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Träfflista för sökning "WFRF:(Bollen Kenneth A.) "

Sökning: WFRF:(Bollen Kenneth A.)

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1.
  • Benjamin, Daniel J., et al. (författare)
  • Redefine statistical significance
  • 2018
  • Ingår i: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 2:1, s. 6-10
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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2.
  • Giordano, Michael L., et al. (författare)
  • Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables
  • 2022
  • Ingår i: Structural Equation Modeling. - : Taylor & Francis Group. - 1070-5511 .- 1532-8007. ; 29:4, s. 584-599
  • Tidskriftsartikel (refereegranskat)abstract
    • This study develops a new limited information estimator for random intercept Multilevel Structural Equation Models (MSEM). It is based on the Model Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) estimator, which has been shown to be an excellent alternative or supplement to maximum likelihood (ML) in SEMs (Bollen, 1996). We also develop a multilevel overidentification test statistic that applies to equations at the within or between levels. Our Monte Carlo simulation analysis suggests that MIIV-2SLS is more robust than ML to misspecification at within or between levels, performs well given fewer than 100 clusters, and shows that our multilevel overidentification test for equations performs well at both levels of the model.
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3.
  • Jin, Shaobo, 1987-, et al. (författare)
  • A unified model-implied instrumental variable approach for structural equation modeling with mixed variables
  • 2021
  • Ingår i: Psychometrika. - : Springer Nature. - 0033-3123 .- 1860-0980. ; 86:2, s. 564-594
  • Tidskriftsartikel (refereegranskat)abstract
    • The model-implied instrumental variable (MIIV) estimator is an equation-by-equation estimator of structural equation models that is more robust to structural misspecifications than full information estimators. Previous studies have concentrated on endogenous variables that are all continuous (MIIV-2SLS) or all ordinal . We develop a unified MIIV approach that applies to a mixture of binary, ordinal, censored, or continuous endogenous observed variables. We include estimates of factor loadings, regression coefficients, variances, and covariances along with their asymptotic standard errors. In addition, we create new goodness of fit tests of the model and overidentification tests of single equations. Our simulation study shows that the proposed MIIV approach is more robust to structural misspecifications than diagonally weighted least squares (DWLS) and that both the goodness of fit model tests and the overidentification equations tests can detect structural misspecifications. We also find that the bias in asymptotic standard errors for the MIIV estimators of factor loadings and regression coefficients are often lower than the DWLS ones, though the differences are small in large samples. Our analysis shows that scaling indicators with low reliability can adversely affect the MIIV estimators. Also, using a small subset of MIIVs reduces small sample bias of coefficient estimates, but can lower the power of overidentification tests of equations.
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