The Impact of Item Parceling on Structural Parameter Invariance in Multi-group Structural Equation Modeling

Abstract

ABSTRACT Item parcels are the indicators of a factor which are created by summing or averaging scores of individual items. Item parceling has been widely applied in structural equation modeling (SEM) but has been less discussed in the multi-group context, especially for structural parameter invariance. The present study investigated the effect of item parceling on parameter bias and invariance tests at the structural level (i.e., latent means and a structural path) in multi-group SEM using a Monte Carlo simulation study. The results show that under a correctly specified measurement invariance model, indicator type would not introduce bias in structural parameter estimates. The estimation and statistical decision would depend on the assumption for measurement (non-)invariance and testing method, rather than the indicator type. When falsely assuming measurement invariance, latent mean or structural path differences are likely to be biased and to conclude incorrect statistical decisions in testing.