A central hallmark of science is replication (Killeen, 2008; Thompson, 2002; Yu, 2003). Unfortunately, replication in EFA can be quite tricky! Even with very large samples, the factor structure and parameter estimates are often unstable or inaccurate reflections of the population. In addition, there is no single effect or test of statistical significance that can be compared across samples—instead there are communalities, eigenvalues, factor loadings, and much more.

Despite the difficulties, replication procedures have a lot to offer EFA. In Chapter 6, we explored a more classic replication methodology of splitting an existing sample (internal replication) or gathering two independent samples (external replication). These classic methods are not as useful with small samples, might require more resources (to recruit larger or duplicate samples), and they can inform us only of the replicability of our estimates in an adjacent sample. However, they do indeed address the question of replicability and provide us with valuable information. In the current chapter we explored an alternative replication method that complements and, in some ways, improves upon the previous methodology. This method of bootstrap resampling seeks to gain insight into the population parameters through the estimation of CIs. While not perfect, resampling methods can inform the researcher as to the relative stability or instability (i.e., replicability or non-replicability) of an effect or result. To our knowledge, this technique has not been widely applied to EFA.

Although these procedures can be somewhat time-consuming and programmatically complex, we hope you see the myriad of benefits that they can offer. We rarely have the ability to know the “true” factor structure in the population (leaving aside the fact that factor structures can vary across subpopulations). Most of the time we have only a sample, a hope that the factor structure will match our theoretical model or models, and optimism that our results will generalize to new samples and studies. The past century or so of exploratory factor analysis has been almost entirely devoted to exploring data in a theoretical way, or seeking to confirm that an instrument matches a theoretical model. It has been rare to see any attention given to what should be at least as important—whether the findings will generalize. In this chapter, we described and explored a methodology to move in that direction. It is not a perfect methodology, as a poor sample will lead to bootstrap analyses that are poor — but, at the least, we can show how precise our results are and how confident we can be about those estimates. As Bruce Thompson has suggested, bootstrap methods can give us valuable information about our results. It can help researchers gain information about their results that they might otherwise not have.