Forestry, Faculty of
DSpace
UBCV
Rivers, Andrea Elizabeth
Master of Science - MSc
University of British Columbia
There is an ongoing need for scientific information to inform natural resource policy decisions, and scientists are viewed as reliable sources of expertise and objectivity at the interface between science and policy. One important question at this interface is whether or not scientists should advocate policy outcomes, and if so, under what conditions? While advocacy can be advantageous to natural resource management policy formation, the risk of scientific credibility loss is often a major deterrent from scientist engagement in advocacy and policy decisions. This study draws upon the results of a web survey of professional foresters and biologists, academic scientists, and a sample of multi-disciplinary undergraduate students on support for advocacy, and issues of advocacy and credibility in natural resource management science. Responses from the four groups were analyzed to examine the influence of science values, environmental values, and socio-demographic characteristics on an individual’s support for advocacy by scientists. Another objective of the study was to explore the relationship between support for advocacy and perceptions of credibility. Science values and political affiliation were found to be significant influences on a respondent’s support for advocacy, consistent with previous research. No significant differences in support for advocacy were found between the four groups, however the results identify differences in the values and credibility perceptions of professional foresters, biologists, and academic scientists. Risks to credibility were observed to increase with higher levels of advocacy. The results suggest credibility of source and mode of communication as other influences on a respondent’s perception of credibility. Drawing on the results from both components of this study, expert advocacy receives broad support, and political advocacy poses considerable risks to the credibility of scientists. The results also reinforce a number of guidelines for credibility protection, and highlight avenues for future research.
PERCEPTIONS OF ADVOCACY AND CREDIBILITY IN POLICY-RELEVANT NATURAL RESOURCE SCIENCE by Andrea Elizabeth Rivers B.Sc. (Hons.), Acadia University, 2007 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2012 © Andrea Elizabeth Rivers, 2012 ii Abstract There is an ongoing need for scientific information to inform natural resource policy decisions, and scientists are viewed as reliable sources of expertise and objectivity at the interface between science and policy. One important question at this interface is whether or not scientists should advocate policy outcomes, and if so, under what conditions? While advocacy can be advantageous to natural resource management policy formation, the risk of scientific credibility loss is often a major deterrent from scientist engagement in advocacy and policy decisions. This study draws upon the results of a web survey of professional foresters and biologists, academic scientists, and a sample of multi-disciplinary undergraduate students on support for advocacy, and issues of advocacy and credibility in natural resource management science. Responses from the four groups were analyzed to examine the influence of science values, environmental values, and socio-demographic characteristics on an individual’s support for advocacy by scientists. Another objective of the study was to explore the relationship between support for advocacy and perceptions of credibility. Science values and political affiliation were found to be significant influences on a respondent’s support for advocacy, consistent with previous research. No significant differences in support for advocacy were found between the four groups, however the results identify differences in the values and credibility perceptions of professional foresters, biologists, and academic scientists. Risks to credibility were observed to increase with higher levels of advocacy. The results suggest credibility of source and mode of communication as other influences on a respondent’s perception of credibility. Drawing on the results from both components of this study, expert advocacy receives broad support, and political advocacy poses considerable risks to the credibility of scientists. The results also reinforce a number of guidelines for credibility protection, and highlight avenues for future research. iii Preface This research was approved by the University of British Columbia Behavioural Research Ethics Board. The certificate of approval is UBC BREB H11-00595. iv Table of Contents Abstract ................................................................................................................................................. ii Preface .................................................................................................................................................. iii Table of Contents ................................................................................................................................. iv List of Tables ......................................................................................................................................... v List of Figures ...................................................................................................................................... vi Acknowledgements ............................................................................................................................. vii Chapter 1: Introduction .................................................................................................................. 1 1.1 Overview .............................................................................................................................. 1 1.2 Advocacy and Credibility..................................................................................................... 2 1.3 Literature Review ................................................................................................................. 