Reliability of Nominal Data Based on Qualitative Judgments

Most research related to the reliability and validity of marketing measures has focused on multi-item quantitative scales. In contrast, little attention has been given to the quality of nominal scale data developed from qualitative judgments. Judgment-based (“coded”) nominal scale data are important and frequently used in marketing research-for example, in analysis of consumer responses to open-ended survey questions, in cognitive response research, in meta-analysis, and in content analysis. The authors address opportunities and challenges involved in evaluating and improving the quality of judgment-based nominal scale data, with specific emphasis on the use of multiple judges. They review approaches commonly used in other disciplines, then develop a new index of reliability that is more appropriate for the type of interjudge data typically found in marketing studies. Data from a cognitive response experiment are used to illustrate the new index and compare it with other common measures. The authors conclude with suggestions on how to improve the design of studies that rely on judgment-coded data.