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Physical Sciences and Mathematics Commons™
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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Comparison Of Model Fit Indices Used In Structural Equation Modeling Under Multivariate Normality, Sengul Cangur, Ilker Ercan
Comparison Of Model Fit Indices Used In Structural Equation Modeling Under Multivariate Normality, Sengul Cangur, Ilker Ercan
Journal of Modern Applied Statistical Methods
The purpose of this study is to investigate the impact of estimation techniques and sample sizes on model fit indices in structural equation models constructed according to the number of exogenous latent variables under multivariate normality. The performances of fit indices are compared by considering effects of related factors. The Ratio Chi-square Test Statistic to Degree of Freedom, Root Mean Square Error of Approximation, and Comparative Fit Index are the least affected indices by estimation technique and sample size under multivariate normality, especially with large sample size.
Relative Importance Of Predictors In Multilevel Modeling, Yan Liu, Bruno D. Zumbo, Amery D. Wu
Relative Importance Of Predictors In Multilevel Modeling, Yan Liu, Bruno D. Zumbo, Amery D. Wu
Journal of Modern Applied Statistical Methods
The Pratt index is a useful and practical strategy for day-to-day researchers when ordering predictors in a multiple regression analysis. The purposes of this study are to introduce and demonstrate the use of the Pratt index to assess the relative importance of predictors for a random intercept multilevel model.
Statistical Power Of Alternative Structural Models For Comparative Effectiveness Research: Advantages Of Modeling Unreliability, Emil N. Coman, Eugen Iordache, Lisa Dierker, Judith Fifield, Jean J. Schensul, Suzanne Suggs, Russell Barbour
Statistical Power Of Alternative Structural Models For Comparative Effectiveness Research: Advantages Of Modeling Unreliability, Emil N. Coman, Eugen Iordache, Lisa Dierker, Judith Fifield, Jean J. Schensul, Suzanne Suggs, Russell Barbour
Journal of Modern Applied Statistical Methods
The advantages of modeling the unreliability of outcomes when evaluating the comparative effectiveness of health interventions is illustrated. Adding an action-research intervention component to a regular summer job program for youth was expected to help in preventing risk behaviors. A series of simple two-group alternative structural equation models are compared to test the effect of the intervention on one key attitudinal outcome in terms of model fit and statistical power with Monte Carlo simulations. Some models presuming parameters equal across the intervention and comparison groups were under- powered to detect the intervention effect, yet modeling the unreliability of the outcome …
A Monte Carlo Comparison Of Robust Manova Test Statistics, Holmes Finch, Brian French
A Monte Carlo Comparison Of Robust Manova Test Statistics, Holmes Finch, Brian French
Journal of Modern Applied Statistical Methods
Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform well with respect to Type I error and power. There are several alternative test statistics that can be considered including robust statistics and the use of the structural equation modeling (SEM) framework. This simulation study focused on comparing the performance of the P test …
Indeterminacy Of Factor Score Estimates In Slightly Misspecified Confirmatory Factor Models, André Beauducel
Indeterminacy Of Factor Score Estimates In Slightly Misspecified Confirmatory Factor Models, André Beauducel
Journal of Modern Applied Statistical Methods
Two methods to calculate a measure for the quality of factor score estimates have been proposed. These methods were compared by means of a simulation study. The method based on a covariance matrix reproduced from a model leads to smaller effects of sampling error.
Impact Of Measurement Model Modification On Structural Parameter Integrity When Measurement Model Is Misspecified, Weihua Fan
Journal of Modern Applied Statistical Methods
In the process of model modification, parameters of residual covariances are often treated as free parameters to improve model fit. However, the effect of such measurement model modifications on the important structural parameter estimates under various measurement model misspecifications has not been systematically studied. Monte Carlo simulation was conducted to compare structural estimates before and after measurement model modifications of adding residual covariances under varying sample sizes and model misspecifications. Results showed that researchers should pay attention when such measurement model modifications are made to initially misspecified model with missing path(s).
An Evaluation Of Multiple Imputation For Meta-Analytic Structural Equation Modeling, Carolyn F. Furlow, S. Natasha Beretvas
An Evaluation Of Multiple Imputation For Meta-Analytic Structural Equation Modeling, Carolyn F. Furlow, S. Natasha Beretvas
Journal of Modern Applied Statistical Methods
A simulation study was used to evaluate multiple imputation (MI) to handle MCAR correlations in the first step of meta-analytic structural equation modeling: the synthesis of the correlation matrix and the test of homogeneity. No substantial parameter bias resulted from using MI. Although some SE bias was found for meta-analyses involving smaller numbers of studies, the homogeneity test was never rejected when using MI.
Can Specification Searches Be Useful For Hypothesis Generation?, Samuel B. Green, Marilyn S. Thompson
Can Specification Searches Be Useful For Hypothesis Generation?, Samuel B. Green, Marilyn S. Thompson
Journal of Modern Applied Statistical Methods
Previous studies suggest that results from specification searches, as typically employed in structural equation modeling, should not be used to reach strong research conclusions due to their poor reliability. Analyses of computer generated data indicate that search results can be sufficiently reliable for exploratory purposes with properly designed and analyzed studies.
Second-Order Latent Growth Models With Shifting Indicators, Gregory R. Hancock, Michelle M. Buehl
Second-Order Latent Growth Models With Shifting Indicators, Gregory R. Hancock, Michelle M. Buehl
Journal of Modern Applied Statistical Methods
Second-order latent growth models assess longitudinal change in a latent construct, typically employing identical manifest variables as indicators across time. However, the same indicators may be unavailable and/or inappropriate for all time points. This article details methods for second-order growth models in which constructs’ indicators shift over time.