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Lam, Kar Yin; Koning, Alex J.; Franses, Philip Hans – Multivariate Behavioral Research, 2011
We consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we avoided the computation of high-dimensional integrals. We extended the approximation technique proposed by Henery (1981) in the context of the Thurstone-Mosteller-Daniels model to any…
Descriptors: Probability, Evaluation Research, Computation, Experiments
Zhang, Guangjian; Preacher, Kristopher J.; Luo, Shanhong – Multivariate Behavioral Research, 2010
This article is concerned with using the bootstrap to assign confidence intervals for rotated factor loadings and factor correlations in ordinary least squares exploratory factor analysis. Coverage performances of "SE"-based intervals, percentile intervals, bias-corrected percentile intervals, bias-corrected accelerated percentile…
Descriptors: Intervals, Sample Size, Factor Analysis, Least Squares Statistics
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
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
Kwok, Oi-man; West, Stephen G.; Green, Samuel B. – Multivariate Behavioral Research, 2007
This Monte Carlo study examined the impact of misspecifying the [big sum] matrix in longitudinal data analysis under both the multilevel model and mixed model frameworks. Under the multilevel model approach, under-specification and general-misspecification of the [big sum] matrix usually resulted in overestimation of the variances of the random…
Descriptors: Monte Carlo Methods, Data Analysis, Computation, Longitudinal Studies
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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