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Glaman, Ryan; Chen, Qi; Henson, Robin K. – Journal of Experimental Education, 2022
This study compared three approaches for handling a fourth level of nesting when analyzing cluster-randomized trial (CRT) data. Although CRT data analyses may include repeated measures, individual, and cluster levels, there may be an additional fourth level that is typically ignored. This study examined the impact of ignoring this fourth level,…
Descriptors: Randomized Controlled Trials, Hierarchical Linear Modeling, Data Analysis, Simulation
Kyle Cox; Ben Kelcey; Hannah Luce – Journal of Experimental Education, 2024
Comprehensive evaluation of treatment effects is aided by considerations for moderated effects. In educational research, the combination of natural hierarchical structures and prevalence of group-administered or shared facilitator treatments often produces three-level partially nested data structures. Literature details planning strategies for a…
Descriptors: Randomized Controlled Trials, Monte Carlo Methods, Hierarchical Linear Modeling, Educational Research
Bulus, Metin; Dong, Nianbo – Journal of Experimental Education, 2021
Sample size determination in multilevel randomized trials (MRTs) and multilevel regression discontinuity designs (MRDDs) can be complicated due to multilevel structure, monetary restrictions, differing marginal costs per treatment and control units, and range restrictions in sample size at one or more levels. These issues have sparked a set of…
Descriptors: Sampling, Research Methodology, Costs, Research Design
Kelcey, Ben; Shen, Zuchao – Journal of Experimental Education, 2020
When well-implemented, mediation analyses play a critical role in probing theories of action because their results help lay the ground work for the critical development of a treatment and the iterative advancement of theories that are foundational to a discipline. Despite strong interest in designs that incorporate mediation, few studies have…
Descriptors: Research Design, Sampling, Statistical Analysis, Hierarchical Linear Modeling
Dong, Nianbo; Kelcey, Benjamin; Spybrook, Jessaca – Journal of Experimental Education, 2018
Researchers are often interested in whether the effects of an intervention differ conditional on individual- or group-moderator variables such as children's characteristics (e.g., gender), teacher's background (e.g., years of teaching), and school's characteristics (e.g., urbanity); that is, the researchers seek to examine for whom and under what…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Intervention, Effect Size
Heyvaert, Mieke; Moeyaert, Mariola; Verkempynck, Paul; Van den Noortgate, Wim; Vervloet, Marlies; Ugille, Maaike; Onghena, Patrick – Journal of Experimental Education, 2017
This article reports on a Monte Carlo simulation study, evaluating two approaches for testing the intervention effect in replicated randomized AB designs: two-level hierarchical linear modeling (HLM) and using the additive method to combine randomization test "p" values (RTcombiP). Four factors were manipulated: mean intervention effect,…
Descriptors: Monte Carlo Methods, Simulation, Intervention, Replication (Evaluation)