GOODNESS-OF-FIT IN CONFIRMATORY FACTOR-ANALYSIS - THE EFFECTS OF SAMPLE-SIZE AND MODEL PARSIMONY

The purpose of the present investigation is to examine the influence o f sample size (N) and model parsimony on a set of 22 goodness-of-fit i ndices including those typically used in confirmatory factor analysis and some recently developed indices. For sample data simulated from tw o known population data structures, values for 6 of 22 fit indices wer e reasonably independent of N and were not significantly affected by e stimating parameters known to have zero values in the population: two indices based on noncentrality described by McDonald (1989; McDonald a nd Marsh, 1990), a relative (incremental) index based on noncentrality (Bentler, 1990; McDonald & Marsh, 1990), unbiased estimates of LISREL 's GFI and AGFI (Joreskog & Sorbom, 1981) presented by Steiger (1989, 1990) that are based on noncentrality, and the widely known relative i ndex developed by Tucker and Lewis (1973). Penalties for model complex ity designed to control for sampling fluctuations and to address the i nevitable compromise between goodness of fit and model parsimony were evaluated.