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Zita Oravecz - One of the best experts on this subject based on the ideXlab platform.
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bayesian estimation of the true score Multitrait Multimethod model with a split ballot design
Structural Equation Modeling, 2018Co-Authors: Jonathan L Helm, Laura Castroschilo, Diana Zavalarojas, Anna Decastellarnau, Zita OraveczAbstract:This article examines whether Bayesian estimation with minimally informed prior distributions can alleviate the estimation problems often encountered with fitting the true score Multitrait–Multimethod structural equation model with split-ballot data. In particular, the true score Multitrait–Multimethod structural equation model encounters an empirical underidentification when (a) latent variable correlations are homogenous, and (b) fitted to data from a 2-group split-ballot design; an understudied case of empirical underidentification due to a planned missingness (i.e., split-ballot) design. A Monte Carlo simulation and 3 empirical examples showed that Bayesian estimation performs better than maximum likelihood (ML) estimation. Therefore, we suggest using Bayesian estimation with minimally informative prior distributions when estimating the true score Multitrait–Multimethod structural equation model with split-ballot data. Furthermore, given the increase in planned missingness designs in psychological res...
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Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Anna Decastellarnau, Laura Castro-schilo, Diana Zavala-rojas, Zita OraveczAbstract:This article examines whether Bayesian estimation with minimally informed prior distributions can alleviate the estimation problems often encountered with fitting the true score Multitrait–Multimethod structural equation model with split-ballot data. In particular, the true score Multitrait–Multimethod structural equation model encounters an empirical underidentification when (a) latent variable correlations are homogenous, and (b) fitted to data from a 2-group split-ballot design; an understudied case of empirical underidentification due to a planned missingness (i.e., split-ballot) design. A Monte Carlo simulation and 3 empirical examples showed that Bayesian estimation performs better than maximum likelihood (ML) estimation. Therefore, we suggest using Bayesian estimation with minimally informative prior distributions when estimating the true score Multitrait–Multimethod structural equation model with split-ballot data. Furthermore, given the increase in planned missingness designs in psychological res...
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bayesian versus maximum likelihood estimation of Multitrait Multimethod confirmatory factor models
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Laura Castroschilo, Zita OraveczAbstract:This article compares maximum likelihood and Bayesian estimation of the correlated trait–correlated method (CT–CM) confirmatory factor model for Multitrait–Multimethod (MTMM) data. In particular, Bayesian estimation with minimally informative prior distributions—that is, prior distributions that prescribe equal probability across the known mathematical range of a parameter—are investigated as a source of information to aid convergence. Results from a simulation study indicate that Bayesian estimation with minimally informative priors produces admissible solutions more often maximum likelihood estimation (100.00% for Bayesian estimation, 49.82% for maximum likelihood). Extra convergence does not come at the cost of parameter accuracy; Bayesian parameter estimates showed comparable bias and better efficiency compared to maximum likelihood estimates. The results are echoed via 2 empirical examples. Hence, Bayesian estimation with minimally informative priors outperforms enables admissible solutions of the CT...
Jonathan L Helm - One of the best experts on this subject based on the ideXlab platform.
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bayesian estimation of the true score Multitrait Multimethod model with a split ballot design
Structural Equation Modeling, 2018Co-Authors: Jonathan L Helm, Laura Castroschilo, Diana Zavalarojas, Anna Decastellarnau, Zita OraveczAbstract:This article examines whether Bayesian estimation with minimally informed prior distributions can alleviate the estimation problems often encountered with fitting the true score Multitrait–Multimethod structural equation model with split-ballot data. In particular, the true score Multitrait–Multimethod structural equation model encounters an empirical underidentification when (a) latent variable correlations are homogenous, and (b) fitted to data from a 2-group split-ballot design; an understudied case of empirical underidentification due to a planned missingness (i.e., split-ballot) design. A Monte Carlo simulation and 3 empirical examples showed that Bayesian estimation performs better than maximum likelihood (ML) estimation. Therefore, we suggest using Bayesian estimation with minimally informative prior distributions when estimating the true score Multitrait–Multimethod structural equation model with split-ballot data. Furthermore, given the increase in planned missingness designs in psychological res...
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Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Anna Decastellarnau, Laura Castro-schilo, Diana Zavala-rojas, Zita OraveczAbstract:This article examines whether Bayesian estimation with minimally informed prior distributions can alleviate the estimation problems often encountered with fitting the true score Multitrait–Multimethod structural equation model with split-ballot data. In particular, the true score Multitrait–Multimethod structural equation model encounters an empirical underidentification when (a) latent variable correlations are homogenous, and (b) fitted to data from a 2-group split-ballot design; an understudied case of empirical underidentification due to a planned missingness (i.e., split-ballot) design. A Monte Carlo simulation and 3 empirical examples showed that Bayesian estimation performs better than maximum likelihood (ML) estimation. Therefore, we suggest using Bayesian estimation with minimally informative prior distributions when estimating the true score Multitrait–Multimethod structural equation model with split-ballot data. Furthermore, given the increase in planned missingness designs in psychological res...
