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Vermunt, Jeroen K. – Psychological Methods, 2011
Steinley and Brusco (2011) presented the results of a huge simulation study aimed at evaluating cluster recovery of mixture model clustering (MMC) both for the situation where the number of clusters is known and is unknown. They derived rather strong conclusions on the basis of this study, especially with regard to the good performance of…
Descriptors: Multivariate Analysis, Simulation, Research, Mathematics
Trafimow, David; MacDonald, Justin A.; Rice, Stephen; Clason, Dennis L. – Psychological Methods, 2010
Largely due to dissatisfaction with the standard null hypothesis significance testing procedure, researchers have begun to consider alternatives. For example, Killeen (2005a) has argued that researchers should calculate p[subscript rep] that is purported to indicate the probability that, if the experiment in question were replicated, the obtained…
Descriptors: Probability, Replication (Evaluation), Statistics, Comparative Analysis
Keselman, H. J.; Miller, Charles W.; Holland, Burt – Psychological Methods, 2011
There have been many discussions of how Type I errors should be controlled when many hypotheses are tested (e.g., all possible comparisons of means, correlations, proportions, the coefficients in hierarchical models, etc.). By and large, researchers have adopted familywise (FWER) control, though this practice certainly is not universal. Familywise…
Descriptors: Validity, Statistical Significance, Probability, Computation
Steiner, Peter M.; Cook, Thomas D.; Shadish, William R.; Clark, M. H. – Psychological Methods, 2010
The assumption of strongly ignorable treatment assignment is required for eliminating selection bias in observational studies. To meet this assumption, researchers often rely on a strategy of selecting covariates that they think will control for selection bias. Theory indicates that the most important covariates are those highly correlated with…
Descriptors: Selection, Bias, Observation, Comparative Analysis
Stern, Hal S. – Psychological Methods, 2005
I. Klugkist, O. Laudy, and H. Hoijtink (2005) presented a Bayesian approach to analysis of variance models with inequality constraints. Constraints may play 2 distinct roles in data analysis. They may represent prior information that allows more precise inferences regarding parameter values, or they may describe a theory to be judged against the…
Descriptors: Probability, Inferences, Bayesian Statistics, Data Analysis