Assessing and Decomposing Treatment Effect Variation of a Reading Intervention for Struggling Readers

Abstract

This study investigated Treatment Effect Variation (TEV) in a reading intervention for struggling readers using an alternative analytic approach: Individual Treatment Effect (ITE). Prior Aptitude-by-Treatment Interaction (ATI) or moderator studies, which sought to understand for whom a reading intervention works, primarily utilized regression framework to examine conditional Average Treatment Effects (ATEs) across subgroups defined by single pre-treatment covariates. However, detecting moderation effects can be especially challenging in reading intervention research due to inherent data-related constraints such as small sample sizes and low statistical power. This study aimed to address these challenges by employing a model-free method proposed by Ding et al. (2016; 2019) to detect and decompose TEV. Specifically, the two primary research questions were: (1) Can the ATE adequately summarize the intervention’s efficacy, or is there significant variation in treatment effects across individuals? (2) What proportion of the total variation in treatment effects can be explained by pre-treatment characteristics versus idiosyncratic variation? The data used for this study was from Project 3 of Project KIDS (Kids and Individual Differences in Schools; van Dijk et al., 2022). Participants were 203 first-grade students in the Dynamic RTI (treatment condition) and 128 in the traditional RTI (control condition). The Fisher randomization test was used to test the null hypothesis of no TEV across four reading outcomes. The results indicated no significant variation in treatment effects across individuals participating in Project 3, suggesting that the ATE sufficiently summarized the treatment effects in this sample. Consequently, the second research question regarding the decomposition of TEV could not be addressed due to the absence of significant variation. These findings underscore the importance of verifying the presence of TEV before conducting detailed analyses of moderation effects. This nonparametric approach can help confirm the existence of heterogeneity in individual responses to reading interventions where inherent data-related constraints exist. In conclusion, preliminary assessments of TEV provide valuable insights that can inform and strengthen the analysis of moderator effects, contributing to a more nuanced understanding of treatment effects in reading intervention research.