Johnston Marie, Dixon Diane, Hart Jo, Glidewell Liz, Schröder Carin, Pollard Beth
Institute of Applied Health Sciences, University of Aberdeen, UK.
Br J Health Psychol. 2014 May;19(2):240-57. doi: 10.1111/bjhp.12095. Epub 2014 Mar 15.
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.
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.
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.
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. Statement of contribution What is already known on this subject? 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. What does this study add? The paper proposes discriminant content validity (DCV), a systematic and transparent method of assessing and reporting whether items assess the intended theoretical construct and only that construct. In three studies, DCV was applied to measures of illness perceptions, control cognitions, and theory of planned behaviour response formats. Appendix S1 gives content validity indices for each item of each questionnaire investigated. 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.
在涉及理论构想的研究中，重要的是测量方法具有良好的内容效度，且测量方法不会受到其他构想内容的污染。虽然可靠性和构想效度经常被报告，但迄今为止，尚未有一种令人满意、透明且系统的方法来评估和报告内容效度。在本文中，我们描述了一种判别内容效度（DCV）方法，并在三项研究中展示了其应用。
判别内容效度包括六个步骤：构想定义、项目选择、评判者识别、评判格式、内容效度的单样本检验以及判别项目的评估。在三项研究中，这些步骤应用于疾病认知测量量表（IPQ-R）和控制认知。
IPQ-R表现良好，大多数项目与目标构想高度相关，尽管时间线和后果方面存在一些小问题。相比之下，控制认知研究发现了独立测量构想时存在的问题。在最后一项研究中，发现计划行为理论构想的直接估计反应格式与李克特格式具有同样好的判别内容效度。
DCV方法允许对每个项目进行定量评估，因此可以为所评估测量方法的内容效度提供信息。该方法可用于在收集数据之前或之后评估内容效度，以选择合适的项目来测量理论构想。此外，附录S1中报告的每个项目的数据可用于项目或测量方法的选择。贡献声明关于该主题已知的内容有哪些？对于评估和报告理论构想测量方法的构想效度，已有公认的方法，但对于其内容效度却没有。内容效度很少以系统和透明的方式报告。本研究增加了什么？本文提出了判别内容效度（DCV），这是一种系统且透明的方法，用于评估和报告项目是否仅评估预期的理论构想。在三项研究中，DCV应用于疾病认知测量、控制认知以及计划行为理论反应格式。附录S1给出了所调查的每个问卷中每个项目的内容效度指标。判别内容效度理想情况下应在测量方法开发期间、用于测量构想之前应用，但也可在使用测量方法之后应用。
