Abstract

Although the recommendation to include multiple indicators of a construct of interest is commonplace in research design texts (e.g., Cook & Campbell, 1979), investigators conducting basic and applied research on forgiveness have typically relied on a single measure of this construct. There are many reasons why forgiveness researchers may fail to use multimodal measurement. Certainly, inclusion of additional questionnaires or other measurement procedures poses a burden to participants as well as researchers. Perhaps investigators simply do not believe that a concomitant benefit to research validity will compensate for this additional burden. In this relatively new research area, selecting multiple indicators that overlap sufficiently to constitute measures of the same underlying construct, but not so much that they are essentially redundant, may pose a challenge. Finally, researchers may avoid including more than one measure of forgiveness because this augmentation to the research design creates challenges at the data analysis phase. Our goal in this chapter is to address each of these challenges. In the sections that follow, we review the rationale for preferring multimodal measurement, provide a conceptual framework to assist researchers and research consumers in evaluating forgiveness measures, and describe common models for data analysis using multiple measures, with illustration of their relevance to the forgiveness domain.