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Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel – Multivariate Behavioral Research, 2012
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…
Descriptors: Bayesian Statistics, Factor Analysis, Models, Simulation
Tong, Xin; Zhang, Zhiyong – Multivariate Behavioral Research, 2012
Growth curve models with different types of distributions of random effects and of intraindividual measurement errors for robust analysis are compared. After demonstrating the influence of distribution specification on parameter estimation, 3 methods for diagnosing the distributions for both random effects and intraindividual measurement errors…
Descriptors: Models, Robustness (Statistics), Statistical Analysis, Error of Measurement
Brosseau-Liard, Patricia E.; Savalei, Victoria; Li, Libo – Multivariate Behavioral Research, 2012
The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have…
Descriptors: Structural Equation Models, Goodness of Fit, Maximum Likelihood Statistics, Robustness (Statistics)
Estabrook, Ryne; Neale, Michael – Multivariate Behavioral Research, 2013
Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the existence of missing data have generally been ignored in this debate. This article presents a simulation study…
Descriptors: Factor Analysis, Scores, Computation, Regression (Statistics)
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
Austin, Peter C. – Multivariate Behavioral Research, 2012
Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one…
Descriptors: Computation, Regression (Statistics), Statistical Bias, Error of Measurement
Gottschall, Amanda C.; West, Stephen G.; Enders, Craig K. – Multivariate Behavioral Research, 2012
Behavioral science researchers routinely use scale scores that sum or average a set of questionnaire items to address their substantive questions. A researcher applying multiple imputation to incomplete questionnaire data can either impute the incomplete items prior to computing scale scores or impute the scale scores directly from other scale…
Descriptors: Questionnaires, Data Analysis, Computation, Monte Carlo Methods
Hung, Lai-Fa – Multivariate Behavioral Research, 2011
The process-component approach has become quite popular for examining many psychological concepts. A typical example is the model with internal restrictions on item difficulty (MIRID) described by Butter (1994) and Butter, De Boeck, and Verhelst (1998). This study proposes a hierarchical generalized random-situation random-weight MIRID. The…
Descriptors: Markov Processes, Computer Software, Psychology, Computation
Zhong, Xiaoling; Yuan, Ke-Hai – Multivariate Behavioral Research, 2011
In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data,…
Descriptors: Structural Equation Models, Simulation, Racial Identification, Computation
Austin, Peter C. – Multivariate Behavioral Research, 2011
Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being…
Descriptors: Smoking, Hospitals, Program Effectiveness, Probability
Vallejo, G.; Fernandez, M. P.; Livacic-Rojas, P. E.; Tuero-Herrero, E. – Multivariate Behavioral Research, 2011
Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured…
Descriptors: Personality Problems, Psychosis, Prevention, Patients
Maydeu-Olivares, Alberto; Brown, Anna – Multivariate Behavioral Research, 2010
The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally…
Descriptors: Item Response Theory, Rating Scales, Models, Comparative Analysis
Steyn, H. S., Jr.; Ellis, S. M. – Multivariate Behavioral Research, 2009
When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…
Descriptors: Effect Size, Multivariate Analysis, Computation, Monte Carlo Methods
Culpepper, Steven Andrew – Multivariate Behavioral Research, 2009
This study linked nonlinear profile analysis (NPA) of dichotomous responses with an existing family of item response theory models and generalized latent variable models (GLVM). The NPA method offers several benefits over previous internal profile analysis methods: (a) NPA is estimated with maximum likelihood in a GLVM framework rather than…
Descriptors: Profiles, Item Response Theory, Models, Maximum Likelihood Statistics
Maydeu-Olivares, Alberto; Hernandez, Adolfo – Multivariate Behavioral Research, 2007
The interpretation of a Thurstonian model for paired comparisons where the utilities' covariance matrix is unrestricted proved to be difficult due to the comparative nature of the data. We show that under a suitable constraint the utilities' correlation matrix can be estimated, yielding a readily interpretable solution. This set of identification…
Descriptors: Identification, Structural Equation Models, Matrices, Comparative Analysis
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