Q Methodology: An Overview

Q Methodology is the scientific and systematic study of subjectivity towards a topic, issue, or question.

The term “subjectivity” may refer to:

• attitudes
• feelings
• opinions
• perspectives
• perceptions
• tastes
• thoughts
• values
• viewpoints

Q Methodology reveals, not only the different viewpoints around a topic, but also clusters of viewpoints. That is, a Q study can expose consensus – similarities in viewpoints – as well as conflict – differences in views.

These findings have wide applications in a variety of fields. Since it was first developed in the 1930s by William Stephenson, a physicist and psychologist, Q Methodology has been used in different areas of study.

What Makes Q Methodology Unique

Q Methodology differs from surveys and other scientific research methods in at least three ways:

1. Data Collection

At the heart of Q Methodology is a unique data collection method in which participants sort a set of statement cards (Q-set). Traditionally, these “statements” were made up of words. However, they may also consist of images and even video (made possible by web-based Q data collection).

The Q-set is drawn from a universe of various opinions, perspectives, or statements around the research topic. This universe of statements is known as the “concourse.” The researcher strives to collect as many of these statements as possible, from a variety of sources. These may include interviews, published works on the topic, popular media, and even social media.

When they have exhausted all the possible viewpoints, the researcher distills the concourse into a smaller number of statements. This subset of the concourse, also known as the Q-set, is made up of statements that are representative of the various perspectives on the topic. Each statement is placed on a card for sorting.

The participant then sorts the statements in two phases:

The first phase or pre-sorting involves arranging the statements in three piles. For example, the participant groups the statements according to which ones they agree with, disagree with, and feel neutral or uncertain about.

Next comes the actual sorting. In this phase, the participant must consider how strongly they feel about each statement. They place each one along a response grid or Q-sort structure, which the researcher provides.

The Q-sort structure is typically in the shape of a normal distribution or bell curve, with the majority of statements placed towards the middle or neutral area and fewer on either end. The distribution can be narrow or wide.

Because the participant ranks statements relative to each other, they’re compelled to arrange the statements within the prescribed Q-sort structure. The resulting Q-sort or arrangement of cards on grid is purely subjective, based on the respondent’s own feelings or opinions. There are no right or wrong answers.

This video gives a good overview of the Q Methodology process:

2. Analysis

In Q Methodology, data are analyzed through correlation and factor analysis. The sample or unit of analysis is each Q-sort. Whereas other research methodologies compare the responses of groups of people, Q Methodology compares all the collected Q-sorts with each other.

Pattern analysis reveals which Q-sorts tend to be similar. These Q-sorts are said to form a cluster of subjectivity, which represents a shared view. A number of shared views, also known as factors, will emerge. These factors reflect the different commonly-held viewpoints on the topic.

Factor analysis then reveals a composite or synthetic Q-sort for each factor. This is a way of arranging the statements that is most representative of that factor. Describing the composite Q-sort for each factor, supplemented with interviews of participants, results in rich and nuanced explanations of the shared views.

Data analysis in Q Methodology is reductive. The researcher begins with a large number of statements or viewpoints on the topic. In the end, this is reduced to just a few select viewpoints.

3. P Set Size and Selection

The research participants or P Set also distinguishes Q Methodology from other research methods.

In a Q study, more participants doesn’t necessarily mean better. In contrast, in a survey, a large number of respondents are necessary for the study and its findings to be considered conclusive. A Q study only requires enough participants as necessary to manifest the shared viewpoints. While there is no prescribed ideal number of participants, on average, 40-60 participants may be more than adequate.

The selection of participants in a Q Methodology study is intentional, purposeful, or strategic rather than random. That is, Q study researchers carefully choose participants. They look for participants who have opinions about the topic, feel strongly, and represent a variety of distinct subjectivities.

Watch the video below where Rachel Baker, a reader in health economics at the Yunus Centre for Social Business and Health at Glasgow Caledonian University, talks about the distinguishing features of Q Methodology:

Q Methodology in Action

Q Methodology is used, not only in psychology, but also in communications, political science, education, public health, governance, and other disciplines.

Findings from Q studies can inform public policies, marketing campaigns, political campaigns, and other efforts to influence people’s attitudes and behaviors.

Here are just a few studies that used Q Methodology as their primary method of investigation:

Bredin, Yennie K., John D.C. Linnell, Leandro Silveira, Natália M. Tôrres, Anah A. Jácomo, & Jon E. Swenson (2015). Institutional stakeholders’ views on jaguar conservation issues in central Brazil. Global Ecology and Conservation, 3, 814–823.

Gruenhagen, Jan Henrik, & Per Davidsson (2018, December). Returnee entrepreneurs: Do they all boost emerging economies? International Review of Entrepreneurship, 16(4), 455-488.

Huh, Sang-Moo, Ok-Ki Lee, & Woo-Je Kim (2018, September). A study on the audience perception types about the success factors of and music videos. Indian Journal of Public Health Research & Development, 9(9), 743-749.

McCauley, Karen, Phyllis Montgomery, Sharolyn Mossey, & Patricia Bailey (2015, November). Canadian community mental health workers’ perceived priorities for supportive housing services in northern and rural contexts. Health and Social Care in the Community, 23(6), 632-641.

Späth, Leonhard (2018). Large-scale photovoltaics? Yes please, but not like this! Insights on different perspectives underlying the trade-off between land use and renewable electricity development. Energy Policy, 122, 429-437.

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