A Primer on Q-Method and the Study of Technology

A Primer on Q-Method and the Study of Technology

Stéphanie Gauttier (Grenoble Ecole de Management, France)
Copyright: © 2021 |Pages: 11
DOI: 10.4018/978-1-7998-3479-3.ch120

Abstract

Qualitative methods are under-represented in the articles published by the main journals in Information Systems, which seem to privilege quantitative studies and statistical representativity of results, following the R logic. This chapter provides an in-depth description of Q-method and demonstrates how its use could be beneficial to studies of technology and could reinforce the transparency and validity of other qualitative methods. The focus of this chapter lies in explaining how Q-method works, so that readers are equipped to set up their own Q-studies. It is based on prior literature and ongoing reflections being held by Q-methodologists online.
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Background

Q-method was developed by the psychologist Stephenson (1935; 1953) as an approach to capture people’s operant subjective views of phenomena. Subjectivity is conceptualized as what ‘emanates from a particular vantage point' (Brown, 1993). Operancy refers to the fact that these views drive individuals' behaviors. It is a method suited to the identification of the drivers, barriers, and structuring elements of behaviors and experiences.

Q-method rests on two important pillars. One is theoretical and refers to concourse theory, the other is methodological and uses Q-sorting procedure and Q-factorial analysis (Gauzente, 2010). First, the concourse theory posits that meaning is dependent upon context and therefore not given in abstracto. The concourse can be defined as the volume of available statements on a topic and is ‘the common coinage of societies large and small, and is designed to cover everything from community gossip and public opinion to the esoteric discussions of scientists and philosophers’ (Brown, 1993). Meanings exist for each individual and vary depending on circumstances, but can also be shared with others, thus making interpersonal communication and interpretation possible.

Key Terms in this Chapter

Conditions of Instruction: Designates the perspective that participants are asked to sort the Q-set from.

Centroid: A factor extraction method which uses a specific form of communality estimation detailed in Brown (1980) .

Flagged on a Factor: Signifies that a Q-sort is representative of this factor view based upon factor loadings (correlations between the sorters and the factors). Other outputs from Q are based only on sorts flagged on a single factor.

Q-Sorting: The ranking of the assertions of the q-sample by participants (P-set) on the forced distribution matrix.

Q-Sample: The assertions chosen from the concourse to be submitted to participants for the q-sorting procedure.

P-Set: Those who proceed to the Q-sorting.

Varimax: Common orthogonal factor rotation method where the sum of the variances of the squared loadings (squared correlations between sorters and factors) is maximized.

Concourse: The volume of possible assertions, in any format, on a given topic.

PCA: A standard, mathematically unambiguous factor extraction procedure that can be found in standard textbooks on factor analysis.

Hand Rotation: Sometimes referred to as judgmental rotation or theoretical rotation, this factor rotation is graphical rather than mathematical. This method allows researchers to rotate based on theoretical considerations rather than statistical ones.

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