Estimating Standardized SEM Parameters Given Nonnormal Data and Incorrect Model: Methods and Comparison. Issue 4 (4th July 2018) - Lai, Keke
Abstract : When both model misspecifications and nonnormal data are present, it is unknown how trustworthy various point estimates, standard errors ( SE s), and confidence intervals (CIs) are for standardized structural equation modeling parameters. We conducted simulations to evaluate maximum likelihood (ML), conventional robust SE estimator (MLM), Huber–White robust SE estimator (MLR), and the bootstrap (BS). We found (a) ML point estimates can sometimes be quite biased at finite sample sizes if misfit and nonnormality are serious; (b) ML and MLM generally give egregiously biased SE s and CIs regardless of the degree of misfit and nonnormality; (c) MLR and BS provide trustworthy SE s and CIs given medium misfit and nonnormality, but BS is better; and (d) given severe misfit and nonnormality, MLR tends to break down and BS begins to struggle.