Adaptation of the Q-methodology for the characterization of a complex concept through a set of products: From the collection of the data to their analysis. (July 2018)

• Record Type: Journal Article
• Authors: Brard, Margot; Lê, Sébastien
• Is Part Of: Food quality and preference. Volume 67(2018)
• Page Start: 77
• Page End: 86
• Publication Date: 2018-07
• Subjects: Q-methodology -- Products -- Categorical -- Concept -- Sorting task -- External preference mapping; Food preferences -- Periodicals; Food -- Quality -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Préférences alimentaires -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Food industry and trade -- Quality control; Food preferences; Food -- Quality; Periodicals; 664
• DOI: 10.1016/j.foodqual.2017.06.010
• Languages: English
• ISSNs: 0950-3293

Abstract

Highlights: An adaptation of the Q-methodology is presented here. It can be seen as a categorical version of the Q-methodology used in psychology. It allows to characterize a complex concept through a set of products. It is based on a data collecting with two predefined categories representing the concept and its negation. Multiple Factor Analysis is a possible way to analyze the collected data. Abstract: An adaptation of the Q-methodology used in psychology to study human subjectivity is introduced as a methodology for characterizing a complex concept through a set of products. Firstly, the data collection step consists in choosing a set of products as exhaustive as possible. This set of products is then given to participants who are asked to sort the products into predefined categories: "products assessed as non-representative of the concept" and "products assessed as representative of the concept". At the end of the experiment, a product is characterized by two types of information: (1) the products it has been associated with by the participants, and (2) the number of times it has been associated with the concept by the participants. While the first type of information is similar to those provided by free sorting task data, the second type of information is specific to these data. Secondly, the product-oriented data analysis step consists in analyzing data by Multiple Factor Analysis (MFA) in order to understand the variability between the products in accordance with the way the data have been collected. MFA presents the ability to provide a representation of the products that takes into account the two types of information mentioned previously. Third and finally, the data visualization step consists in enhancing the representation of the products through a categorical version of the maps provided by external preference mapping. This methodology is illustrated through an experiment based on the concept of innovation through a set of perfumes.