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bayesian versus maximum likelihood estimation of Multitrait Multimethod confirmatory factor models
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Laura Castroschilo, Zita OraveczAbstract:This article compares maximum likelihood and Bayesian estimation of the correlated trait–correlated method (CT–CM) confirmatory factor model for Multitrait–Multimethod (MTMM) data. In particular, Bayesian estimation with minimally informative prior distributions—that is, prior distributions that prescribe equal probability across the known mathematical range of a parameter—are investigated as a source of information to aid convergence. Results from a simulation study indicate that Bayesian estimation with minimally informative priors produces admissible solutions more often maximum likelihood estimation (100.00% for Bayesian estimation, 49.82% for maximum likelihood). Extra convergence does not come at the cost of parameter accuracy; Bayesian parameter estimates showed comparable bias and better efficiency compared to maximum likelihood estimates. The results are echoed via 2 empirical examples. Hence, Bayesian estimation with minimally informative priors outperforms enables admissible solutions of the CT...
Laura Castroschilo - One of the best experts on this subject based on the ideXlab platform.
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bayesian estimation of the true score Multitrait Multimethod model with a split ballot design
Structural Equation Modeling, 2018Co-Authors: Jonathan L Helm, Laura Castroschilo, Diana Zavalarojas, Anna Decastellarnau, Zita OraveczAbstract:This article examines whether Bayesian estimation with minimally informed prior distributions can alleviate the estimation problems often encountered with fitting the true score Multitrait–Multimethod structural equation model with split-ballot data. In particular, the true score Multitrait–Multimethod structural equation model encounters an empirical underidentification when (a) latent variable correlations are homogenous, and (b) fitted to data from a 2-group split-ballot design; an understudied case of empirical underidentification due to a planned missingness (i.e., split-ballot) design. A Monte Carlo simulation and 3 empirical examples showed that Bayesian estimation performs better than maximum likelihood (ML) estimation. Therefore, we suggest using Bayesian estimation with minimally informative prior distributions when estimating the true score Multitrait–Multimethod structural equation model with split-ballot data. Furthermore, given the increase in planned missingness designs in psychological res...
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bayesian versus maximum likelihood estimation of Multitrait Multimethod confirmatory factor models
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Laura Castroschilo, Zita OraveczAbstract:This article compares maximum likelihood and Bayesian estimation of the correlated trait–correlated method (CT–CM) confirmatory factor model for Multitrait–Multimethod (MTMM) data. In particular, Bayesian estimation with minimally informative prior distributions—that is, prior distributions that prescribe equal probability across the known mathematical range of a parameter—are investigated as a source of information to aid convergence. Results from a simulation study indicate that Bayesian estimation with minimally informative priors produces admissible solutions more often maximum likelihood estimation (100.00% for Bayesian estimation, 49.82% for maximum likelihood). Extra convergence does not come at the cost of parameter accuracy; Bayesian parameter estimates showed comparable bias and better efficiency compared to maximum likelihood estimates. The results are echoed via 2 empirical examples. Hence, Bayesian estimation with minimally informative priors outperforms enables admissible solutions of the CT...
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augmenting the correlated trait correlated method model for Multitrait Multimethod data
Structural Equation Modeling, 2016Co-Authors: Laura Castroschilo, Kevin J Grimm, Keith F. WidamanAbstract:We introduce an approach for ensuring empirical identification of the correlated trait–correlated method (CT–CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait–correlated method (ACT–CM) models because they are based on systematically augmenting the Multitrait–Multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT–CM model, but a well-identified fully augmented correlated trait–correlated method (FACT–CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor—a specific case shown to lead to an empirically underidentified CT–CM model.
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Multitrait Multimethod analysis
The Encyclopedia of Clinical Psychology, 2015Co-Authors: Keith F. Widaman, Laura CastroschiloAbstract:Multitrait–Multimethod (MTMM) analysis is based on the assumption that every measurement in psychology is a trait–method unit, and so contains both trait-related and method-related variance. If each of a set of traits is measured using each of a set of methods, an MTMM matrix of correlations among measures can be calculated. Convergent validation is supported if measures of purportedly the same trait correlate highly across different methods of measurement, discriminant validation is supported if measures of different traits correlate at relatively low levels, and method variance is shown if measures obtained with the same method of measurement correlate highly. Initially, informal rules involving comparisons of correlations within an MTMM were employed to evaluate convergent and discriminant validation. These methods have been supplanted by statistical approaches using confirmatory factor analysis to represent and test patterns of relations among measures. Keywords: data analysis in psychology; psychometric testing; methodology; modeling
Deborah A Kashy - One of the best experts on this subject based on the ideXlab platform.
