A Monte Carlo Study Investigating the Impact of Item Parceling on Measures of Fit in Confirmatory Factor Analysis

Abstract

A simulation study was conducted to examine the effect of item parceling on goodness-of-fit indices at different levels of sample size, number of indicators per factor, factor structure/pattern coefficients, interfactor correlations, and item-level data distribution. Results revealed that the use of item parcels yielded more nonconverged solutions and Heywood cases than individual items. The likelihood of nonconverged solutions and Heywood cases increased as the number of indicators per factor (more items per parcel) decreased. Meanwhile, parcel solutions as compared with item solutions resulted in better fit as measured by the chi-square to degrees-of-freedom ratio, Goodness-of-Fit Index (GFI), Expected Cross-Validation Index (ECVI), and root mean square error of approximation (RMSEA), as well as two incremental fit indices, the Non-Normed Fit Index (NNFI) and Comparative Fit Index (CFI). The same pattern of results was found with data that varied in terms of skewness and kurtosis at the item level. However, the likelihood of nonconverged solutions and Heywood cases was more pronounced when data were extremely skewed/kurtotic at the item level.