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Wall, Melanie M.; Guo, Jia; Amemiya, Yasuo – Multivariate Behavioral Research, 2012
Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus…
Descriptors: Sample Size, Simulation, Form Classes (Languages), Diseases
Schluchter, Mark D. – Multivariate Behavioral Research, 2008
In behavioral research, interest is often in examining the degree to which the effect of an independent variable X on an outcome Y is mediated by an intermediary or mediator variable M. This article illustrates how generalized estimating equations (GEE) modeling can be used to estimate the indirect or mediated effect, defined as the amount by…
Descriptors: Intervals, Predictor Variables, Equations (Mathematics), Computation
Hwang, Heungsun; Takane, Yoshio; DeSarbo, Wayne S. – Multivariate Behavioral Research, 2007
The growth curve model has been a useful tool for the analysis of repeated measures data. However, it is designed for an aggregate-sample analysis based on the assumption that the entire sample of respondents are from a single homogenous population. Thus, this method may not be suitable when heterogeneous subgroups exist in the population with…
Descriptors: Equations (Mathematics), Antisocial Behavior, Computation, Child Behavior

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
A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression.

Johnson, Jeff W. – Multivariate Behavioral Research, 2000
Proposes a heuristic method for estimating the relative weight of predictor variables in multiple regression that is computationally efficient with any number of predictors and that can be shown to produce results similar to those produced by more complex methods. (SLD)
Descriptors: Estimation (Mathematics), Heuristics, Predictor Variables, Regression (Statistics)

Algina, James; Olejnik, Stephen – Multivariate Behavioral Research, 2000
Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)
Descriptors: Correlation, Estimation (Mathematics), Predictor Variables, Regression (Statistics)

Raykov, Tenko – Multivariate Behavioral Research, 2002
Proposes an analytic approach to standard error and confidence interval estimation of scale reliability with fixed congeneric measures. The method is based on a generally applicable estimator stability evaluation procedure, the delta method. The approach, which combines wide-spread point estimation of composite reliability in behavioral scale…
Descriptors: Error of Measurement, Estimation (Mathematics), Rating Scales, Reliability
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