Q-method is an interactive research technique that measures a person’s subjective view on a topic and looks for shared viewpoints among a group of respondents. It can efficiently capture the essence of how participants feel about a subject as collective voices, and at the same time find the subtle differences between people’s views and values.

Although social scientists have been using Q-method for decades, is not yet well-known and is still considered an emerging or innovative technique. Robust quantitative data and rich qualitative information can be obtained from a relatively small sample size.

Unlike open-ended interview techniques or surveys, participants express their views by sorting a set of cards in order of preference on a pyramid-shaped chart. Usually there are 40-60 cards, each with a statement on a topic, but images can also be used. Analysis of the data provides results as sets of factors that represent different perspectives shared by participants. Although many researchers avoid statistical processes, freely available Q-method software ensures that the analysis of data is quick to learn.

Simon Watts and Paul Stenner have published an excellent and readable guide called Doing Q-Methodological Research

Q-Method Opinion Domain Q-Method Participants

Most Q-method projects follow a standardized step-by-step process:

  1. Generation of the Q-Set

    • Statements are collected from a variety of sources (newspapers, literature, transcripts from interviews) that express subjective views about a topic

    • A representative set of statement cards is generated (usually 40-60)

  2. Data Collection

    • Participants are selected because their opinions are sought on a given topic. A relatively small number of participants (~30) can provide robust results.

    • A Q-sorting sheet is generated with a forced-choice preference pyramid that allows participants to rank the statements according to level of agreement with statement relative to the other statements.\

  3. Analysis and Interpretation of Data

    • Q-method software finds commonalities between the sorts, and generates factor arrays, which are ‘idealized’ or archetypical sorts

    • The idealized sorts are interpreted to better understand the viewpoint. Questionnaires may accompany the sorts to provide additional information that supports the interpretation process.

The main difference between this method and a conventional survey is that the statement rankings are relative to each other, rather than individually. You get a more nuanced view of your population by dividing them into 3 or 4 main areas of agreement - which is useful when you are trying to get a holistic view of how people see the problem you are trying to solve.

Christine has used Q-method in several projects: a statement-based study examining perspectives on energy and sustainability, two Visual Q-method projects that explored different aspects of environmental perception and preference, and a Visual Q-method study exploring children’s preferences for outdoor play activities.

For details about these projects, follow the links: