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

Area of Science:

• Educational Psychology
• Developmental Psychology
• Applied Behavior Analysis

Background:

• Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models require effective resource allocation for student interventions.
• Identifying distinct student profiles based on academic and behavioral risk is crucial for targeted support.

Purpose of the Study:

• To identify latent classes of third-grade students based on academic and behavioral risk indicators.
• To determine if these identified classes predict end-of-year academic achievement.
• To inform the allocation of intervention resources within RTI and MTSS frameworks.

Main Methods:

• Latent Class Analysis (LCA) was performed on a sample of 517 third-grade students.
• Fall screening data in reading, mathematics, and behavior were utilized as indicators.
• Predictive validity was assessed against end-of-year statewide achievement test scores.

Main Results:

• Three distinct subclasses were identified: Tier 1 (minimal concerns, 32%), Tier 2 (academic risk, some behavior risk, 37%), and Tier 3 (significant academic and behavior problems, 31%).
• All identified classes were predictive of end-of-year statewide achievement test performance.
• A significant performance hierarchy was observed: Tier 1 > Tier 2 > Tier 3.

Conclusions:

• Brief screening measures can effectively identify distinct student classes with varying academic and behavioral risk levels.
• These identified classes demonstrate predictive power for later academic success.
• Findings support differentiated intervention strategies and resource allocation for students at risk of academic failure and behavioral issues.