The effects of a tier 2 reading comprehension intervention aligned to tier 1 instruction for fourth graders with inattention and reading difficulties.

This study reports a secondary analysis from a quasi-experimental design study (N = 13 schools) to examine the effects of aligned Tier 1 (T1) and Tier 2 (T2) instruction for a subsample of fourth graders with inattention and reading difficulties. Of this sample (N = 63 students), 100% received free- or reduced-price lunch, 92% identified as Hispanic, and 22% received special education services. T1 instruction focused on implementing practices to support reading comprehension and content learning during social studies instruction. The aligned T2 intervention focused on remediating reading comprehension difficulties using the same evidence-based practices implemented in T1, thus supporting students with connecting learning and applying skills across settings. Schools were assigned to one of three conditions: (a) aligned T1-T2 instruction; (b) nonaligned T1-T2 instruction, in which T1 and T2 practices were not intentionally aligned; or (c) business-as-usual (BaU) T1 and T2 practices. No significant differences were detected between the nonaligned T1-T2 and BaU conditions on student outcomes. However, large, statistically significant effects were detected in favor of the aligned T1-T2 condition compared to BaU on measures of content knowledge (Unit 1 ES = 0.85; Unit 2 ES = 1.46; Unit 3 ES = 0.79), vocabulary (Unit 1 ES = 0.88; Unit 2 ES = 0.85), and content reading comprehension (ES = 0.79). The aligned T1-T2 condition also outperformed the nonaligned T1-T2 condition on content knowledge (Unit 2 ES = 1.35; Unit 3 ES = 0.56), vocabulary (Unit 1 ES = 0.82), and the content reading comprehension assessment (ES = 0.69). Various effect sizes were not different from zero after correcting for clustered data. Although the magnitude of the effect sizes suggested promise, additional research is needed to fully understand the effects of aligned instruction on the reading outcomes of students with inattention and reading difficulty.

Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data.

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2023 APA, all rights reserved).