Discriminant content validity: A quantitative methodology for assessing content of theory-based measures, with illustrative applications

Objectives

In studies involving theoretical constructs, it is important that measures have good content validity and that there is not contamination of measures by content from other constructs. While reliability and construct validity are routinely reported, to date, there has not been a satisfactory, transparent, and systematic method of assessing and reporting content validity. In this paper, we describe a methodology of discriminant content validity (DCV) and illustrate its application in three studies.

Methods

Discriminant content validity involves six steps: construct definition, item selection, judge identification, judgement format, single-sample test of content validity, and assessment of discriminant items. In three studies, these steps were applied to a measure of illness perceptions (IPQ-R) and control cognitions.

Results

The IPQ-R performed well with most items being purely related to their target construct, although timeline and consequences had small problems. By contrast, the study of control cognitions identified problems in measuring constructs independently. In the final study, direct estimation response formats for theory of planned behaviour constructs were found to have as good DCV as Likert format.

Conclusions

The DCV method allowed quantitative assessment of each item and can therefore inform the content validity of the measures assessed. The methods can be applied to assess content validity before or after collecting data to select the appropriate items to measure theoretical constructs. Further, the data reported for each item in AppendixS1 can be used in item or measure selection.