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Shaw, Mairead; Flake, Jessica K. – Educational Measurement: Issues and Practice, 2023
Clustered data structures are common in many areas of educational and psychological research (e.g., students clustered in schools, patients clustered by clinician). In the course of conducting research, questions are often administered to obtain scores reflecting latent constructs. Multilevel measurement models (MLMMs) allow for modeling…
Descriptors: Hierarchical Linear Modeling, Research Methodology, Data Analysis, Structural Equation Models
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Aydin, Burak; Algina, James – Journal of Experimental Education, 2022
Decomposing variables into between and within components are often required in multilevel analysis. This method of decomposition should not ignore possible unreliability of an observed group mean (i.e., arithmetic mean) that is due to small cluster sizes and can lead to substantially biased estimates. Adjustment procedures that allow unbiased…
Descriptors: Hierarchical Linear Modeling, Prediction, Research Methodology, Educational Research
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Tara Slominski; Oluwatobi O. Odeleye; Jacob W. Wainman; Lisa L. Walsh; Karen Nylund-Gibson; Marsha Ing – CBE - Life Sciences Education, 2024
Mixture modeling is a latent variable (i.e., a variable that cannot be measured directly) approach to quantitatively represent unobserved subpopulations within an overall population. It includes a range of cross-sectional (such as latent class [LCA] or latent profile analysis) and longitudinal (such as latent transition analysis) analyses and is…
Descriptors: Educational Research, Multivariate Analysis, Research Methodology, Hierarchical Linear Modeling
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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
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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
Moeyaert, Mariola; Yang, Panpan; Xu, Xinyun; Kim, Esther – Grantee Submission, 2021
Hierarchical linear modeling (HLM) has been recommended as a meta-analytic technique for the quantitative synthesis of single-case experimental design (SCED) studies. The HLM approach is flexible and can model a variety of different SCED data complexities, such as intervention heterogeneity. A major advantage of using HLM is that participant…
Descriptors: Meta Analysis, Case Studies, Research Design, Hierarchical Linear Modeling
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Ben Van Dusen; Heidi Cian; Jayson Nissen; Lucy Arellano; Adrienne D. Woods – Sociology of Education, 2024
This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed-effects models when performing intersectional studies. The research questions are as follows: (1) What are typical strata representation rates and outcomes on physics research-based assessments? (2) To what…
Descriptors: Educational Research, Intersectionality, Critical Race Theory, STEM Education
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Wang, Yan; Kim, Eunsook; Joo, Seang-Hwane; Chun, Seokjoon; Alamri, Abeer; Lee, Philseok; Stark, Stephen – Journal of Experimental Education, 2022
Multilevel latent class analysis (MLCA) has been increasingly used to investigate unobserved population heterogeneity while taking into account data dependency. Nonparametric MLCA has gained much popularity due to the advantage of classifying both individuals and clusters into latent classes. This study demonstrated the need to relax the…
Descriptors: Nonparametric Statistics, Hierarchical Linear Modeling, Monte Carlo Methods, Simulation
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Shirilla, Paul; Solid, Craig; Graham, Suzanne E. – Journal of Experiential Education, 2022
Background: A common critique of adventure education research methodology is the overreliance on pre-/post-study designs to measure change. Purpose: This paper compares and contrasts two methods of data analysis on the same adventure education data set to show how these distinct approaches provide starkly different results and interpretation.…
Descriptors: Longitudinal Studies, Hierarchical Linear Modeling, Adventure Education, Educational Research
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Preacher, Kristopher J.; Sterba, Sonya K. – Exceptional Children, 2019
A common theme uniting articles in this special issue is a focus on aptitude-by-treatment interactions (ATIs). This timely and welcome focus allows the field to synthesize current substantive findings on ATIs in educational intervention research in both reading and math domains. In this methodological commentary, we begin by reviewing traditional…
Descriptors: Intervention, Interaction, Hierarchical Linear Modeling, Educational Research
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Xiao, ZhiMin; Higgins, Steve; Kasim, Adetayo – Journal of Experimental Education, 2019
Lord's Paradox occurs when a continuous covariate is statistically controlled for and the relationship between a continuous outcome and group status indicator changes in both magnitude and direction. This phenomenon poses a challenge to the notion of evidence-based policy, where data are supposed to be self-evident. We examined 50 effect size…
Descriptors: Statistical Analysis, Decision Making, Research Methodology, Scores
Moeyaert, Mariola; Akhmedjanova, Diana; Ferron, John; Beretvas, S. Natasha; Van den Noortgate, Wim – Grantee Submission, 2020
The methodology of single-case experimental designs (SCED) has been expanding its efforts toward rigorous design tactics to address a variety of research questions related to intervention effectiveness. Effect size indicators appropriate to quantify the magnitude and the direction of interventions have been recommended and intensively studied for…
Descriptors: Effect Size, Research Methodology, Research Design, Hierarchical Linear Modeling
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Keller, Lena; Lüdtke, Oliver; Preckel, Franzis; Brunner, Martin – Educational Psychology Review, 2023
Intersectional approaches have become increasingly important for explaining educational inequalities because they help to improve our understanding of how individual experiences are shaped by simultaneous membership in multiple social categories that are associated with interconnected systems of power, privilege, and oppression. For years, there…
Descriptors: Equal Education, Intersectionality, Hierarchical Linear Modeling, Educational Research
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Luo, Wen; Li, Haoran; Baek, Eunkyeng; Chen, Siqi; Lam, Kwok Hap; Semma, Brandie – Review of Educational Research, 2021
Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results.…
Descriptors: Hierarchical Linear Modeling, Multivariate Analysis, Prediction, Research Problems
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Moranski, Kara; Ziegler, Nicole – Language Learning, 2021
Multisite research (MSR) offers the key advantages of greater statistical power and external validity via larger and more diverse participant pools. In second language acquisition (SLA) research, recent developments in meta-analysis have created a robust foundation for MSR. Although logistical and financial obstacles can complicate expansion…
Descriptors: Validity, Language Research, Second Language Learning, Meta Analysis
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