ABSTRACT
This chapter focuses on the identification of outliers in meta-analytic data. When we use the term outlier, we refer to a single data point that is extreme in its value relative to other values of the variable. Outliers are important because they can have a substantial impact on empirical findings, and subsequently, the conclusions that we draw from them. As Cortina and Gully (1999) noted, outliers can exist at many different levels including the data point outlier, the case outlier, the variable outlier, and the study outlier. Our primary focus in this chapter is the study outlier. In addition to a general discussion of outliers in meta-analytic data, we present Huffcutt and Arthur’s (1995) sample-adjusted meta-analytic deviancy procedure, a technique for identifying outliers in meta-analytic data. The SAS code for conducting this outlier analysis for both correlations (rs) and effect sizes (ds) is presented along with illustrative examples.
