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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
Nazari, Sanaz; Leite, Walter L.; Huggins-Manley, A. Corinne – Journal of Experimental Education, 2023
The piecewise latent growth models (PWLGMs) can be used to study changes in the growth trajectory of an outcome due to an event or condition, such as exposure to an intervention. When there are multiple outcomes of interest, a researcher may choose to fit a series of PWLGMs or a single parallel-process PWLGM. A comparison of these models is…
Descriptors: Growth Models, Statistical Analysis, Intervention, Comparative Analysis
Leite, Walter L.; Aydin, Burak; Gurel, Sungur – Journal of Experimental Education, 2019
This Monte Carlo simulation study compares methods to estimate the effects of programs with multiple versions when assignment of individuals to program version is not random. These methods use generalized propensity scores, which are predicted probabilities of receiving a particular level of the treatment conditional on covariates, to remove…
Descriptors: Probability, Weighted Scores, 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
Kelly, Sean; Ye, Feifei – Journal of Experimental Education, 2017
Educational analysts studying achievement and other educational outcomes frequently encounter an association between initial status and growth, which has important implications for the analysis of covariate effects, including group differences in growth. As explicated by Allison (1990), where only two time points of data are available, identifying…
Descriptors: Regression (Statistics), Models, Error of Measurement, Scores
Peng, Chao-Ying Joanne; Chen, Li-Ting – Journal of Experimental Education, 2014
Given the long history of discussion of issues surrounding statistical testing and effect size indices and various attempts by the American Psychological Association and by the American Educational Research Association to encourage the reporting of effect size, most journals in education and psychology have witnessed an increase in effect size…
Descriptors: Effect Size, Statistical Analysis, Computation, Classification
Beasley, T. Mark – Journal of Experimental Education, 2014
Increasing the correlation between the independent variable and the mediator ("a" coefficient) increases the effect size ("ab") for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation caused by…
Descriptors: Statistical Analysis, Effect Size, Nonparametric Statistics, Statistical Inference
Schoeneberger, Jason A. – Journal of Experimental Education, 2016
The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying…
Descriptors: Sample Size, Models, Computation, Predictor Variables
Henson, Robin K.; Natesan, Prathiba; Axelson, Erika D. – Journal of Experimental Education, 2014
The authors examined the distributional properties of 3 improvement-over-chance, I, effect sizes each derived from linear and quadratic predictive discriminant analysis and from logistic regression analysis for the 2-group univariate classification. These 3 classification methods (3 levels) were studied under varying levels of data conditions,…
Descriptors: Effect Size, Probability, Comparative Analysis, Classification
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
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
Onwuegbuzie, Anthony J.; Levin, Joel R.; Ferron, John M. – Journal of Experimental Education, 2011
Building on previous arguments for why educational researchers should not provide effect-size estimates in the face of statistically nonsignificant outcomes (Robinson & Levin, 1997), Onwuegbuzie and Levin (2005) proposed a 3-step statistical approach for assessing group differences when multiple outcome measures are individually analyzed…
Descriptors: Hypothesis Testing, Statistical Analysis, Effect Size, Probability
Fidalgo, Angel M.; Hashimoto, Kanako; Bartram, Dave; Muniz, Jose – Journal of Experimental Education, 2007
In this study, the authors assess several strategies created on the basis of the Mantel-Haenszel (MH) procedure for conducting differential item functioning (DIF) analysis with small samples. One of the analytical strategies is a loss function (LF) that uses empirical Bayes Mantel-Haenszel estimators, whereas the other strategies use the classical…
Descriptors: Bayesian Statistics, Test Bias, Statistical Analysis, Sample Size

Williams, Richard H. – Journal of Experimental Education, 1974
An equation comparable to Spearman's correction for attenuation, which does not depend upon the assumption that error scores are uncorrelated with true scores and with other sets of scores, is derived. (Editor)
Descriptors: Correlation, Error of Measurement, Statistical Analysis, True Scores

Riniolo, Todd C. – Journal of Experimental Education, 1999
Presents an alternative statistical test, BOOT(subscript)med for the two-group situation when a small experimental group is being compared with a large control group. BOOTmed is a between-groups median test derived through bootstrapping techniques. Empirical validation indicates that BOOTmed maintains relatively robust error rates under a variety…
Descriptors: Comparative Analysis, Control Groups, Error of Measurement, Statistical Analysis
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