When and how to use Q methodology to understand perspectives in conservation research (2024)

Stage 1 Research Design

The first step is to identify the topic that sets the scope for the study and the overall question to ask respondents (e.g., “Sort these opinions from most like what I think to least like what I think”). The next step is to collect a comprehensive list of items (statements) that suggest a subjective opinion about the research topic. The comprehensive list is a proxy for the ‘concourse’: the full spectrum of items reflecting all possible opinions around the topic of concern (which in theory would be infinite). Items may be drawn from several sources, such as written material (newspapers, scientific publications, etc.), interviews or expert consultation. It is important to document this process.

From the concourse, researchers select a representative sample of items (the Q‐set), which respondents will rank. Approaches to selecting this sample of items vary. For example, items may be categorized according to subtopics and a balanced number from all subtopics may be chosen (e.g., Jacobsen & Linnell 2016).

The aim of Q is to uncover the diversity of opinions, irrespective of whether they are predominant in the population. Consequently, the sample of respondents (the P‐sample) is usually a nonrandom selection of individuals, and the sampling strategy is primarily purposive (selected following criteria other than randomness). Ideally, for the purposive sampling, researchers would be broadly familiar with the stakeholders and their views beforehand, which facilitates selecting respondents with a variety of opinions. In some cases, snowball or convenience sampling is used (whereby respondents are chosen if a previous respondent mentions them or if they are easily available [Saumure & Given 2012]). Alternatively, respondents can be selected based on observable characteristics, such as socioeconomic or professional status. Because of the use of inverted FA, an unusual feature of Q is that the rule of thumb that larger sample sizes are better does not necessarily apply. Powerful Q results can be obtained with very small samples (e.g., Sandbrook etal. 2013).

Stage 2 Data Collection

Respondents are asked to rank the items and this ranking is called Q‐sort. They rank the items over a grid with columns. This grid usually represents a simplified bell‐shaped distribution (an example of such a grid is the triangular grid in the part of data collection’ in Fig. ​Fig.1),1), the kurtosis of which varies across studies (i.e., the proportion between width and height). Respondents follow a condition of instruction for the ranking (e.g., from most agreement to most disagreement). The instruction can refer, for example, to agreement with items, importance, acceptability, or closeness to respondent's beliefs. Items placed in the same column receive the same ranking score. Researchers choose whether they ask respondents to fit all items in the slots within the grid or to allow, in each column, more (or fewer) items than the predefined slots (forced and nonforced distribution, respectively). The process is usually face to face, but it can also be by post or online (using software such as Q‐assessor, FlashQ, Q‐sortware, or WebQ). It is common to collect qualitative data as part of the Q process, either through respondents articulating their thought process during the ranking or after they have completed the process. Commonly, respondents are asked to explain the rationale of ranking items in the most extreme columns. Qualitative data thus collected play an important role in the interpretation.

A Q‐sort represents the perspective of a single respondent, whereby the respondent expresses what items prompt their strongest subjective reactions (those ranked in the extreme columns) relative to other items. Respondents need to provide sufficient variability in their response, such that they reveal nuanced degrees of engagement with items. For example, responses suggesting they agree very much with half of the items and disagree with the rest must be avoided. A good strategy to ensure this variability is to use a grid with a sufficiently ample range of columns. In the example in Fig. ​Fig.1,1, this range takes all integers from ‐3 to 3, for instance. If nonforced distribution is preferred, the data collector should ensure that respondents do not cluster items in just a few columns and remind them that items should be ranked relative to one another rather than independently.

Piloting is an important stage in Q to ensure that statements are comprehensible for the respondents and to identify other unforeseen problems. Lessons learned from piloting can be used to revise the Q‐set or make adjustments to the Q‐sort process.

Stage 3 Analysis

All the Q‐sorts collected are compared and grouped by similarity. Each group is then summarized as a single perspective (full analytical process is explained in Brown [1980]). This comparison, grouping, and summarizing is done through multivariate data‐reduction techniques (such as principal components analysis [PCA] or FA). There are a number of dedicated software packages available to analyze Q data (e.g., PQMethod [Schmolck 2014] and qmethod for R [Zabala 2014]).

