Is more ever too much? The number of indicators per factor in confirmatory factor analysis

We evaluated whether "more is ever too much" for the number of indicators (p) per factor (p/f) in confirmatory factor analysis by varying sample size (N = 50-1000) and p/f(2-12 items per factor) in 35,000 Monte Carlo solutions. For all Ns, solution behavior steadily improved (more proper solutions, more accurate parameter estimates; greater reliability) with increasing p/f: There was a compensatory relation between N and p/f: large p/f compensated for small Nand large N compensated for small p/f but large-N and large-p/f was best. A bias in the behavior of the chi(2) was also demonstrated where apparent goodness of fit declined with increasing p/f ratios even though approximating models were "true". Fit was similar for proper and improper solutions, as were parameter estimates from improper solutions not involving offending estimates. We also used the 12-p/f data to construct 2, 3, 4, or 6 parcels of items (e.g., two parcels of 6 items per factor, three parcels of 4 items per factor, etc.), but the 12-indicator (nonparceled) solutions were somewhat better behaved. At least for conditions in our simulation study, traditional "rules" implying fewer indicators should be used for smaller N may be inappropriate and researchers should consider using more indicators per factor than is evident in current practice.