Discriminant content validity: A quantitative methodology for assessing content of theory‐based measures, with illustrative applications. (15th March 2014)

• Record Type: Journal Article
• Authors: Johnston, Marie; Dixon, Diane; Hart, Jo; Glidewell, Liz; Schröder, Carin; Pollard, Beth
• Is Part Of: British journal of health psychology. Volume 19:Part 2(2014)
• Page Start: 240
• Page End: 257
• Publication Date: 2014-03-15
• Subjects: Clinical health psychology -- Periodicals; 613.019
• DOI: 10.1111/bjhp.12095
• Languages: English
• ISSNs: 1359-107X

Abstract

<abstract abstract-type="main" id="bjhp12095-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="bjhp12095-sec-0001" sec-type="section"> <title>Objectives</title> <p>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.</p> </sec> <sec id="bjhp12095-sec-0002" sec-type="section"> <title>Methods</title> <p>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.</p> </sec> <sec id="bjhp12095-sec-0003" sec-type="section"> <title>Results</title> <p>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.</p> </sec> <sec id="bjhp12095-sec-0004" sec-type="section"> <title>Conclusions</title> <p>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 Appendix S1 can be used in item or measure selection.</p> </sec> <sec id="bjhp12095-sec-0005" sec-type="section"> <p> <boxed-text content-type="box" id="bjhp12095-blkfxd-0001" position="anchor" orientation="portrait"> <label>Statement of contribution</label> <p> <italic> <bold>What is already known on this subject?</bold> </italic> </p> <p>There are agreed methods of assessing and reporting construct validity of measures of theoretical constructs, but not their content validity. Content validity is rarely reported in a systematic and transparent manner.</p> <p> <italic> <bold>What does this study add?</bold> </italic> </p> <p> <list id="bjhp12095-list-0001" list-type="bullet"> <list-item> <p>The paper proposes discriminant content validity (DCV), a systematic and transparent method of assessing and reporting whether items assess the intended theoretical construct and <italic>only</italic> that construct.</p> </list-item> <list-item> <p>In three studies, DCV was applied to measures of illness perceptions, control cognitions, and theory of planned behaviour response formats.</p> </list-item> <list-item> <p>Appendix S1 gives content validity indices for each item of each questionnaire investigated.</p> </list-item> <list-item> <p>Discriminant content validity is ideally applied while the measure is being developed, before using to measure the construct(s), but can also be applied after using a measure.</p> </list-item> </list> </p> </boxed-text> </p> </sec> </abstract>