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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
Lee, Chun-Ting; Zhang, Guangjian; Edwards, Michael C. – Multivariate Behavioral Research, 2012
Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…
Descriptors: Personality Traits, Intervals, Monte Carlo Methods, Factor Analysis
Schweizer, Karl – Multivariate Behavioral Research, 2011
The standardization of loadings gives a metric to the corresponding latent variable and thus scales the variance of this latent variable. By assigning an appropriately estimated weight to all the loadings on the same latent variable it can be achieved that the average squared loading is 1 as the result of standardization. As a consequence, there…
Descriptors: Structural Equation Models, Short Term Memory, Evaluation Methods, Comparative Analysis
Shiyko, Mariya P.; Ram, Nilam – Multivariate Behavioral Research, 2011
Researchers have been making use of ecological momentary assessment (EMA) and other study designs that sample feelings and behaviors in real time and in naturalistic settings to study temporal dynamics and contextual factors of a wide variety of psychological, physiological, and behavioral processes. As EMA designs become more widespread,…
Descriptors: Generalizability Theory, Intervals, Smoking, Self Efficacy
Long, Jeffrey D.; Loeber, Rolf; Farrington, David P. – Multivariate Behavioral Research, 2009
Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides…
Descriptors: Computation, Inferences, Crime, Models
Choi, Jaehwa; Fan, Weihua; Hancock, Gregory R. – Multivariate Behavioral Research, 2009
This note suggests delta method implementations for deriving confidence intervals for a latent mean effect size measure for the case of 2 independent populations. A hypothetical kindergarten reading example using these implementations is provided, as is supporting LISREL syntax. (Contains 1 table.)
Descriptors: Intervals, Syntax, Effect Size, Evaluation Methods
du Toit, Stephen H. C.; Browne, Michael W. – Multivariate Behavioral Research, 2007
The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…
Descriptors: Structural Equation Models, Evaluation Methods
van Rosmalen, Joost; Koning, Alex J.; Groenen, Patrick J. F. – Multivariate Behavioral Research, 2009
Multiplicative interaction models, such as Goodman's (1981) RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are suitable only for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of…
Descriptors: Class Size, Scaling, Predictor Variables, Models
Kim, Se-Kang; Davison, Mark L.; Frisby, Craig L. – Multivariate Behavioral Research, 2007
This paper describes the Confirmatory Factor Analysis (CFA) parameterization of the Profile Analysis via Multidimensional Scaling (PAMS) model to demonstrate validation of profile pattern hypotheses derived from multidimensional scaling (MDS). Profile Analysis via Multidimensional Scaling (PAMS) is an exploratory method for identifying major…
Descriptors: Profiles, Factor Analysis, Multidimensional Scaling, Evaluation Methods
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

Kroonenberg, Pieter M.; Lombardo, Rosaria – Multivariate Behavioral Research, 1999
Suggests using nonsymmetric correspondence analysis to evaluate contingency tables with a dependence structure. Presents a nontechnical overview with the relevant formulas in an appendix. Illustrates the technique with three examples from diverse areas. (SLD)
Descriptors: Evaluation Methods, Statistical Analysis

Ferrando, Pere J.; Lorenzo-Seva, Urbano – Multivariate Behavioral Research, 1999
Describes the implementation of a standard Pearson chi-square statistic to test the null hypothesis of bivariate normality for latent variables in the Type I censored model. Assesses the behavior of the statistic through simulation and illustrates the statistic through an empirical example. Discusses limitations of the test. (Author/SLD)
Descriptors: Chi Square, Evaluation Methods, Hypothesis Testing, Multivariate Analysis
Raykov, Tenko – Multivariate Behavioral Research, 2006
A method for examining invariance in validity of multiple-component instruments in repeated measure designs is outlined. The approach is developed within the framework of covariance structure modeling and is applicable for purposes of ascertaining temporal stability in scale validity. In addition, the procedure provides a range of plausible values…
Descriptors: Longitudinal Studies, Evaluation Methods, Test Validity, Item Analysis

Abedi, Jamal – Multivariate Behavioral Research, 1996
The Interrater/Test Reliability System (ITRS) is described. The ITRS is a comprehensive computer tool used to address questions of interrater reliability that computes several different indices of interrater reliability and the generalizability coefficient over raters and topics. The system is available in IBM compatible or Macintosh format. (SLD)
Descriptors: Computer Software, Computer Software Evaluation, Evaluation Methods, Evaluators
Song, Xin-Yuan; Lee, Sik-Yum – Multivariate Behavioral Research, 2006
In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…
Descriptors: Structural Equation Models, Bayesian Statistics, Markov Processes, Monte Carlo Methods
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