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Rindskopf, David – Psychological Methods, 2012
Muthen and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By…
Descriptors: Structural Equation Models, Bayesian Statistics, Factor Analysis, Computation
Macho, Siegfried; Ledermann, Thomas – Psychological Methods, 2011
The phantom model approach for estimating, testing, and comparing specific effects within structural equation models (SEMs) is presented. The rationale underlying this novel method consists in representing the specific effect to be assessed as a total effect within a separate latent variable model, the phantom model that is added to the main…
Descriptors: Structural Equation Models, Computation, Comparative Analysis, Sampling
Doebler, Philipp; Holling, Heinz; Bohning, Dankmar – Psychological Methods, 2012
We propose 2 related models for the meta-analysis of diagnostic tests. Both models are based on the bivariate normal distribution for transformed sensitivities and false-positive rates. Instead of using the logit as a transformation for these proportions, we employ the "t"[subscript alpha] family of transformations that contains the log, logit,…
Descriptors: Models, Meta Analysis, Diagnostic Tests, Comparative Analysis
Bauer, Daniel J.; Sterba, Sonya K. – Psychological Methods, 2011
Previous research has compared methods of estimation for fitting multilevel models to binary data, but there are reasons to believe that the results will not always generalize to the ordinal case. This article thus evaluates (a) whether and when fitting multilevel linear models to ordinal outcome data is justified and (b) which estimator to employ…
Descriptors: Item Response Theory, Models, Computation, Research
Pan, Tianshu; Yin, Yue – Psychological Methods, 2012
In the discussion of mean square difference (MSD) and standard error of measurement (SEM), Barchard (2012) concluded that the MSD between 2 sets of test scores is greater than 2(SEM)[superscript 2] and SEM underestimates the score difference between 2 tests when the 2 tests are not parallel. This conclusion has limitations for 2 reasons. First,…
Descriptors: Error of Measurement, Geometric Concepts, Tests, Structural Equation Models
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
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
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
Shadish, William R. – Psychological Methods, 2010
This article compares Donald Campbell's and Donald Rubin's work on causal inference in field settings on issues of epistemology, theories of cause and effect, methodology, statistics, generalization, and terminology. The two approaches are quite different but compatible, differing mostly in matters of bandwidth versus fidelity. Campbell's work…
Descriptors: Inferences, Generalization, Epistemology, Causal Models
Kelley, Ken; Rausch, Joseph R. – Psychological Methods, 2011
Longitudinal studies are necessary to examine individual change over time, with group status often being an important variable in explaining some individual differences in change. Although sample size planning for longitudinal studies has focused on statistical power, recent calls for effect sizes and their corresponding confidence intervals…
Descriptors: Intervals, Sample Size, Effect Size, Longitudinal Studies
Evaluating Model Fit for Growth Curve Models: Integration of Fit Indices from SEM and MLM Frameworks
Wu, Wei; West, Stephen G.; Taylor, Aaron B. – Psychological Methods, 2009
Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation…
Descriptors: Structural Equation Models, Goodness of Fit, Longitudinal Studies, Comparative Analysis
Le, Huy; Schmidt, Frank L. – Psychological Methods, 2006
Using computer simulation, the authors assessed the accuracy of J. E. Hunter, F. L. Schmidt, and H. Le's (2006) procedure for correcting for indirect range restriction, the most common type of range restriction, in comparison with the conventional practice of applying the Thorndike Case II correction for direct range restriction. Hunter et…
Descriptors: Computer Simulation, Predictor Variables, Correlation, Computation
Marsh, Herbert W.; Wen, Zhonglin; Hau, Kit-Tai – Psychological Methods, 2004
Interactions between (multiple indicator) latent variables are rarely used because of implementation complexity and competing strategies. Based on 4 simulation studies, the traditional constrained approach performed more poorly than did 3 new approaches-unconstrained, generalized appended product indicator, and quasi-maximum-likelihood (QML). The…
Descriptors: Structural Equation Models, Item Analysis, Error Patterns, Computation
Kraemer, Helena Chmura – Psychological Methods, 2005
R. Rosenthal and D. B. Rubin (2003) proposed an effect size, r-sub(equivalent), to be used when (a) only sample size and p values are known for a study, (b) there are no generally accepted effect size indicators, or (c) sample sizes are so small or the data so non-normal that the directly computed effect sizes would be more misleading than the…
Descriptors: Effect Size, Sample Size, Reader Response, Criticism