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Lane, Sean P.; Kelleher, Bridgette L. – Developmental Psychology, 2023
Recruiting participants for studies of early-life longitudinal development is challenging, often resulting in practical upper bounds in sample size and missing data due to attrition. These factors pose risks for the statistical power of such studies depending on the intended analytic model. One mitigation strategy is to increase measurement…
Descriptors: Longitudinal Studies, Child Development, Hierarchical Linear Modeling, Research Design
Mariola Moeyaert; Panpan Yang; Yukang Xue – Journal of Experimental Education, 2024
We have entered an era in which scientific evidence increasingly informs research practice and policy. As there is an exponential increase in the use of single-case experimental designs (SCEDs) to evaluate intervention effectiveness, there is accumulating evidence available for quantitative synthesis. Consequently, there is a growing interest in…
Descriptors: Meta Analysis, Research Design, Synthesis, Patients
Raykov, Tenko; Menold, Natalja; Leer, Jane – Educational and Psychological Measurement, 2022
Two- and three-level designs in educational and psychological research can involve entire populations of Level-3 and possibly Level-2 units, such as schools and educational districts nested within a given state, or neighborhoods and counties in a state. Such a design is of increasing relevance in empirical research owing to the growing popularity…
Descriptors: Hierarchical Linear Modeling, Computation, Statistical Analysis, Research Design
Wei Li; Yanli Xie; Dung Pham; Nianbo Dong; Jessaca Spybrook; Benjamin Kelcey – Asia Pacific Education Review, 2024
Cluster randomized trials (CRTs) are commonly used to evaluate the causal effects of educational interventions, where the entire clusters (e.g., schools) are randomly assigned to treatment or control conditions. This study introduces statistical methods for designing and analyzing two-level (e.g., students nested within schools) and three-level…
Descriptors: Research Design, Multivariate Analysis, Randomized Controlled Trials, Hierarchical Linear Modeling
Raykov, Tenko; DiStefano, Christine – Educational and Psychological Measurement, 2022
A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to…
Descriptors: Hierarchical Linear Modeling, Correlation, Evaluation, Research Design
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
Shen, Zuchao; Kelcey, Benjamin – Journal of Research on Educational Effectiveness, 2022
Optimal sampling frameworks attempt to identify the most efficient sampling plans to achieve an adequate statistical power. Although such calculations are theoretical in nature, they are critical to the judicious and wise use of funding because they serve as important starting points that guide practical discussions around sampling tradeoffs and…
Descriptors: Sampling, Research Design, Randomized Controlled Trials, Statistical Analysis
Paul Thompson; Kaydee Owen; Richard P. Hastings – International Journal of Research & Method in Education, 2024
Traditionally, cluster randomized controlled trials are analyzed with the average intervention effect of interest. However, in populations that contain higher degrees of heterogeneity or variation may differ across different values of a covariate, which may not be optimal. Within education and social science contexts, exploring the variation in…
Descriptors: Randomized Controlled Trials, Intervention, Mathematics Education, Mathematics Skills
Li, Wei; Konstantopoulos, Spyros – Educational and Psychological Measurement, 2023
Cluster randomized control trials often incorporate a longitudinal component where, for example, students are followed over time and student outcomes are measured repeatedly. Besides examining how intervention effects induce changes in outcomes, researchers are sometimes also interested in exploring whether intervention effects on outcomes are…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Longitudinal Studies, Hierarchical Linear Modeling
Li, Wei; Dong, Nianbo; Maynarad, Rebecca; Spybrook, Jessaca; Kelcey, Ben – Journal of Research on Educational Effectiveness, 2023
Cluster randomized trials (CRTs) are commonly used to evaluate educational interventions, particularly their effectiveness. Recently there has been greater emphasis on using these trials to explore cost-effectiveness. However, methods for establishing the power of cluster randomized cost-effectiveness trials (CRCETs) are limited. This study…
Descriptors: Research Design, Statistical Analysis, Randomized Controlled Trials, Cost Effectiveness
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
Moeyaert, Mariola; Yang, Panpan; Xu, Xinyun – Grantee Submission, 2021
This study investigated the power of two-level hierarchical linear modeling (HLM) to explain variability in intervention effectiveness between participants in context of single-case experimental design (SCED) research. HLM is a flexible technique that allows the inclusion of participant characteristics (e.g., age, gender, and disability types) as…
Descriptors: Hierarchical Linear Modeling, Intervention, Research Design, Participant Characteristics
Shen, Zuchao; Kelcey, Benjamin – Journal of Educational and Behavioral Statistics, 2020
Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework…
Descriptors: Sampling, Research Design, Randomized Controlled Trials, Statistical Analysis
Moeyaert, Mariola; Yang, Panpan – Grantee Submission, 2021
This study introduces an innovative meta-analytic approach, two-stage multilevel meta-analysis that considers the hierarchical structure of single-case experimental design (SCED) data. This approach is unique as it is suitable to include moderators at the intervention level, participant level, and study level, and is therefore especially…
Descriptors: Hierarchical Linear Modeling, Meta Analysis, Research Design, Case Studies