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Eunsook Kim; Diep Nguyen; Siyu Liu; Yan Wang – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Factor mixture modeling (FMM) is generally complex with both unobserved categorical and unobserved continuous variables. We explore the potential of item parceling to reduce the model complexity of FMM and improve convergence and class enumeration accordingly. To this end, we conduct Monte Carlo simulations with three types of data, continuous,…
Descriptors: Structural Equation Models, Factor Analysis, Factor Structure, Monte Carlo Methods
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Gonzalez, Oscar; MacKinnon, David P. – Educational and Psychological Measurement, 2018
Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to…
Descriptors: Statistical Analysis, Monte Carlo Methods, Measurement, Models
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Kilic, Abdullah Faruk; Uysal, Ibrahim; Atar, Burcu – International Journal of Assessment Tools in Education, 2020
This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance…
Descriptors: Factor Analysis, Computation, Methods, Sample Size
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Huang, Jiajing; Liang, Xinya; Yang, Yanyun – AERA Online Paper Repository, 2017
In Bayesian structural equation modeling (BSEM), prior settings may affect model fit, parameter estimation, and model comparison. This simulation study was to investigate how the priors impact evaluation of relative fit across competing models. The design factors for data generation included sample sizes, factor structures, data distributions, and…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Sample Size
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Kim, Seohyun; Lu, Zhenqiu; Cohen, Allan S. – Measurement: Interdisciplinary Research and Perspectives, 2018
Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables.…
Descriptors: Bayesian Statistics, Structural Equation Models, Computation, Social Science Research
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Asún, Rodrigo A.; Rdz-Navarro, Karina; Alvarado, Jesús M. – Sociological Methods & Research, 2016
This study compares the performance of two approaches in analysing four-point Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted…
Descriptors: Likert Scales, Item Analysis, Factor Analysis, Comparative Analysis
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Parker, Kevin C. H.; Atkinson, Leslie – Psychological Assessment, 1995
The statistical ramifications of using equal weights, as compared to regression-based differential weights, in the estimation of Wechsler Adult Intelligence Scale--Revised (WAIS-R) factor scores were explored using WAIS standardization data, based on 1,880 adults. Differences resulting from the two approaches were substantial, as is discussed.…
Descriptors: Computation, Estimation (Mathematics), Factor Structure, Intelligence
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Alhija, Fadia Nasser-Abu; Wisenbaker, Joseph – Structural Equation Modeling: A Multidisciplinary Journal, 2006
A simulation study was conducted to examine the effect of item parceling on confirmatory factor analysis parameter estimates and their standard errors at different levels of sample size, number of indicators per factor, size of factor structure/pattern coefficients, magnitude of interfactor correlations, and variations in item-level data…
Descriptors: Monte Carlo Methods, Computation, Factor Analysis, Sample Size