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analysis of the Multitrait Multimethod matrix by confirmatory factor analysis
Psychological Bulletin, 1992Co-Authors: David A Kenny, Deborah A KashyAbstract:The Multitrait-Multimethod (MTMM) matrix permits examination of the convergent and discriminant validity of psychological measures. Estimation using confirmatory factor analysis (CFA), the predominant analytical technique, has often resulted in severe difficulties, such as out-of-range estimates and convergence problems. This article shows that an important special case of one of the more frequently advocated CFA models is not identified and is therefore not estimable. Because most MTMM data are likely to conform closely to this special case, resulting analyses suffer from empirical underidentification. Alternaive CFA models are discussed
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Analysis of the Multitrait^Multimethod matrix by confirmatory factor analysis
Psychological Bulletin, 1992Co-Authors: David A Kenny, Deborah A KashyAbstract:The Multitrait-Multimethod (MTMM) matrix permits examination of the convergent and discriminant validity of psychological measures. Estimation using confirmatory factor analysis (CFA), the predominant analytical technique, has often resulted in severe difficulties, such as out-of-range estimates and convergence problems. This article shows that an important special case of one of the more frequently advocated CFA models is not identified and is therefore not estimable. Because most MTMM data are likely to conform closely to this special case, resulting analyses suffer from empirical underidentification. Alternaive CFA models are discussed
Willem E Saris - One of the best experts on this subject based on the ideXlab platform.
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the latent class Multitrait Multimethod model
Psychological Methods, 2015Co-Authors: Daniel L Oberski, Jacques A Hagenaars, Willem E SarisAbstract:A latent class Multitrait-Multimethod (MTMM) model is proposed to estimate random and systematic measurement error in categorical survey questions while making fewer assumptions than have been made so far in such evaluations, allowing for possible extreme response behavior and other nonmonotone effects. The method is a combination of the MTMM research design of Campbell and Fiske (1959), the basic response model for survey questions of Saris and Andrews (1991), and the latent class factor model of Vermunt and Magidson (2004, pp. 227-230). The latent class MTMM model thus combines an existing design, model, and method to allow for the estimation of the degree to and manner in which survey questions are affected by systematic measurement error. Starting from a general form of the response function for a survey question, we present the MTMM experimental approach to identification of the response function's parameters. A "trait-method biplot" is introduced as a means of interpreting the estimates of systematic measurement error, whereas the quality of the questions can be evaluated by item information curves and the item information function. An experiment from the European Social Survey is analyzed and the results are discussed, yielding valuable insights into the functioning of a set of example questions on the role of women in society in 2 countries.
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the split ballot Multitrait Multimethod approach implementation and problems
Structural Equation Modeling, 2013Co-Authors: Melanie Revilla, Willem E SarisAbstract:Saris, Satorra, and Coenders (2004) proposed a new approach to estimate the quality of survey questions, combining the advantages of 2 existing approaches: the Multitrait–Multimethod (MTMM) and the split-ballot (SB) ones. Implemented in practice, this new approach led to frequent problems of nonconvergence and improper solutions. This article uses Monte Carlo simulations to understand why the SB-MTMM is working well in some cases but not in others. The number of SB groups is a crucial element: The 3-group design is performing better. However, the 2-group design can also perform well: The analyses suggest that the interaction between the absolute values of the correlations between the traits and the relative values of the different correlations between traits plays an important role.
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fit of different models for Multitrait Multimethod experiments
Structural Equation Modeling, 2002Co-Authors: Irmgard W Corten, Germà Coenders, Willem E Saris, William M Van Der Veld, Chris E Aalberts, Charles KornelisAbstract:In the past, several models have been developed for the estimation of the reliability and validity of measurement instruments from Multitrait-Multimethod (MTMM) experiments. Suggestions have been made for additive, multiplicative and correlated uniqueness models, whereas recently Coenders and Saris (2000) suggested a procedure to test these models against one another. In this article, the different models suggested for the analysis of MTMM matrixes have been compared for their fit to 87 data sets collected in the United States (Andrews, 1984; Rodgers, Andrews, & Herzog, 1992), Austria (Koltringer, 1995), and the Netherlands (Scherpenzeel & Saris, 1997). As most variables are categorical, the analysis has been carried out on the basis of polychoric-polyserial correlation coefficients and of Pearson correlations. The fit of the models based on polychoric correlations is much worse than the fit of models based on product moment correlations, but in both cases a model that assumes additive method effects fits...