3 1.4 Models of Advocacy .......................................................................................................... 16 1.5 Case Studies ....................................................................................................................... 18 1.6 Research Questions and Hypotheses .................................................................................. 24 Chapter 2: Methods ....................................................................................................................... 31 2.1 Research Design ................................................................................................................. 31 2.2 Sampling ............................................................................................................................ 31 2.3 Survey Instrument Development ........................................................................................ 34 2.4 Measures ............................................................................................................................ 36 2.5 Data Analysis ..................................................................................................................... 41 2.6 Limitations ......................................................................................................................... 49 Chapter 3: Results .......................................................................................................................... 51 3.1 Sample Overview/ Demographics ...................................................................................... 51 3.2 Support for Advocacy vs. Value Orientations/Demographics ........................................... 55 3.3 Credibility .......................................................................................................................... 93 Chapter 4: Discussion and Conclusions ....................................................................................... 99 4.1 Support for Advocacy Influences ....................................................................................... 99 4.2 Perceptions of Credibility................................................................................................. 104 4.3 Future Research ................................................................................................................ 108 4.4 Conclusions ...................................................................................................................... 109 References ......................................................................................................................................... 111 Appendix A - Questionnaire ............................................................................................................ 116 Appendix B – Supplementary Tables ............................................................................................. 122 v List of Tables Table 1.1 Categories of scientist involvement in natural resource policy. ............................................. 3 Table 1.2 Models of Advocacy derived for survey instrument. ........................................................... 18 Table 2.1 Science Value Index. ............................................................................................................ 37 Table 2.2 Environmental value index (NEP). ....................................................................................... 38 Table 2.3 Hypothetical perceived credibility scenarios. ....................................................................... 40 Table 2.4 Response rates (by sample group). ....................................................................................... 42 Table 2.5 Student sample response rates (by department). .................................................................. 42 Table 2.6 Science value scale construction. ......................................................................................... 46 Table 2.7 Environmental value (NEP) scale construction. ................................................................... 48 Table 3.1 UBC Faculty Member departments. ..................................................................................... 53 Table 3.2 Student sample departments. ................................................................................................ 55 Table 3.3 Science value item frequencies (%) and descriptive statistics ............................................. 57 Table 3.4 Science value scale scores (descriptive statistics). ............................................................... 60 Table 3.5 NEP item frequencies (%) and descriptive statistics. ........................................................... 62 Table 3.6 Environmental value (NEP) index scores. ............................................................................ 64 Table 3.7. Support for advocacy: frequencies (%) and descriptive statistics. ...................................... 65 Table 3.8 Support for advocacy vs. science value scores. .................................................................... 67 Table 3.9 Support for advocacy vs. science value scores by sample source. ....................................... 67 Table 3.10 Support for advocacy vs. NEP scores. ................................................................................ 68 Table 3.11. Support for advocacy vs. NEP scores by sample source. .................................................. 69 Table 3.12. Support for advocacy vs. age. ............................................................................................ 70 Table 3.13 Support for advocacy vs. gender. ....................................................................................... 70 Table 3.14. Support for advocacy vs. education. .................................................................................. 71 Table 3.15. Support for advocacy vs. education (APB sample). .......................................................... 