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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
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Lee, Sik-Yum; Lu, Bin – Multivariate Behavioral Research, 2003
In this article, a case-deletion procedure is proposed to detect influential observations in a nonlinear structural equation model. The key idea is to develop the diagnostic measures based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. An one-step pseudo approximation is proposed to reduce the…
Descriptors: Structural Equation Models, Computation, Mathematics, Simulation
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Ford, David L.; And Others – Multivariate Behavioral Research, 1978
Econometric techniques for estimating the parameters of individual and group multi-attribute utility models are discussed. These techniques permit measurement of intra-and inter-individual heterogeneity with regard to the importance ascribed to the model attributes. (Author/JKS)
Descriptors: Economic Research, Higher Education, Individual Characteristics, Mathematical Models
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Pavur, Robert; Nath, Ravinder – Multivariate Behavioral Research, 1989
A Monte Carlo simulation study compared the power and Type I errors of the Wilks lambda statistic and the statistic of M. L. Puri and P. K. Sen (1971) on transformed data in a one-way multivariate analysis of variance. Preferred test procedures, based on robustness and power, are discussed. (SLD)
Descriptors: Comparative Analysis, Mathematical Models, Monte Carlo Methods, Multivariate Analysis
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Collins, Linda M.; And Others – Multivariate Behavioral Research, 1986
The present study compares the performance of phi coefficients and tetrachorics along two dimensions of factor recovery in binary data. These dimensions are (1) accuracy of nontrivial factor identifications; and (2) factor structure recovery given a priori knowledge of the correct number of factors to rotate. (Author/LMO)
Descriptors: Computer Software, Factor Analysis, Factor Structure, Item Analysis