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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
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
Savalei, Victoria; Kolenikov, Stanislav – Psychological Methods, 2008
Recently, R. D. Stoel, F. G. Garre, C. Dolan, and G. van den Wittenboer (2006) reviewed approaches for obtaining reference mixture distributions for difference tests when a parameter is on the boundary. The authors of the present study argue that this methodology is incomplete without a discussion of when the mixtures are needed and show that they…
Descriptors: Structural Equation Models, Goodness of Fit, Evaluation Methods, Statistical Analysis
Jo, Booil – Psychological Methods, 2008
This article links the structural equation modeling (SEM) approach with the principal stratification (PS) approach, both of which have been widely used to study the role of intermediate posttreatment outcomes in randomized experiments. Despite the potential benefit of such integration, the 2 approaches have been developed in parallel with little…
Descriptors: Structural Equation Models, Monte Carlo Methods, Inferences, Outcomes of Treatment
Gonzalez, Jorge; De Boeck, Paul; Tuerlinckx, Francis – Psychological Methods, 2008
Structural equation models are commonly used to analyze 2-mode data sets, in which a set of objects is measured on a set of variables. The underlying structure within the object mode is evaluated using latent variables, which are measured by indicators coming from the variable mode. Additionally, when the objects are measured under different…
Descriptors: Structural Equation Models, Data Analysis, Evaluation Methods, Models
Stoel, Reinoud D.; Garre, Francisca Galindo; Dolan, Conor; van den Wittenboer, Godfried – Psychological Methods, 2006
The authors show how the use of inequality constraints on parameters in structural equation models may affect the distribution of the likelihood ratio test. Inequality constraints are implicitly used in the testing of commonly applied structural equation models, such as the common factor model, the autoregressive model, and the latent growth…
Descriptors: Testing, Structural Equation Models, Evaluation Methods
Maydeu-Olivares, Albert; Bockenholt, Ulf – Psychological Methods, 2005
L. L. Thurstone's (1927) model provides a powerful framework for modeling individual differences in choice behavior. An overview of Thurstonian models for comparative data is provided, including the classical Case V and Case III models as well as more general choice models with unrestricted and factor-analytic covariance structures. A flow chart…
Descriptors: Flow Charts, Factor Analysis, Structural Equation Models, Decision Making