72 Table 3.16 Support for advocacy vs. political affiliation. .................................................................... 72 Table 3.17. Support for advocacy vs. political affiliation (faculty sample). ........................................ 73 Table 3.18. Support for advocacy vs. faculty (faculty sample). ........................................................... 74 Table 3.19 Support for advocacy: Faculty of Forestry member frequencies by department. ............... 75 Table 3.20 Support for advocacy: Faculty of Science member frequencies by department................. 76 Table 3.21 Support for Advocacy: “other” faculty members by department. ...................................... 76 Table 3.22 Bivariate summary: support for advocacy. ......................................................................... 77 Table 3.23. Bivariate summary: support for advocacy (APB sample). ................................................ 78 Table 3.24. Bivariate summary: support for advocacy (faculty sample). ............................................. 79 Table 3.25. Bivariate summary: support for advocacy (student sample). ............................................ 79 Table 3.26. Support for advocacy ordinal regression models (ABCFP sample). ................................. 82 Table 3.27. Support for advocacy ordinal regression model (APB sample). ....................................... 84 Table 3.28. Support for advocacy ordinal regression models (faculty sample). .................................. 85 Table 3.29. Support for advocacy ordinal regression models (student sample). .................................. 87 Table 3.30. Support for advocacy ordinal regression models (complete sample). ............................... 89 Table 3.31 Support for advocacy ordinal regression summary. ........................................................... 91 Table 3.32 Perceived credibility frequencies (%) and descriptive statistics . ....................................... 94 Table 3.33 Credibility of Source frequencies (%) and descriptive statistics. ....................................... 97 Table 4.1 Support for advocacy: summary of main findings (complete sample). .............................. 100 Table 4.2 Support for advocacy: summary of main findings (by sample). ........................................ 101 vi List of Figures Figure 1.1 Advocacy model continuum. .............................................................................................. 30 Figure 3.1 Support for advocacy vs. science values. ............................................................................ 92 Figure 3.2 Support for advocacy vs. NEP scores. ................................................................................ 93 Figure 3.3 Perceived credibility vs. support for advocacy by sample source. ...................................... 96 vii Acknowledgements I would like to thank my supervisor, Dr. George Hoberg, for his guidance and support throughout my graduate student career. It has been a pleasure to work with him, and I took forward to future collaboration. I would also like to extend a big thanks to fellow forestry graduate students (past and present) who have become my good friends, greatly contributing to my graduate experience. Thank you all for your ongoing support. My committee members, Drs. Tindall and Harshaw, have been instrumental in the development of this thesis. Thank you to Dr. Tindall for his instruction in social science research methods, and his assistance in development of the survey instrument. Dr. Howie Harshaw played in an integral role in guiding and advising me throughout many aspects of this project from sampling and data collection, to statistical analysis, and beyond. Thank you Howie for always taking the time to help, especially through my struggles with multivariate statistics. I would also like to thank all of the faculty members, foresters, biologists, and students who took the time to fill out my questionnaire. Additional thanks to the Faculty of Forestry for providing financial support through their internal awards program. Data analysis was one of the most challenging components of this thesis. In addition to Howie’s tremendous support in this area, I would like to extend further thanks to Andy Field for writing a humorous and engaging book on statistics for social sciences, this made my statistics experience much more tolerable and (almost) fun, and to CBC radio for keeping me company during long SPSS sessions. Finally, I would like to thank my amazing family and friends from the east coast, the west coast, and everywhere in between. Special thanks go out to my parents, and to my favorite Prince Edward Island ‘spud picker’ for making a huge left coast transition and becoming a constant source of support and encouragement. Much love. 1 Chapter 1: Introduction 1.1 Overview The ongoing demand for scientific information in policy development has research scientists frequently being called upon as sources of expertise and objectivity in the policy process. Science is widely viewed as a reliable source of information about the natural world and of environmental impacts of human activities. Natural resource management policy issues involve scientific complexities, and a relative degree of uncertainty. Therefore, scientists and the knowledge that they provide can offer valuable contributions at the interface between science and policy by informing decision-making processes, and subsequent regulatory decisions. The role of scientists at the science-policy interface can be challenging. While science is not the sole determinant of policy outcomes, the management of forests, and the extraction of such resources as oil, gas, and minerals requires that policy-makers be informed by sound scientific information to mitigate impacts on water, air, land, climate, and biodiversity. One of the most difficult dilemmas at this interface is whether or not scientists should be advocates of certain positions with respect to natural resource management, and if so, under what conditions? Advocacy can be advantageous to public policy outcomes, but can also compromise research results and be detrimental to a scientist’s reputation. While there are many arguments for and against advocacy by scientists, a predominant deterrent of scientists advocating specific policy preferences, and a fear of many who do, is the risk of losing their scientific credibility. Given the politically charged, often controversial nature of many resource management policy issues, the perceptions of the science community, and general public, can play a significant role in the policy outcome. A loss of credibility in the eyes of the public and/or science community can compromise perceptions of a given scientist, their field of research, or the authority of science as a whole, and consequently, jeopardize research results and policy formation. 