Modeling fuzzy data in qualitative marketing research

In marketing, qualitative data are used in theory development to investigat e marketing phenomena in more depth. After qualitative data are collected, the judgment-based classification of items into categories is routinely use d to summarize and communicate the information contained in the data. In th is article, the authors provide marketing researchers with a method that (1 ) provides useful substantive information about the proportion and degree t o which items belong to several categories and (2) measures the classificat ion accuracy of the judges. The model is called the fuzzy latent class mode l (FLCM), because it extends Dillon and Mulani's (1984) latent class model by freeing it from the restrictive assumption that all items are crisp for a given categorization. Instead, FLCM allows for items to be either crisp o r fuzzy. Crisp items belong exclusively to one category, whereas fuzzy item s belong-in varying degree-to multiple categories. This relaxation in the a ssumption about the nature of qualitative data makes FLCM more widely appli cable: Qualitative data in marketing research are often fuzzy, because they involve open-ended descriptions of complex phenomena. The authors also pro pose a moment-based measure of overall data fuzziness that is bounded by 0 (completely crisp) and 1 (completely fuzzy).