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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
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
Fay, Derek M.; Levy, Roy; Schulte, Ann C. – Journal of Experimental Education, 2022
Longitudinal data structures are frequently encountered in a variety of disciplines in the social and behavioral sciences. Growth curve modeling offers a highly extensible framework that allows for the exploration of rich hypotheses. However, owing to the presence of interrelated sources of potential data-model misfit at multiple levels, the…
Descriptors: Measurement, Models, Bayesian Statistics, Hierarchical Linear Modeling
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
Baek, Eunkyeng; Beretvas, S. Natasha; Van den Noortgate, Wim; Ferron, John M. – Journal of Experimental Education, 2020
Recently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum…
Descriptors: Bayesian Statistics, Intervention, Case Studies, Monte Carlo Methods
Shen, Ting; Konstantopoulos, Spyros – Journal of Experimental Education, 2022
Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…
Descriptors: Data Collection, Educational Research, Hierarchical Linear Modeling, Bayesian Statistics
Park, Sunyoung; Natasha Beretvas, S. – Journal of Experimental Education, 2021
When selecting a multilevel model to fit to a dataset, it is important to choose both a model that best matches characteristics of the data's structure, but also to include the appropriate fixed and random effects parameters. For example, when researchers analyze clustered data (e.g., students nested within schools), the multilevel model can be…
Descriptors: Hierarchical Linear Modeling, Statistical Significance, Multivariate Analysis, Monte Carlo Methods
Chang, Wanchen; Pituch, Keenan A. – Journal of Experimental Education, 2019
When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design…
Descriptors: Hierarchical Linear Modeling, Multivariate Analysis, Research Problems, Error of Measurement
Huang, Francis L. – Journal of Experimental Education, 2018
Studies analyzing clustered data sets using both multilevel models (MLMs) and ordinary least squares (OLS) regression have generally concluded that resulting point estimates, but not the standard errors, are comparable with each other. However, the accuracy of the estimates of OLS models is important to consider, as several alternative techniques…
Descriptors: Hierarchical Linear Modeling, Least Squares Statistics, Regression (Statistics), Comparative Analysis
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2016
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Computation, Statistical Bias
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)
Hembry, Ian; Bunuan, Rommel; Beretvas, S. Natasha; Ferron, John M.; Van den Noortgate, Wim – Journal of Experimental Education, 2015
A multilevel logistic model for estimating a nonlinear trajectory in a multiple-baseline design is introduced. The model is applied to data from a real multiple-baseline design study to demonstrate interpretation of relevant parameters. A simple change-in-levels (?"Levels") model and a model involving a quadratic function…
Descriptors: Computation, Research Design, Data, Intervention
Smith, Lindsey J. Wolff; Beretvas, S. Natasha – Journal of Experimental Education, 2017
Conventional multilevel modeling works well with purely hierarchical data; however, pure hierarchies rarely exist in real datasets. Applied researchers employ ad hoc procedures to create purely hierarchical data. For example, applied educational researchers either delete mobile participants' data from the analysis or identify the student only with…
Descriptors: Student Mobility, Academic Achievement, Simulation, Influences
Huang, Francis L. – Journal of Experimental Education, 2016
Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Sample Size, Error of Measurement
Wu, Jiun-Yu; Kwok, Oi-Man; Willson, Victor L. – Journal of Experimental Education, 2014
The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the…
Descriptors: Longitudinal Studies, Hierarchical Linear Modeling, Prediction, Regression (Statistics)
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