2 This study explores perceptions of advocacy and credibility in natural resource management science from the perspective of the science community, resource professionals, and the general public. It is based on results from an online survey distributed to professional foresters, professional biologists, academic scientists, and undergraduate university students in 2011. This research examines the influences of socio-demographic characteristics, science values and environmental attitudes, on an individual’s perceptions of advocacy and credibility with respect to resource management science. It also seeks to determine if the degree of a scientist’s advocacy can affect the perception of their credibility by fellow scientists and the public. This introductory chapter provides an overview of the study’s objectives and definitions for research concepts and is followed by a review of relevant published literature, models of advocacy and two applied case studies. The chapter concludes with the study’s research questions and hypotheses. Chapter 2 documents the methodology of this study, including survey instrument design, sampling, data collection, and analysis. Chapter 3 details the study’s results, and Chapter 4 provides a comprehensive discussion of results, implications for future research, and conclusions. 1.2 Advocacy and Credibility Advocacy is defined in the Oxford Dictionary (2012a) as “public support for, or recommendation of a particular cause or policy”. More specific to the reporting of research in the natural resource sciences, Lackey (2007) defines policy advocacy as “support for a particular policy or class of policies” (13). Credibility, and loss thereof, deters many scientists from participating in the policy process (Blockstein 2002; Lach et al. 2003). Credibility is defined as “the quality of being trusted and believed in” (Oxford Dictionary 2012b). 3 1.3 Literature Review A review of relevant published literature identifies a variety of roles for scientists at the science-policy interface, research investigating the presence of and support for advocacy within the science community, and a series of previous studies gauging the influence of personal values and demographic attributes on an individual’s support for advocacy. Additionally, the available literature concerning issues of scientific credibility in natural resource policy is presented including the relevance of credibility within the scientific community and the public sphere, factors that influence credibility, and a collection of scholarly guidelines for credibility maintenance at the science-policy interface. 1.3.1 Roles of Scientists in Natural Resource Policy There is widespread agreement in the literature that scientists play an important role in communicating scientific information to help resolve important resource policy questions (Lackey 2007; Mills 2000; Mills and Clark 2001). As Lach et al. (2003) explain: Managing natural resources involves analyzing complex biophysical, ecological, and social systems in which the collection and interpretation of substantial amounts of technical and scientific information are critical (171). However, there is less agreement about precisely what role scientists should play. Steel et al. (2001, 2004, 2009, 2010) and Lach et al. (2003) derived five possible categories of scientist involvement from interviews and exploratory surveys: report, interpret, integrate, advocate, and make decisions. The descriptions of each category range from minimal to dominant roles: Table 1.1 Categories of scientist involvement in natural resource policy (Lach et al. 2003, 74). Category Description Report Reporting scientific results that others use in making decisions on natural resource management issues Interpret Reporting and then interpreting scientific results for others who are involved in natural resource management decisions Integrate Working closely with managers and others in integrating scientific results into management decisions Advocate Actively advocating for specific and preferred natural resource management decisions Make Decisions Making decisions about natural resource management 4 Similarly, Blockstein (2002) has created a ‘data to advocacy’ continuum consisting of five categories of scientist involvement in the political process: data, interpretation, advice, counsel, and advocacy. Scott et al. (2007) take a different approach to categorizing the roles of scientists in natural resource policy by looking at behavioural traits and constructing a continuum of policy advocacy that identifies examples of actions research scientists might take and classifies them as policy advocacy, not policy advocacy, or a grey area in between. “Stipulating preferred policy decisions”, “supporting a class of policies based on general beliefs or values”, and “lobbying for specific policies or management outcomes” are examples of actions that can be classified as policy advocacy. Actions such as “using language and words in ways that can be interpreted differently by different groups or stakeholders”, and “sharing research results with one or a limited range of special interest groups” are less distinguishable and may or may not constitute policy advocacy (Scott et al. 2007, 34). Lackey (2007) argues that policy advocacy can be “active, covert, or inadvertent” and declares the presence of value-laden language in scientific writing as “stealth policy advocacy”. Advocacy can take a variety of forms. Brussard and Tull (2007) define ‘professional advocacy’ as “informing policy makers, managers, and the public about issues that arise in one’s area of expertise” (21). Mills (2000) details the concept of ‘position advocacy’ which is said to occur when a scientist uses their public platform and scientific standing to advocate on behalf of personal values that could be falsely perceived as scientific information. Similarly, Pielke (2002) speaks of ‘political advocacy’ as the association of scientific findings with ideological or political values. 