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Wall, Melanie M.; Guo, Jia; Amemiya, Yasuo – Multivariate Behavioral Research, 2012
Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus…
Descriptors: Sample Size, Simulation, Form Classes (Languages), Diseases
Austin, Peter C. – Multivariate Behavioral Research, 2012
Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one…
Descriptors: Computation, Regression (Statistics), Statistical Bias, Error of Measurement
Lottridge, Susan M.; Nicewander, W. Alan; Mitzel, Howard C. – Multivariate Behavioral Research, 2011
This inquiry had 2 components: (1) the first was substantive and focused on the comparability of paper-based and computer-based test forms and (2) the second was a within-study comparison wherein a quasi-experimental method, propensity score matching, was compared with a credible benchmark method, a within-subjects design. The tests used in the…
Descriptors: Comparative Analysis, Probability, Scores, Statistical Analysis
Cafri, Guy; Kromrey, Jeffrey D.; Brannick, Michael T. – Multivariate Behavioral Research, 2010
This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…
Descriptors: Periodicals, Effect Size, Sampling, Psychology
Austin, Peter C. – Multivariate Behavioral Research, 2011
Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being…
Descriptors: Smoking, Hospitals, Program Effectiveness, Probability
Peer reviewedPrice, Lydia J. – Multivariate Behavioral Research, 1993
The ability of the NORMIX algorithm to recover overlapping population structures was compared to the OVERCLUS procedure and another clustering procedure in a Monte Carlo study. NORMIX is found to be more accurate than other procedures in recovering overlapping population structure when appropriate implementation options are specified. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Comparative Analysis

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