As in standard PCA and FA, the data are reduced to a few factors (or components, i.e., the perspective shared by each group). This reduction is done in two main steps: extraction and rotation. The main analytical decisions in Q are as follows: the number of groups (i.e., number of factors), the method to extract factors (PCA or centroid FA), and the method to rotate factors (further analytical decisions discussed in Zabala and Pascual [2016]).

Extracting the factors consists of summarizing all individual responses into a few representative responses. The criteria used to choose the number of factors to extract vary across studies (details in Watts and Stenner [2012]). Most studies consider two or more criteria. In addition to typical criteria used in conventional FA or PCA, in Q a preliminary interpretation of the resulting perspectives can also be used to decide the number of factors. For example, if a perspective does not seem realistic or is qualitatively similar to others, the researcher may extract fewer factors.

Next, rotating the factors is akin to changing the viewpoint from where results are observed, much like changing the range of a scale or applying a logarithm. This is done to obtain a clearer and more interpretable structure of the results (further information on rotation in Brown [1980]). Other features of the analysis that are specific to Q are the percentage of the data variability explained by the results and the flagging of responses (i.e., tagging them as most representative of a given factor, both explained in detail in Watts and Stenner [2012]).

Two key results describe each shared perspective (factor). One set of coefficients, the factor loadings, indicate the relation between each respondent and factor. Factor loadings can be interpreted similar to the way correlation coefficients are interpreted. Another set of values indicate the relation between each item and factor. These values can be either z scores or normalized scores (Fig. ​(Fig.1).1). The z scores are the weighted average of the scores that similar respondents gave to an item. The normalized scores are integer approximations of z scores (but not the rounded values [Zabala 2014]). These item scores indicate how a hypothetical person representing a group of similar respondents (the factor) would rank the items.

The z scores give more precision about how strongly engaged each perspective is with each item. They are also used to determine whether an item is a consensus (with similar z scores across factors) and whether the item distinguishes a factor (significantly different z score in a factor compared with the rest).

Stage 4 Interpretation

The interpretation of factors is based on a combination of the item scores, qualitative data (e.g., collected from respondents during the Q‐sort), and the researcher's understanding of the case and of respondent views. Both the z scores and the normalized scores indicate the ranking of items of a shared perspective. Items that are both distinctive and with the highest or lowest scores tend to be most useful for interpretation. When these items are identified, the interpreter evaluates why they are in that position. Consensus items indicate the common ground among all factors.

Each factor is usually given a meaningful label and described in detail. Although labels are not essential, they provide readers with a shorthand identification of what the perspective is about. Labels usually refer to the most distinguishing characteristic of the perspective, for example: “environmental stewards,” “production maximizers,” and “networking entrepreneurs” (Brodt etal. 2004). Labels can also be phrases, such as “separation of science and conservation” (e.g., Cairns 2012) or full sentences (e.g., Gruber 2011).

Studies using Q yield a clear output: a set of synthesized, shared perspectives. A detailed exposition of their differences and commonalities is usually discussed relative to subtopics of the research question. Disagreements may be as researchers expected but are sometimes surprising. These include cases in which the actual differences were less contentious than what was anticipated prior to the study (e.g., Visser etal. 2007; Mazur & Asah 2013). In some cases, general values were agreed but disagreement lay in the specifics of how to achieve shared goals (e.g., Cavanagh etal. 2016), or it was shown that further scientific research would not solve the existing social conflict (e.g., Neff & Larson 2014).

Considerations for Applying Q

There are some fundamental considerations when applying Q that researchers should be aware of. First, as with many social research techniques, it is important to consider the risk of creating bias through respondent–interviewer interactions. A particular challenge occurs when respondents ask for clarification of the meaning of key terms in the statements, given that the interpretation of these terms can be a relevant point of difference between perspectives. In such cases, researcher answers should be consistent and avoid leading respondents toward a particular view. Second, respondents can find the sorting process challenging. Some take a long time to make decisions (there may be long periods of silence while respondents cognitively process items and decide), and others can find the need to sort items relative to each other frustrating or confusing. In both cases, the researcher needs to be patient and supportive of the respondent. Third, the choice of items influences the research critically: how respondents understand the message, whether the perspectives cover the topic sufficiently, and whether results are easily interpretable. Because the selection of items can be heavily influenced by the researcher, some studies report researcher bias as a potential problem (Ockwell 2008; Kamal etal. 2014). Critical to minimizing this risk is to make the item selection process systematic, exhaustive, and transparent. It is also important to keep alert to the possibility that qualitative data collected during Q‐sorts reveal a relevant point of view absent from the Q‐set. Fourth, in selecting items, there is a trade‐off between inclusiveness and cognitive overloading of respondents. Not all possible viewpoints can be captured by a limited set of items (Bredin etal. 2015a). Yet researchers need to delineate a set of items that respondents can sort in a reasonable time, otherwise the effectiveness and consistency of responses may be questioned (Swaffield & Fairweather 1996). Finally, the nonrandom sampling approach means that results cannot be directly extrapolated to a wider population and that frequencies cannot be estimated. Other methods applied during follow‐up studies can be used to explore the prevalence of positions identified using Q in the wider population (Danielson 2009).

