Standardized Parameters in Misspecified Structural Equation Models: Empirical Performance in Point Estimates, Standard Errors, and Confidence Intervals

2017 ◽  
Vol 24 (4) ◽  
pp. 571-584 ◽  
Author(s):  
Keke Lai ◽  
Xiaoguang Zhang
Keyword(s):  
Confidence Intervals ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Standard Errors ◽  
Point Estimates ◽  
Empirical Performance ◽  
Standardized Parameters

scholarly journals A unified model-implied instrumental variable approach for structural equation modeling with mixed variables

Psychometrika ◽  
2021 ◽  
Author(s):  
Shaobo Jin ◽  
Fan Yang-Wallentin ◽  
Kenneth A. Bollen
Keyword(s):  
Instrumental Variable ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Small Sample ◽  
Regression Coefficients ◽  
Equation Modeling ◽  
Standard Errors ◽  
Asymptotic Standard Errors ◽  
Factor Loadings

AbstractThe 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.

Measuring the Big Five in Self-Report and Other Ratings

2000 ◽  
Vol 16 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Claudio Barbaranelli ◽  
Gian Vittorio Caprara
Keyword(s):  
Big Five ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Error Variance ◽  
The Self ◽  
Self Report ◽  
Sources Of Information ◽  
Third Person ◽  
Fit Indexes ◽  
Response Modes

Summary: The aim of the study is to assess the construct validity of two different measures of the Big Five, matching two “response modes” (phrase-questionnaire and list of adjectives) and two sources of information or raters (self-report and other ratings). Two-hundred subjects, equally divided in males and females, were administered the self-report versions of the Big Five Questionnaire (BFQ) and the Big Five Observer (BFO), a list of bipolar pairs of adjectives ( Caprara, Barbaranelli, & Borgogni, 1993 , 1994 ). Every subject was rated by six acquaintances, then aggregated by means of the same instruments used for the self-report, but worded in a third-person format. The multitrait-multimethod matrix derived from these measures was then analyzed via Structural Equation Models according to the criteria proposed by Widaman (1985) , Marsh (1989) , and Bagozzi (1994) . In particular, four different models were compared. While the global fit indexes of the models were only moderate, convergent and discriminant validities were clearly supported, and method and error variance were moderate or low.

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