The effect of nonindependence on significance testing in dyadic research


  • This research was supported in part by National Science Foundation grants BNS 9008077 and DBS 9307949.I want to thank William Cook, Bruno Zumbo, Deborah Kashy, and the journal's reviewers and editors who generously provided comments on an earlier draft. I thank Charles Judd, who originally suggested to me the idea of a fail-safe correlation.

concerning this article should be addressed to David A. Kenny, Department of Psychology, University of Connecticut, Storrs, CT 06269-1020.


Relationship researchers regularly gather data from both members of the dyad, and these two scores are likely to be correlated. This nonindependence of observations can bias p values in significance testing if person is the unit in the statistical analysis. A method for determining how much bias results from dyadic interdependence is presented. Correction factors based on the degree of interdependence, design type, and the number of dyads are used to adjust the F statistic and its degrees of freedom to produce a corrected p value. Bias depends on the type of design and the degree of nonindependence, while the number of dyads in the study ordinarily has only a small effect on bias. Various strategies for controlling for nonindependence are briefly reviewed.