After rotation (see below for rotation criteria) compare the item loading tables; the one with the “cleanest” factor structure – item loadings above .30, no or few item crossloadings, no factors with fewer than three items – has the best fit to the data. If all loading tables look messy or uninterpretable then there is a problem with the data that cannot be resolved by manipulating the number of factors retained. Sometimes dropping problematic items (ones that are low-loading, crossloading or freestanding) and rerunning the analysis can solve the problem, but the researcher has to consider if doing so compromises the integrity of the data. If the factor structure still fails to clarify after multiple test runs, there is a problem with item construction, scale design, or the hypothesis itself, and the researcher may need to throw out the data as unusable and start from scratch. One other possibility is that the sample size was too small and more data needs to be collected before running the analyses; this issue is addressed later in this paper.