Extreme-Groups Designs in Studies of Dimensional Phenomena: Advantages, Caveats, and Recommendations

Extreme-groups designs (EGDs) are common in psychopathology research, often using diagnostic category as an independent variable. Continuous-variable analysis strategies drawing from a general linear model framework can be applied to such designs. The growing emphasis on dimensional examinations of psychological constructs, encouraged by the National Institute of Mental Health Research Domain Criteria framework, encourages continuous-variable analytic strategies. However, the interpretative implications of applying these strategies to various types of populations and sample score distributions, including those used in EGDs, are not always recognized. Appropriateness and utility of EGDs depend in part on whether the goal is to determine whether a relationship exists between 2 variables or to determine its strength. Whereas the literature investigating EGDs has emphasized symmetrical thresholds for defining extreme groups (e.g., bottom 10% vs. top 10%), psychopathologists often employ asymmetric thresholds (e.g., above a diagnostic threshold vs. a broader range of scores in a healthy comparison group). The present article selectively reviews literature on EGDs and extends it with simulations of symmetric and asymmetric selection criteria. Results indicate that including a wide range of scores in EGDs substantially mitigates problems (e.g., inflation of effect size) that arise when using statistical methods classically employed for continuous variables.