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Ludtke, Oliver; Robitzsch, Alexander; Kenny, David A.; Trautwein, Ulrich – Psychological Methods, 2013
The social relations model (SRM) is a conceptual, methodological, and analytical approach that is widely used to examine dyadic behaviors and interpersonal perception within groups. This article introduces a general and flexible approach to estimating the parameters of the SRM that is based on Bayesian methods using Markov chain Monte Carlo…
Descriptors: Statistical Analysis, Computation, Interpersonal Relationship, Models
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Muthen, Bengt; Asparouhov, Tihomir – Psychological Methods, 2012
This rejoinder discusses the general comments on how to use Bayesian structural equation modeling (BSEM) wisely and how to get more people better trained in using Bayesian methods. Responses to specific comments cover how to handle sign switching, nonconvergence and nonidentification, and prior choices in latent variable models. Two new…
Descriptors: Structural Equation Models, Bayesian Statistics, Factor Analysis, Statistical Analysis
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Culpepper, Steven Andrew; Aguinis, Herman – Psychological Methods, 2011
Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but…
Descriptors: Statistical Analysis, Error of Measurement, Monte Carlo Methods, Structural Equation Models
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Lai, Keke; Kelley, Ken – Psychological Methods, 2011
In addition to evaluating a structural equation model (SEM) as a whole, often the model parameters are of interest and confidence intervals for those parameters are formed. Given a model with a good overall fit, it is entirely possible for the targeted effects of interest to have very wide confidence intervals, thus giving little information about…
Descriptors: Accuracy, Structural Equation Models, Computation, Sample Size
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McGrath, Robert E.; Walters, Glenn D. – Psychological Methods, 2012
Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Computation
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Harring, Jeffrey R.; Weiss, Brandi A.; Hsu, Jui-Chen – Psychological Methods, 2012
Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent…
Descriptors: Structural Equation Models, Geometric Concepts, Computation, Comparative Analysis
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Lix, Lisa M.; Sajobi, Tolulope – Psychological Methods, 2010
This study investigates procedures for controlling the familywise error rate (FWR) when testing hypotheses about multiple, correlated outcome variables in repeated measures (RM) designs. A content analysis of RM research articles published in 4 psychology journals revealed that 3 quarters of studies tested hypotheses about 2 or more outcome…
Descriptors: Hypothesis Testing, Correlation, Statistical Analysis, Research Design
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DeCoster, Jamie; Iselin, Anne-Marie R.; Gallucci, Marcello – Psychological Methods, 2009
Despite many articles reporting the problems of dichotomizing continuous measures, researchers still commonly use this practice. The authors' purpose in this article was to understand the reasons that people still dichotomize and to determine whether any of these reasons are valid. They contacted 66 researchers who had published articles using…
Descriptors: Statistical Analysis, Classification, Monte Carlo Methods, Predictor Variables
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Hafdahl, Adam R.; Williams, Michelle A. – Psychological Methods, 2009
In 2 Monte Carlo studies of fixed- and random-effects meta-analysis for correlations, A. P. Field (2001) ostensibly evaluated Hedges-Olkin-Vevea Fisher-[zeta] and Schmidt-Hunter Pearson-r estimators and tests in 120 conditions. Some authors have cited those results as evidence not to meta-analyze Fisher-[zeta] correlations, especially with…
Descriptors: Monte Carlo Methods, Computer Software, Statistical Analysis, Correlation
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Bonnett, Douglas G. – Psychological Methods, 2008
Most psychology journals now require authors to report a sample value of effect size along with hypothesis testing results. The sample effect size value can be misleading because it contains sampling error. Authors often incorrectly interpret the sample effect size as if it were the population effect size. A simple solution to this problem is to…
Descriptors: Intervals, Hypothesis Testing, Effect Size, Sampling
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Serlin, Ronald C.; Harwell, Michael R. – Psychological Methods, 2004
It is well-known that for normally distributed errors parametric tests are optimal statistically, but perhaps less well-known is that when normality does not hold, nonparametric tests frequently possess greater statistical power than parametric tests, while controlling Type I error rate. However, the use of nonparametric procedures has been…
Descriptors: Multiple Regression Analysis, Monte Carlo Methods, Nonparametric Statistics, Error Patterns