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Steele, Joel S.; Ferrer, Emilio – Multivariate Behavioral Research, 2011
This article presents our response to Oud and Folmer's "Modeling Oscillation, Approximately or Exactly?" (2011), which criticizes aspects of our article, "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011). In this response, we present a conceptual explanation of the derivative-based estimation…
Descriptors: Calculus, Responses, Simulation, Models
Rozeboom, William W. – Multivariate Behavioral Research, 2009
The topic of this article is the interpretation of structural equation modeling (SEM) solutions. Its purpose is to augment structural modeling's metatheoretic resources while enhancing awareness of how problematic is the causal significance of SEM-parameter solutions. Part I focuses on the nonuniqueness and consequent dubious interpretability of…
Descriptors: Structural Equation Models, Equations (Mathematics), Matrices, Probability

Rovine, Michael J.; Molenaar, Peter C. M. – Multivariate Behavioral Research, 2000
Presents a method for estimating the random coefficients model using covariance structure modeling and allowing one to estimate both fixed and random effects. The method is applied to real and simulated data, including marriage data from J. Belsky and M. Rovine (1990). (SLD)
Descriptors: Estimation (Mathematics), Mathematical Models

Hoijtink, Herbert – Multivariate Behavioral Research, 2001
Discusses , in the context of confirmatory latent class analysis, model selection using Bayes factors and (pseudo) likelihood ratio statistics. Uses a small simulation study to show that in this context, Bayes factors and the pseudo likelihood ratio statistics have the best properties. (SLD)
Descriptors: Bayesian Statistics, Mathematical Models

Hernandez, Ana; Gonzalez-Roma, Vicente – Multivariate Behavioral Research, 2002
Studied whether empirical multitrait multioccasion (MTMO) data conform more closely to multiplicative models than to additive models, using four additive models and two versions of the multiplicative Direct Product model. Results based on matrices from previous studies show that both additive and multiplicative models usually fit the same MTMO…
Descriptors: Goodness of Fit, Mathematical Models

Raykov, Tenko; Penev, Spiridon – Multivariate Behavioral Research, 1999
Presents a necessary and sufficient condition for the equivalence of structural-equation models that is applicable to models with parameter restrictions and models that may or may not fulfill assumptions of the rules. Illustrates the application of the approach for studying model equivalence. (SLD)
Descriptors: Mathematical Models, Structural Equation Models

Curry, David J. – Multivariate Behavioral Research, 1976
The purpose of this study is to develop statistical tests for within cluster homogeneity when objects are scored on binary variables. (DEP)
Descriptors: Cluster Grouping, Mathematical Models, Statistical Analysis

Gold, E. Mark; Hoffman, Paul J. – Multivariate Behavioral Research, 1976
A clustering technique is described, the objective of which is to detect deviant subpopulations which deviate from a primary subpopulation in a well defined direction. (Author/DEP)
Descriptors: Algorithms, Cluster Analysis, Cluster Grouping, Mathematical Models

Mulaik, Stanley A. – Multivariate Behavioral Research, 1987
Exploratory factor analysis derives its key ideas from many sources, including Aristotle, Francis Bacon, Descartes, Pearson and Yule, and Kant. The conclusions of exploratory factor analysis are never complete without subsequent confirmatory factor analysis. (Author/GDC)
Descriptors: Factor Analysis, History, Induction, Mathematical Models

MacCallum, Robert C.; Hong, Sehee – Multivariate Behavioral Research, 1997
Procedures are presented for conducting power analyses of tests of overall fit of covariance structure models when null and alternative levels of model fit are specified in terms of values of the GFI or AGFI fit indexes. Reasons the root mean square error of approximation fit index may be preferable are discussed. (SLD)
Descriptors: Goodness of Fit, Mathematical Models, Power (Statistics)

Kaplan, David – Multivariate Behavioral Research, 1989
Model modification problems in covariance structure analysis are examined. The Modification Index, suggesting modifications based on a test statistic's largest drop in overall value, and the Expected Parameter Change, suggesting modifications based on the removal of large specification errors, are applied to two specifications of the Wisconsin…
Descriptors: Mathematical Models, Statistical Analysis, Theory Practice Relationship

Jaccard, James; And Others – Multivariate Behavioral Research, 1990
Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)
Descriptors: Equations (Mathematics), Mathematical Models, Multiple Regression Analysis

Guadagnoli, Edward; Velicer, Wayne – Multivariate Behavioral Research, 1991
In matrix comparison, the performance of four vector matching indices (the coefficient of congruence, the Pearson product moment correlation, the "s"-statistic, and kappa) was evaluated. Advantages and disadvantages of each index are discussed, and the performance of each was assessed within the framework of principal components…
Descriptors: Comparative Analysis, Factor Analysis, Mathematical Models, Matrices

van Buuren, Stef; de Leeuw, Jan – Multivariate Behavioral Research, 1992
Application of equality constraints on the categories of a variable is a simple and useful extension of multiple correspondence analysis. Equality is an easy way to incorporate prior knowledge. A procedure to deal with unequal category numbers and with subsets of variables is outlined and illustrated. (SLD)
Descriptors: Classification, Knowledge Level, Mathematical Models, Multivariate Analysis

Tate, Richard L.; Bryant, John L. – Multivariate Behavioral Research, 1986
The shape of the response surface associated with a discriminant analysis provides insight into the value of the derived optimal discriminant variates. A procedure for the determination of "indifference regions," presented in this article, allows the assessment of the degree of flatness of the response surface for any analysis.…
Descriptors: Discriminant Analysis, Mathematical Models, Multivariate Analysis, Statistical Studies