1.3.2 Advocacy There are significant disagreements in the literature about whether advocacy by scientists is appropriate. Many scientists believe that there is a line that should not be crossed on the ‘data to advocacy’ continuum, but disagree on where it should be drawn (Blockstein 2002). 5 One common view is that scientists have ethical and moral obligations to act as advocates, as their expertise can help resolve important societal issues (Lubchenco 1998). Within the natural resource disciplines, professional societies are thought to be one of the best channels through which to provide scientific information to policy makers (Blockstein 2002; Scott, Rachlow, and Lackey 2008). Pro-advocacy views are especially prevalent in the field of conservation biology (Foote, Krogman, and Spence 2009; Scott, Rachlow, and Lackey 2008; Scott et al. 2007). Advocacy is regarded by many biologists as a professional obligation, with conservation biologists often being encouraged to advocate for science, biodiversity, ecosystem services, and the natural world (Brussard and Tull 2007; Blockstein 2002). There is an element of advocacy in the mandate of many professional associations. For example, The Association of British Columbia Forest Professionals includes “advocating for good forest stewardship” as part of their directive (ABCFP 2012). The British Columbia Association of Professional Biologists lists “to encourage the development and application of sound biological principles in the management and conservation of resources,” and “to inform and advise industry, governments, and the public of the potential impacts on biological resources of land use and resource development proposals” as part of the association’s mission (APB 2012). It has also been argued that academic scientists are well situated to advocate on behalf of environmental issues. As Foote et al. (2009) suggest: “academia fosters a wide-ranging tolerance and open discussion of perspectives, providing more opportunity for advocacy than most other work settings” (580). However, Foote et al. (2009) also note that a professor’s willingness to advocate can be subject to disciplinary differences in funding sources. Advocacy can also be found in peer-reviewed scientific publications. Scott et al. (2007) reviewed research papers in six major resource science journals to evaluate the presence of policy advocacy, as indicated by normative or value-laden language, or stipulation of preferred policy and/or management preferences. They observed these indicators of advocacy were frequently occurring in journals of the natural resource field, especially 6 Conservation Biology. Furthermore, Scott et al. (2007) surveyed conservation biologists for their opinions about this issue. Most respondents (70%) agreed that publications in scientific journals should advocate policy preferences, however many included stipulations such as “only when the preferences are supported by the science, and only if the policy preference is clearly separated from the science” (Scott et al. 2007, 33). Lach et al. (2003) surveyed four groups in the U.S. Pacific North-west (university and federal agency scientists, managers of state and federal natural resource programs, members of public interest groups and organizations, and the attentive public 1 ) regarding preferred roles for scientist involvement by employing five categories of involvement (report, interpret, integrate, advocate, make decisions). They found all groups to be favorable towards the ‘interpretive’ and ‘integrative’ categories. Scientists on average preferred the ‘interpretive’ role most, while the majority of the other three groups favored the ‘integrative’ role. Members of interest groups and the attentive public were more likely to support an advocacy role and less likely to support the minimalist reporting role. The majority of respondents from all groups were less supportive of the fifth role “scientists making decisions themselves” (Lach et al. 2003). A similar study was done to investigate attitudes toward advocacy in the marine conservation community. Gray and Campbell (2009) employed the same categories, but within the context of marine protected areas. Surveying ecologists and other stakeholders at an International Marine Conservation Congress, and separating them by source of employment (academic, government, NGO, other), Grey and Campbell (2009) found there was almost unanimous support for the ‘integrate’ category and moderate support for the ‘interpret’ and ‘advocate’ categories. There was lower support for the ‘report’ and ‘make decisions’ categories at the opposite ends of the spectrum. No significant differences were reported between groups. 1 The “attentive public”, as defined by Lach et al. (2003) and Steel et al. (2001), are individuals that identify themselves as being aware of or involved in natural resource problems. The attentive public has been found to have higher environmental concern, knowledge of the scientific method/ use of science in society (Lach et al. 2003; Steel et al. 2001). 