Q Methodology

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Q methodology encompasses an entire philosophical approach to the study of subjectivity and more specifically, intersubjectivity. It is an interactive and reflective process incorporating both quantitative and qualitative methods to provide a robust method of studying individuals' subjectivity. A Q study involves participants sorting a set of statements or pictures to make a map of their own opinion. Factor analysis enables the clustering of shared viewpoints, discourses or narratives on a given topic. Q method is sometimes used in mini-publics as a way of studying how participants' views change through deliberative participation.

Problems and Purpose

The aim of Q methodology is to study shared subjectivity. It is about identifying shared viewpoints. It is not about identifying groups of people, or about generalising to the wider population.


Q methodology was invented by William Stephenson in 1935. Stephenson held PhDs in both physics and psychology, and wanted a scientific method of studying human subjectivity. It is speculated that the name Q came from the method's juxtaposition with R. In R (survey style research), the researcher seeks a representative sample of participants and carefully selects the varables (questions or statements). In Q, the inverse is true. The participants themselves are variables, with the researcher looking for variation across individuals' views. The 'Q-set' (items that are sorted by the participant) are designed to represent as much as possible the range of views that exist on the topic at hand.

Participant Selection

When conducting a Q study, the researcher invites participants on the basis that they have a relevant and interesting viewpoint on the topic under investigation. The aim of the study is not to find out what the general population thinks about the topic, but to understand the range of shared viewpoints. Therefore, there is no particular benefit to having a random sample. Furthermore, a Q study requires some in-depth thinking and ranking of statements on a topic and if participants are not interested or involved in the topic, it is more difficult to complete and less significance can be attached to their sort.

Deliberation, Decisions, and Public Interaction

Q method has been used by deliberative democracy scholars to study preference change, before and after deliberation. Typically, participants in a deliberative process complete a Q study both before and after taking part in a process such as a mini-public. Niemeyer and Dryzek (2007) put forward Q methodology as a method of identifying 'meta-consensus' through a six-step process:

  1. Construct the 'concourse' - this term is used to describe the range of views that exist on a single topic. It's possible to use academic literature, media or interviews to do this.
  2. Construct the 'Q set' - a set of items (they can be statements or pictures) that are representative of the concourse. 
  3. Select participants - participants are selected strategically to ensure diversity of views within the sample. Q typically has small numbers of participants. Usually there are less participants than in the Q-set.
  4. Each participant completes a Q sort with the researcher. This involves sorting the items on a grid, usually in a quasi-normal distribution on a scale of positive to negative evaluation. It is possible to do this online, but in person gives the participant the opportunity to ask questions and discuss with the researcher.
  5. Factor analysis and rotation. A variety of software packages are available to do this. Most popular is PQMethod (Schmolck 2014), a free, Q-specific package. Factor analysis and rotation enables the clustering of the individual Q sorts. 
  6. Interpretation means bringing mathematical factors to life - into narrative, discourses or shared viewpoints. Interpretation requires close qualitative analysis and is supported by interviews from the sorting.

Q has been applied across many disciplines including education, politics, psychology, environmental sciences and organisational studies. 

Influence, Outcomes, and Effects

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Analysis and Lessons Learned

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