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Yi, Zhiyao; Chen, Yi-Hsin; Yin, Yue; Cheng, Ke; Wang, Yan; Nguyen, Diep; Pham, Thanh; Kim, EunSook – Journal of Experimental Education, 2022
A simulation study was conducted to examine the efficacy of nine frequently-used HOV tests, including Levene's tests with squared residuals and with absolute residuals, Brown and Forsythe (BF) test, Bootstrap BF test, O'Brien test, Z-variance test, Box-ScheffĂ© (BS) test, Bartlett test, and Pseudo jackknife test under comprehensive simulation…
Descriptors: Statistical Analysis, Robustness (Statistics), Sampling, Statistical Inference
Shieh, Gwowen – Journal of Experimental Education, 2019
The analysis of covariance (ANCOVA) is a useful statistical procedure that incorporates covariate features into the adjustment of treatment effects. The consequences of omitted prognostic covariates on the statistical inferences of ANCOVA are well documented in the literature. However, the corresponding influence on sample-size calculations for…
Descriptors: Sample Size, Statistical Analysis, Computation, Accuracy
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
Baek, Eunkyeng; Luo, Wen; Henri, Maria – Journal of Experimental Education, 2022
It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…
Descriptors: Meta Analysis, Hierarchical Linear Modeling, Comparative Analysis, Statistical Inference
Kang, Yoonjeong; Harring, Jeffrey R.; Li, Ming – Journal of Experimental Education, 2015
The authors performed a Monte Carlo simulation to empirically investigate the robustness and power of 4 methods in testing mean differences for 2 independent groups under conditions in which 2 populations may not demonstrate the same pattern of nonnormality. The approaches considered were the t test, Wilcoxon rank-sum test, Welch-James test with…
Descriptors: Comparative Analysis, Monte Carlo Methods, Statistical Analysis, Robustness (Statistics)
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
Stapleton, Laura M.; Pituch, Keenan A.; Dion, Eric – Journal of Experimental Education, 2015
This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the…
Descriptors: Effect Size, Measurement Techniques, Statistical Analysis, Research Design
Ugille, Maaike; Moeyaert, Mariola; Beretvas, S. Natasha; Ferron, John M.; Van den Noortgate, Wim – Journal of Experimental Education, 2014
A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect…
Descriptors: Effect Size, Statistical Bias, Sample Size, Regression (Statistics)
Bai, Haiyan – Journal of Experimental Education, 2013
Propensity score estimation plays a fundamental role in propensity score matching for reducing group selection bias in observational data. To increase the accuracy of propensity score estimation, the author developed a bootstrap propensity score. The commonly used propensity score matching methods: nearest neighbor matching, caliper matching, and…
Descriptors: Statistical Inference, Sampling, Probability, Computation

Games, Paul A.; Hedges, Larry V. – Journal of Experimental Education, 1987
Variance stabilizing transformations yield statistics whose standard errors are only influenced by the number of observations on which they are based. A solution that can be extended to inference on all of these parameters is presented and illustrated via three examples. (TJH)
Descriptors: Correlation, Error of Measurement, Least Squares Statistics, Multivariate Analysis