7 On the other side of the advocacy debate, it has been argued that the participation of scientists in controversial policy-relevant issues can challenge their authority as experts (Bocking 2004). Lach et al.’s (2003) findings, as discussed above, were that the majority of scientists surveyed preferred a less involved ‘interpretive’ role in comparison to resource managers, interest groups and the attentive public who preferred the more involved ‘integrate’ model. Employing the same categories for analysis, Steel et al. (2004) had similar results, suggesting that many scientists are hesitant to support an advocacy role. Interviews with ecological scientists conducted by Lach et al. (2003) identified several reasons for this reluctance: - Amount of time advocacy can take away from their professional responsibilities - Lack of skill in the non-scientific arena - Concern that their credibility as scientists will be challenged by colleagues, politicians, and representatives of interest groups (171) The inherent fear that a scientist’s personal policy preferences will color their science is pervasive at the science-policy interface (Lackey 2007). Mills (2000) acknowledges that scientists can make important contributions to policy but views advocacy for specific policy outcomes an “unethical misrepresentation of personal values as if they were science information” (168). While resource science is often associated with judgments regarding value and purpose (Bocking 2004), Mills (2000) argues: At issue is not whether scientists have personal values. Rather, it is whether they should express those values by advocating for any particular decision outcome during a public debate in which they are being counted on to be a neutral provider of scientific information (166). Currently, there is no clear consensus among the science community on whether or not advocacy is appropriate, and to what extent. While advocacy can be encouraged within professional societies and the academic community, many scientists are hesitant to engage in advocacy roles at the science-policy interface. 1.3.3 Influence of personal values/ demographic attributes The literature on advocacy in resource science connects support for advocacy to underlying personal values towards science and environmental concern. Additionally, 8 demographic attributes such as age, gender, level of education, general political affiliation, and source of employment/ area of study have also been found to influence an individual’s support for advocacy. 1.3.3.1 Science Values Steel et al. (2001, 2004) hypothesize that preferences for the role of scientists in the policy process are likely influenced by personal attitudes towards science. Steel et al. (2001) used objectivity as a measure of science values and found that respondents who believed scientists were more objective than other actors in natural resource management disagreed with the minimalist reporting category, showing support for the more involved models. A different approach, taken by Steel et al. (2004), examined attitudes about science and the scientific process associated with a ‘traditional’ or ‘positivist’ view of science and how they connect with preferred roles for scientists in resource management. In a comparison of scientists and the attentive public, the public was found to be the most receptive of positivistic views toward science and the scientific method. The public’s positivistic orientations were correlated with strong support for scientist involvement in the policy process. Scientists, on the other hand, were surprisingly less accepting of positivism and negatively correlated with the “integrate” model, suggesting skepticism regarding their role in resource policy (Steel et al. 2004). Similarly, Gray and Campbell (2009) evaluated respondent’s “positivist” views of science using a similar index. Government-employed marine conservation scientists and stakeholders with positivist orientations were found to have greater support for the more involved categories ‘advocate’ and ‘make decisions’ (Gray and Campbell 2009). 9 1.3.3.2 Environmental Values The New Ecological Paradigm (NEP) scale has become an extensively used measure of environmental value orientations (Dunlap 2008; Dunlap et al. 2000). 2 The index measures an individual’s level of biocentrism and environmental concern by taking into account five facets of a pro-ecological worldview: limits to growth, the fragility of nature’s balance, the possibility of an eco-crisis, anti-anthropocentrism, and rejection of exemptionalism (Dunlap et al. 2000). There is a wide-ranging body of past research that has explored the relationships between NEP scores and various socio-demographic attributes. Using the NEP scale as an indicator of biocentric values and environmental concern, Steel et al. (2004) found respondents with higher NEP scores (ie: more biocentric) were significantly less supportive of scientists merely reporting research, and very supportive of the interpret, integrate, advocate, and make decisions models. 1.3.3.3 Age In a survey of members of the Canadian and American publics (Washington, Oregon, and British Columbia), Steel et al. (2001) found younger respondents were less likely to support the most dominant ‘make decisions’ role for scientists in natural resource policy. No statistically significant effects were observed between age and the other four roles (report, interpret, integrate, advocate). The relationship between age and environmental concern is also relevant given the findings of Steel et al. (2004), as detailed in section 1.3.3.2, that there is a correlation between NEP scores and support for more scientist involvement in policy decisions. The work of Inglehart (1989) suggests that younger people have more concern for environmental issues as they have a greater association with post-materialist values. 3 Similarly, Jones and Dunlap (2004) found younger respondents to score higher on the NEP scale. However, Blake et al. (1997) found middle-aged respondents to have higher levels of environmental concern (as measured by NEP scores), than younger cohorts. 2 Originally derived by Dunlap and Van Liere (1978) as the “New Environmental Paradigm”, it was later updated at re-titled as the “New Ecological Paradigm” (Dunlap et al. 2000). 