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Zigler, Christina K.; Ye, Feifei – AERA Online Paper Repository, 2016
Mediation in multi-level data can be examined using conflated multilevel modeling (CMM), unconflated multilevel modeling (UMM), or multilevel structural equation modeling (MSEM). A Monte Carlo study was performed to compare the three methods on bias, type I error, and power in a 1-1-1 model with random slopes. The three methods showed no…
Descriptors: Hierarchical Linear Modeling, Structural Equation Models, Monte Carlo Methods, Statistical Bias
Capraro, Robert M.; Graham, James M. – 2002
This paper illustrates first how estimated Structural Equation Modeling (SEM) measurement error variances are actually estimates of score reliabilities. The major advantage of SEM over other analytic methods is that it accounts for measurement error. Score reliabilities are estimated as part of structural modeling, so that structural models test…
Descriptors: Error of Measurement, Estimation (Mathematics), Reliability, Scores
Nevitt, Jonathan – 2000
Structural equation modeling (SEM) attempts to remove the negative influence of measurement error and allows for investigation of relationships at the level of the underlying constructs of interest. SEM has been regarded as a "large sample" technique since its inception. Recent developments in SEM, some of which are currently available…
Descriptors: Error of Measurement, Goodness of Fit, Maximum Likelihood Statistics, Monte Carlo Methods
Nevitt, Johnathan; Hancock, Gregory R. – 1998
Though common structural equation modeling (SEM) methods are predicated upon the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to use distribution-free estimation methods. Fortunately, promising alternatives are being integrated into…
Descriptors: Chi Square, Computer Software, Error of Measurement, Estimation (Mathematics)
Wang, Lin; And Others – 1995
Research in structured equation modeling (SEM) suggests that nonnormal data will invalidate chi-square tests and produce erroneous standard errors. However, much remains unknown about the extent to which, and the conditions under which nonnormal data can affect SEM application, especially when excessive skewness and kurtosis are present in data.…
Descriptors: Behavior Patterns, Chi Square, Children, Error of Measurement