Overall, conducting a Q study is not easy. The various stages in the process take considerable time, and in our experience some researchers underestimate its complexity due to the relatively straightforward quantitative analysis involved.

Management Alternatives

One common use of Q is to elicit alternatives or solutions to conservation problems. These uses are at the stage of policy design, when measures have not yet been implemented and researchers aim to understand various options. For example, understanding the views of those who may be affected by a policy can help managers choose instruments adapted to each motivation (Nordhagen etal. 2017; Zabala etal. 2017).

Identifying views about management alternatives is also useful to understand power dynamics among decision makers. For example, in a study regarding the management of a protected area (Mattson etal. 2011), the management approaches identified using Q anticipated changes in the governing board. A few months after the study, the views of individuals who entered the board had aligned with the view predominating among other members of the board, whereas the only board member whose view was distinct according to the study resigned.

Critical Reflection

Because conservation is a value‐laden discipline, Q has been applied to critically reflect on the values that underpin the activity of conservation professionals and researchers (e.g., Sandbrook etal. 2011; Blanchard etal. 2016). Debates studied with Q include diverse perspectives on the value of market‐based policies in conservation (Holmes etal. 2016); interpretations of broad, contested, or ill‐defined concepts, such as ecosystem services (Fisher & Brown 2014; Bredin etal. 2015a); and the role of science in conservation (Bischof 2010).

In explicating perspectives that underlie conservation practice and research, Q informs understanding of how the relationship between facts and values is perceived by different groups of people and helps in the development of self‐awareness. These reflections are useful, for example, to clarify discourses about how policies should be shaped or to challenge prevailing assumptions about the existence of strongly positive or negative stances. As an example of the latter, Sandbrook et al. (2013) in their study of conservationist's views on market‐based approaches to conservation, found that few embrace or discard these instruments completely and that the main difference was in the emphasis given to their potential versus that given to their caveats.

Policy Appraisal

The Q methodology has been used to appraise current or prospective conservation policy and to explore whether and why a policy mechanism is or will be accepted. Example applications include the acceptability of wildlife‐monitoring systems (Chandran etal. 2015), of marine protected areas (Gall & Rodwell 2016), and of proposed land‐use changes (Swaffield & Fairweather 1996). Unexpected perceptions about proposed policies can be uncovered. For example, a study on perceived potential measures for fire management found that from four main possible approaches identified among respondents, only two were reflected in existing policy proposals (Ockwell 2008).

Conflict Resolution

Addressing conflict is a common endeavor in conservation, and Q is particularly useful for such a purpose. It has been used mainly in conflicts related to the management of large terrestrial wildlife (Mazur & Asah 2013; Bredin etal. 2015b). In these controversies, stakeholders disagree about the way forward, tension is high, and further scientific evidence may be insufficient to overcome frictions.

When a situation is intractable or gridlocked (Mazur & Asah 2013), Q helps researchers identify which stakeholders have the most conflicting views and “crystallize points of contention” (MacDonald etal. 2015), allowing actors with contested opinions to disagree openly and to concretize their arguments (Chandran etal. 2015). A more precise understanding of the common ground and of disagreements can be instrumental to mediate conflicts and find solutions. Also, Q can uncover new unexpected connections among topics, provide common vocabulary (MacDonald etal. 2015) that facilitates dialogue, and identify unexpected similarities in the views of seemingly opposing stakeholders.

The literature review search string (Appendix S1), a list of articles in the review (Appendix S2), and information extracted from each study for the review (Appendix S3) are available online. The authors are solely responsible for the content and functionality of these materials. Queries (other than absence of the material) should be directed to the corresponding author

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