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Aidoo, Eric Nimako; Appiah, Simon K.; Boateng, Alexander – Journal of Experimental Education, 2021
This study investigated the small sample biasness of the ordered logit model parameters under multicollinearity using Monte Carlo simulation. The results showed that the level of biasness associated with the ordered logit model parameters consistently decreases for an increasing sample size while the distribution of the parameters becomes less…
Descriptors: Statistical Bias, Monte Carlo Methods, Simulation, Sample Size
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
Chan, Wendy – Journal of Experimental Education, 2021
Statisticians have developed propensity score methods to improve generalizations from studies that do not employ random sampling. However, these methods rely on assumptions whose plausibility may be questionable. We introduce and discuss bounding, an approach that is based on alternative assumptions that may be more plausible. The bounding…
Descriptors: Generalization, Statistics Education, Guidelines, Simulation
Leite, Walter L.; Stapleton, Laura M. – Journal of Experimental Education, 2011
In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…
Descriptors: Structural Equation Models, Simulation, Geometric Concepts, Sample Size
Finch, Holmes – Journal of Experimental Education, 2010
Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…
Descriptors: Simulation, Predictor Variables, Discriminant Analysis, Comparative Analysis
Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S. – Journal of Experimental Education, 2009
Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…
Descriptors: Longitudinal Studies, Models, Measurement, Multivariate Analysis
Peer reviewedHutchinson, Susan R. – Journal of Experimental Education, 1993
Simulated population data were used to compare relative performances of the modification index and C. Chou and P. M. Bentler's Lagrange multiplier test (a multivariate generalization of a modification index) for four levels of model misspecification. Both indices failed to recover the true model except at the lowest level of misspecification. (SLD)
Descriptors: Comparative Analysis, Computer Simulation, Multivariate Analysis, Population Distribution
Peer reviewedGibbons, Jean D.; Chakraborti, S. – Journal of Experimental Education, 1991
A simulation study compared the performance of the Mann-Whitney "U" test, Student's "t" test, and the alternate (separate variance) "t" test for two mutually independent random samples from normal distributions, with both one-tailed and two-tailed alternatives. Type I error probabilities were computed with equal and…
Descriptors: Comparative Analysis, Computer Simulation, Equations (Mathematics), Mathematical Models
Peer reviewedOlejnik, Stephen – Journal of Experimental Education, 1987
This study examined the sampling distribution of the analysis of variance F ratio in the two sample cases when it followed a preliminary test for variance equality. When the population variances were equal, the sampling distribution approximated the theoretical F distribution quite well, but not when population variances differed. (JAZ)
Descriptors: Analysis of Variance, Comparative Analysis, Computer Simulation, Sample Size
Peer reviewedZimmerman, Donald W.; Zumbo, Bruno D. – Journal of Experimental Education, 1993
Comparisons of the Wilcoxon test, Friedman test, and repeated-measures analysis of variance (ANOVA) on ranks in a computer simulation show that the Friedman test performs like the sign test whereas the ANOVA performs like the Wilcoxon test. Classification of these tests in introductory statistics textbooks should be revised. (SLD)
Descriptors: Analysis of Variance, Classification, Comparative Analysis, Computer Simulation
Peer reviewedMarascuilo, Leonard A. – Journal of Experimental Education, 1979
The utility of the biomedical model of adjusted statistics is demonstrated. The model is recommended for use by educational researchers to randomize subjects for a more accurate estimate of school programs' success or failure when compared across classrooms or other units. (Author/MH)
Descriptors: Academic Achievement, Analysis of Variance, Comparative Analysis, Criterion Referenced Tests

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