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Oliver Lüdtke; Alexander Robitzsch – Journal of Experimental Education, 2025
There is a longstanding debate on whether the analysis of covariance (ANCOVA) or the change score approach is more appropriate when analyzing non-experimental longitudinal data. In this article, we use a structural modeling perspective to clarify that the ANCOVA approach is based on the assumption that all relevant covariates are measured (i.e.,…
Descriptors: Statistical Analysis, Longitudinal Studies, Error of Measurement, Hierarchical Linear Modeling
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
Miratrix, Luke W.; Weiss, Michael J.; Henderson, Brit – Journal of Research on Educational Effectiveness, 2021
Researchers face many choices when conducting large-scale multisite individually randomized control trials. One of the most common quantities of interest in multisite RCTs is the overall average effect. Even this quantity is non-trivial to define and estimate. The researcher can target the average effect across individuals or sites. Furthermore,…
Descriptors: Computation, Randomized Controlled Trials, Error of Measurement, Regression (Statistics)
Bolin, Jocelyn H.; Finch, W. Holmes; Stenger, Rachel – Educational and Psychological Measurement, 2019
Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the…
Descriptors: Hierarchical Linear Modeling, Computation, Statistical Analysis, Maximum Likelihood Statistics
Cao, Chunhua; Kim, Eun Sook; Chen, Yi-Hsin; Ferron, John; Stark, Stephen – Educational and Psychological Measurement, 2019
In multilevel multiple-indicator multiple-cause (MIMIC) models, covariates can interact at the within level, at the between level, or across levels. This study examines the performance of multilevel MIMIC models in estimating and detecting the interaction effect of two covariates through a simulation and provides an empirical demonstration of…
Descriptors: Hierarchical Linear Modeling, Structural Equation Models, Computation, Identification
Enders, Craig K.; Keller, Brian T.; Levy, Roy – Grantee Submission, 2018
Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables,…
Descriptors: Hierarchical Linear Modeling, Behavioral Science Research, Computer Software, Bayesian Statistics
Finch, Holmes – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
Multilevel models (MLMs) have proven themselves to be very useful in social science research, as data from a variety of sources is sampled such that individuals at level-1 are nested within clusters such as schools, hospitals, counseling centers, and business entities at level-2. MLMs using restricted maximum likelihood estimation (REML) provide…
Descriptors: Hierarchical Linear Modeling, Comparative Analysis, Computation, Robustness (Statistics)
Leckie, George – Journal of Educational and Behavioral Statistics, 2018
The traditional approach to estimating the consistency of school effects across subject areas and the stability of school effects across time is to fit separate value-added multilevel models to each subject or cohort and to correlate the resulting empirical Bayes predictions. We show that this gives biased correlations and these biases cannot be…
Descriptors: Value Added Models, Reliability, Statistical Bias, Computation
Zigler, Christina K.; Ye, Feifei – AERA Online Paper Repository, 2016
Mediation in multi-level data can be examined using conflated multilevel modeling (CMM), unconflated multilevel modeling (UMM), or multilevel structural equation modeling (MSEM). A Monte Carlo study was performed to compare the three methods on bias, type I error, and power in a 1-1-1 model with random slopes. The three methods showed no…
Descriptors: Hierarchical Linear Modeling, Structural Equation Models, Monte Carlo Methods, Statistical Bias
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
Ren, Chunfeng; Shin, Yongyun – Grantee Submission, 2016
In this paper, we analyze a two-level latent variable model for longitudinal data from the National Growth of Health Study where surrogate outcomes or biomarkers and covariates are subject to missingness at any of the levels. A conventional method for efficient handling of missing data is to reexpress the desired model as a joint distribution of…
Descriptors: Longitudinal Studies, Statistical Analysis, Data, Maximum Likelihood Statistics
Drechsler, Jörg – Journal of Educational and Behavioral Statistics, 2015
Multiple imputation is widely accepted as the method of choice to address item-nonresponse in surveys. However, research on imputation strategies for the hierarchical structures that are typically found in the data in educational contexts is still limited. While a multilevel imputation model should be preferred from a theoretical point of view if…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Educational Research, Statistical Bias
Austin, Bruce; French, Brian; Adesope, Olusola; Gotch, Chad – Journal of Experimental Education, 2017
Measures of variability are successfully used in predictive modeling in research areas outside of education. This study examined how standard deviations can be used to address research questions not easily addressed using traditional measures such as group means based on index variables. Student survey data were obtained from the Organisation for…
Descriptors: Predictor Variables, Models, Predictive Measurement, Statistical Analysis
McNeish, Daniel M. – Journal of Educational and Behavioral Statistics, 2016
Mixed-effects models (MEMs) and latent growth models (LGMs) are often considered interchangeable save the discipline-specific nomenclature. Software implementations of these models, however, are not interchangeable, particularly with small sample sizes. Restricted maximum likelihood estimation that mitigates small sample bias in MEMs has not been…
Descriptors: Models, Statistical Analysis, Hierarchical Linear Modeling, Sample Size
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2014
One approach for combining single-case data involves use of multilevel modeling. In this article, the authors use a Monte Carlo simulation study to inform applied researchers under which realistic conditions the three-level model is appropriate. The authors vary the value of the immediate treatment effect and the treatment's effect on the time…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Case Studies, Research Design
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