406 J. Montana et al.


These realist and relativist perspectives, respectively, are recognized as important operating distinctions in the environmental sciences, including conservation (Moon & Blackman, 2014). Sixthly, there are current debates about whether or not


engaging stakeholders during research, rather than at the end of research, is useful for conservation outcomes (Nel et al., 2016; Sutherland et al., 2017). As such, there probably is variation in the degree to which conservation researchers seek to engage in pragmatic collaboration with stakeholders or work independently in framing research questions and processes. In psychological terms, we considered the research


preferences of respondents to be factors that could be understood through individual measurements (response to questions) and thus statistically analysed. For each factor, we drafted 8–10 questions for inclusion in the question- naire. These were counterbalanced so that half represented endorsement of one extreme of the factor and the other half represented endorsement of its opposite. For example, ques- tions pertaining to the subject focus of research included statements indicating a focus on species and ecosystems (e.g. ‘In my research, I am primarily interested in the dy- namics of animals, plants and their ecosystems’) counterba- lanced with those indicating a focus on humans and their institutions (e.g. ‘The primary goal of my research is to understand humans and their institutions better’). This en- sured a balance of positive and negatively termed questions and resulted in the development of a list of 50 questions. This questionnaire was piloted with a small focus group


to test the extent to which the questions made sense and adequately addressed the construct of research preferences they were intended to measure, so that we could revise the questions if required. Participants involved in the focus group (n = 7) were selectively sampled from the University of Cambridge Conservation Research Institute to include a range of genders (men = 4, women = 3), disciplines (zoology = 4, geography = 2, plant sciences = 1) and world regions (Europe = 4, South America = 2, Africa = 1). The focus group session lasted for 1 hour, during which partici- pants were asked to complete the questionnaire and report any misunderstandings or reactions. We then made minor revisions to the balance and wording of questions. In March 2017 we posted the revised questionnaire


online, on the Qualtrics platform. The order of questions was randomized and demographic questions on age, gender, discipline, world region, and self-identification as a natural and/or social scientist were added. We did not adopt a fixed definition of these categories, but instead allowed respon- dents to self-identify. Participants were invited to complete the questionnaire between March and April 2017 by emails sent to the mailing lists of the attendees of the 2016 and 2017 Student Conferences on Conservation Science and within the University of Cambridge Conservation Research


Institute, and posters and fliers inviting responses were dis- tributed at the 2017 conference. The opportunity to enter a prize draw for a gift card was made available to all partici- pants. The responses provided the data through which the questionnairewas statistically validated using factor analysis.


Factor analysis


The statistical validation of the questionnaire was carried out through factor analysis, which modelled the inter- relationships between the questions in the questionnaire based on collected responses, to identify a smaller set of factors each representing a different research preference. Before conducting the exploratory factor analysis, we re- moved some questions (items in psychological terms): six because they correlated with one or fewer items, and one that correlated at 0.2 level with two items (indicating a weak relationship and an unlikely fit in a potential factor solution). Then exploratory factor analyses were performed with the principle axis factoring extraction method, with the oblique (direct oblimin) rotation. The screen plot showed an inflection at c. 6–8 factors, which corresponded with the a priori factor structure. Therefore, most analyses were set to extract 6–8 factors. Coefficients were suppressed below 0.3, as low coefficients would demonstrate too weak a rela- tionship between the item and the factor (Field, 2009). With these parameters, we chose a factor solution based on the criterion that all factors consisted of at least three variables that loaded above 0.3, while retaining as many of the original variables as possible. To arrive at this solution, items were removed if they consistently did not load onto any factors or if they cross-loaded evenly onto two or more factors. The final factor solution had a Kaiser- Meyer-Olkin measure of sampling adequacy of 0.622, demonstrating that the sample is acceptable, although the analysis would benefit from a larger sample size (Kaiser, 1974). Bartlett’s test of sphericity (χ2(595.03)= 171, P,0.001) indicated that correlations between items were sufficiently large for factor analysis (Field, 2009). Cronbach’s α test for reliability is included in the table of factor loadings. The final factor solution with six factors and 19 associated variables became the validated question- naire. We reviewed the factors and associated variables to check for meaningful relations, assigned names and devel- oped complementary pairs for communication of results.


Demographic analysis


To examine any differences between respondents that self- assigned into the categories of natural scientist, social scien- tist, and both natural and social scientist, we compared the mean scores of the factors in the validated questionnaire across these categories. Participants had been given the


Oryx, 2021, 55(3), 404–411 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S003060531900067X


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