The goal of this chapter was to summarize some of the scholarship surrounding the age-old question of “how large a sample is large enough?” Unfortunately, the answer is not simple. When data contains a clear factor structure, the results might be seen in smaller sample sizes. When the factor structure is not clear, larger samples and subject to item ratios provide more accurate results. These findings, however, are of little practical use because the magnitude of item loadings is often unknown before a project begins and not within the researcher’s control. In addition, even when the data does have a clear factor structure and a relatively large subject to item ratio (as was the case with the SDQ data), the error rate can exceed the standard alpha level of .05. It is true that larger sample sizes and subject to item ratios are better and more reliable than small, but there is still some amount of error in our results! In general, though, we would support Jöreskog and Sörbom’s (1996) recommendation of at least 10 participants per parameter estimated; but we would encourage researchers to try to get closer to 20 participants per parameter.

The controversy around sample size again reinforces the point that EFA is exploratory. It should be used only for exploring data, not hypothesis or theory testing, nor is it suited to “validation” of instruments. We have seen many cases where researchers used EFA when they should have used confirmatory factor analysis. After an instrument has been developed using EFA and other techniques, it is time to move to confirmatory factor analysis to answer questions such as “does an instrument have the same structure across certain population subgroups?” Based on the data presented in this chapter, we think it is safe to conclude that researchers using large samples and making informed choices from the options available for data analysis are the ones most likely to accomplish their goal: to come to conclusions that will generalize beyond a particular sample to either another sample or to the population (or a population) of interest. To do less is to arrive at conclusions that are unlikely to be of any use or interest beyond that sample and that analysis.