specifically, previous studies have identified three to four classes of children that are primarily differentiated by academic skill level.…”

“…In addition, VLMR indicated this model was significantly better than the two-class solution, and entropy values were adequate. Finally, based on substantive theory, parsimony, interpretability, and findings in related studies (King et al, 2016), a three-class solution was further indicated as optimal model fit.…”

“…Results of that study indicated that not only did meaningful latent classes of students exist, these classes were also predictive of success on a statewide achievement measure. With predictable negative outcomes in the same school year as the screening, implications of this study include the importance of targeted intervention efforts early in the school year for students with demonstrated risk (King, Lembke, & Reinke, 2016). Although these important findings can be used to inform early intervention efforts, a significant limitation of previous work has been the failure to identify academic skill patterns among students with behavioral risk.…”

Predicting Academics via Behavior Within an Elementary Sample: An Evaluation of the Social, Academic, and Emotional Behavior Risk Screener (Saebrs)

“…When paired, academic and behavioral screening data could be used to identify students displaying (a) limited academic skills alone, (b) problem behavior alone, or (c) limited academic skills with related problem behavior. Recent research has supported differentiation of students into such categories to inform decisions regarding targeted and intensive supports (King, Lembke, & Reinke, ).…”

Interpretation and Use of the Social, Academic, and Emotional Behavior Risk Screener: A Latent Transition Approach

“…Person-centered analyses, such as LPA, are useful in capturing more specific patterns within samples than variable-centric analyses capture (von Eye & Wiedermann, 2015). Others have used person-centered analyses to increase sensitivity in detecting behavioral risk (see King, Lembke, & Reinke, 2016;Reinke et al, 2008).…”