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Showing 1 to 15 of 20 results Save | Export
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Jingru Zhang; James E. Pustejovsky – Society for Research on Educational Effectiveness, 2024
Background/Context: In meta-analysis examining educational intervention, characterizing heterogeneity and exploring the sources of variation in synthesized effects have become increasingly prominent areas of interest. When combining results from a collection of studies, statistical dependency among their effects size estimates will arise when a…
Descriptors: Meta Analysis, Investigations, Effect Size, Computation
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Julia-Kim Walther; Martin Hecht; Steffen Zitzmann – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized…
Descriptors: Sample Size, Hierarchical Linear Modeling, Structural Equation Models, Matrices
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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
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Man, Kaiwen; Schumacker, Randall; Morell, Monica; Wang, Yurou – Educational and Psychological Measurement, 2022
While hierarchical linear modeling is often used in social science research, the assumption of normally distributed residuals at the individual and cluster levels can be violated in empirical data. Previous studies have focused on the effects of nonnormality at either lower or higher level(s) separately. However, the violation of the normality…
Descriptors: Hierarchical Linear Modeling, Statistical Distributions, Statistical Bias, Computation
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Zsuzsa Bakk; Roberto Di Mari; Jennifer Oser; Jouni Kuha – Structural Equation Modeling: A Multidisciplinary Journal, 2022
In this article, we present a two-stage estimation approach applied to multilevel latent class analysis (LCA) with covariates. We separate the estimation of the measurement and structural model. This makes the extension of the structural model computationally efficient. We investigate the robustness against misspecifications of the proposed…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Computation, Measurement
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Sarah E. Robertson; Jon A. Steingrimsson; Issa J. Dahabreh – Evaluation Review, 2024
When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…
Descriptors: Randomized Controlled Trials, Generalization, Inferences, Hierarchical Linear Modeling
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Sijia Huang; Li Cai – Journal of Educational and Behavioral Statistics, 2024
The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called…
Descriptors: Classification, Data Collection, Data Analysis, Item Response Theory
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Rüttenauer, Tobias; Ludwig, Volker – Sociological Methods & Research, 2023
Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Statistical Bias, Computation
Strauss, Christian L. L. – ProQuest LLC, 2022
In many psychological and educational applications, it is imperative to obtain valid and reliable score estimates of multilevel processes. For example, in order to assess the quality and characteristics of high impact learning processes, one must compute accurate scores representative of student- and classroom-level constructs. Currently, there…
Descriptors: Scores, Factor Analysis, Models, True Scores
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Han Du; Brian Keller; Egamaria Alacam; Craig Enders – Grantee Submission, 2023
In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). A multilevel mediation model is used as an illustrative example to compare different types of DIC and WAIC. More specifically, the study compares the…
Descriptors: Bayesian Statistics, Models, Comparative Analysis, Probability
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Tomek, Sara; Robinson, Cecil – Measurement: Interdisciplinary Research and Perspectives, 2021
Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant's age, or conversely, participants may experience an intervention that causes trajectories to…
Descriptors: Growth Models, Statistical Analysis, Hierarchical Linear Modeling, Computation
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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
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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
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Rights, Jason D.; Sterba, Sonya K. – New Directions for Child and Adolescent Development, 2021
Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds ("R[superscript 2]") that…
Descriptors: Effect Size, Longitudinal Studies, Hierarchical Linear Modeling, Computation
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Dongho Shin – Grantee Submission, 2024
We consider Bayesian estimation of a hierarchical linear model (HLM) from small sample sizes. The continuous response Y and covariates C are partially observed and assumed missing at random. With C having linear effects, the HLM may be efficiently estimated by available methods. When C includes cluster-level covariates having interactive or other…
Descriptors: Bayesian Statistics, Computation, Hierarchical Linear Modeling, Data Analysis
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