Although authors have been presenting methods for summarizing replication in EFA for half a century and more, most summarization techniques have been flawed or less informative than ideal. In the 21st century, with CFA invariance analysis as the gold standard for assessing generalizability and replicability, replication within EFA has an important role to play—but a different role than half a century ago. Today, replication in EFA is a starting point—it adds value to EFA analyses in that it helps indicate the extent to which these models are likely to generalize to the next data set, and also helps to further identify volatile or problematic items. This information is potentially helpful in the process of developing and validating an instrument, as well as for potential users of an instrument that has yet to undergo CFA invariance analysis.

However, there are often barriers to replication analysis. Foremost among these barriers is the lack of adequate sample size in most EFAs that are reported in the literature. The first priority for researchers should be adequate samples. The second should be estimation of the replicability (or stability) of the model presented. In the next chapter we review bootstrap analysis as a potential solution to this issue, as it allows use of a single, appropriately large sample to estimate the potential volatility of a scale.