Young-Ri Lee

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 becau

Meta-analysis methodology has evolved with the development of more robust statistical techniques; however, few reviews in special education have focused specifically on methodological rigor in meta-analyses. In this study, we examined 29 meta-analyses of mathematics interventions published from 2000 to 2022 to determine the extent to which researchers have applied four best practices in meta-analyses focused on mathematics interventions for students with disabilities. Our findings were (a) studi

Intelligent Tutoring Systems are a genre of highly adaptive software providing individualized instruction. The current study was a conceptual replication of a previous randomized control trial that incorporated the intelligent tutoring system Native Numbers, a program designed for early numeracy instruction. As a conceptual replication, we kept the method of instruction, the demographics, the number of kindergarten classrooms (n = 3), and the same numeracy and intrinsic motivation screeners as t

The present study examines bias in parameter estimates and standard error in cross-classified random effect modeling (CCREM) caused by omitting the random interaction effects of the cross-classified factors, focusing on the effect of a sample size within cells and ratio of a small cell. A Monte Carlo simulation study was conducted to compare the correctly specified and the misspecified CCREM. While there was negligible bias in fixed effects, substantial biases were found in the random effects of

An essential element for increasing student mathematics achievement is providing teachers with professional development (PD) aimed at the design and delivery of high-quality mathematics instruction. To date, however, there is a lack of consistent data on the efficacy of PD on student outcomes; moreover, there is a need to explore PD characteristics as moderators of student outcomes. The purpose of this meta-analysis was to synthesize the effects of teacher PD on mathematics outcomes for students

Intelligent Tutoring Systems are a genre of highly adaptive software providing individualized instruction. The current study was a conceptual replication of a previous randomized control trial that incorporated the intelligent tutoring system Native Numbers, a program designed for early numeracy instruction. As a conceptual replication, we kept the method of instruction, the demographics, the number of kindergarten classrooms (n = 3), and the same numeracy and intrinsic motivation screeners as t

A large body of research documents underlying cognitive factors, many of which are shared, in students with reading disabilities (RDs), math disabilities (MDs), comorbid reading and math disabilities (RD + MD), as well as students with attention-deficit/hyperactivity disorder (ADHD) and students with RD and ADHD. In an effort to examine differences in reading, mathematics, and cognitive outcomes among these students, we investigated the outcomes between these groups across the published research

Supplementary materials to: Grimes, K. R., Park, S., McClelland, A., Park, J., Lee, Y. R., Nozari, M., Umer, Z., Zaparolli, B., & Bryant, D. (2021). Effectiveness of a numeracy intelligent tutoring system in kindergarten: A conceptual replication. Journal of Numerical Cognition, 7(3), 388–410. https://doi.org/10.5964/jnc.6931

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 one 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 bec