3 Post-materialist values are those emphasizing self expression and quality of life, in contrast with materialist values which are dominated by physical and economic security (Inglehart 1989). 10 Although the findings with regard to age are conflicting, Steel et al.’s findings are the most pertinent to this study as they apply directly to the relationship between preferred roles for scientists and age. 1.3.3.4 Gender In Western studies, women have been shown to be significantly more concerned about environmental issues than men (Blake, Guppy, and Urmetzer 1997; Milbrath 1984). Although the findings of Steel et al. (2004) would suggest higher environmental concern to be correlated with more support for scientist involvement, the general public contingent of Steel et al. (2001) found women were more likely than men to support the ‘interpret’ role for scientists in resource policy and less likely to support the more dominant ‘advocate’ and ‘make decisions’ roles. However, gender was found to have less influence on the personal attitudes of scientists and managers towards science and preferred roles of scientists in natural resource policy as it did within the general public (Steel, Warner, and Lach 2010). 1.3.3.5 Education Results from Steel et al.’s (2001) study of Canadian and American public indicates respondents with higher levels of educational attainment would prefer to see scientists go beyond the minimalist reporting role and show support for the interpret, integrate, and advocate roles. With regard to pro-environmental orientation, the past work of Inglehart (1989) and Jones and Dunlap (1992) suggests that respondents with a higher education will show more concern for environmental issues; however, Blake et al. (1997) found no significant difference in environmental concern between various levels of education. 1.3.3.6 Political Affiliation Steel et al. (2001) found respondents on the far right (ie: conservative) of the political spectrum were more likely to support a minimalist role for scientists while the far left (ie: liberal, progressive) do not support such a role, giving preference to the integrate and advocate models. Respondents from the far left and right of the political spectrum both 11 disagree with the most involved model that involves scientists making decisions themselves (Steel et al. 2001). Supporters of left leaning provincial political parties in British Columbia were identified as having higher levels of environmental concern as captured by NEP scores (Blake, Guppy, and Urmetzer 1996), which further enforces the idea that individuals expressing environmental concern are likely to support a more involved role for scientists in policy development. More generally, higher NEP scores have been associated with political liberalism (Jones and Dunlap 1992). 1.3.3.7 Source of Employment/ Field of Research Generally, individuals employed by, or connected to, primary industries did not support the integration of scientists into management, or advocacy on behalf of scientists with regard to the management of resources (‘advocate’ category) (Steel et al. 2001). Steel et al. (2001) cited the perceived adverse economic impacts of the American and Canadian government’s “sustainable resource management policies” as the rationale for these findings. Similarly, in terms of environmental concern, individuals employed by the forest industry were found to have lower NEP scores in a study of the general public (Tindall, Harshaw, and Sheppard 2010). Tindall et al. (2010) also reported a “social influence effect” suggesting that individuals with close social ties to individuals employed by the forest industry share similar values, such as satisfaction with public participation in forest management practices. Field of research can also influence perceptions advocacy, as formal training and organizational cultures differ between scientific fields (eg. physical vs. natural sciences) (Barke and Jenkins-Smith 1993). A study of environmental risk perception and policy preferences in the context of nuclear energy and waste compared patterns of belief among scientists and engineers from different sources of employment and fields of research (Barke and Jenkins-Smith 1993). Life scientists (biological fields) were found to perceive 12 the greatest risks, perceive higher overall environmental risks, and prefer stronger environmental management requirements in contrast to physical scientists (physicists, chemists, engineers) (Barke and Jenkins-Smith 1993). Life scientists were also more likely to support calls for government action (Barke and Jenkins-Smith 1993). Foote et al. (2009) suggest that in academia, willingness to advocate can be influenced by disciplinary differences in funding sources. Research in many scientific areas such as medicine and natural science is heavily reliant on funding agencies. Competing for research grants can reduce the amount of time available for advocacy (Foote et al. 2009). Additionally, academic research in many scientific fields such as forestry can be funded by industry, which may diminish a scientist’s willingness to advocate. 1.3.4 Credibility The risk of the loss of scientific credibility is a major deterrent to many scientists from participating in the policy process (Lach et al. 2003). The politicization of science is one reason why credibility loss is feared as a consequence of advocacy. Contested policy issues prod areas on the frontiers of knowledge where scientific consensus is fragile and uncertainty is high (Jasanoff 1987). Environmental issues are, more often than not, fraught with political controversy (Bocking 2004; Lackey 2007). This creates problems at the science-policy interface as more and more scientists are perceived by the science community and/or general public to be advocating specific findings in alignment with their ideological and political views (Lackey 2007; Pielke 2004). Expertise is also a political resource, so politicians and interest groups use science and scientists as part of their political strategy. Mills and Clark (2001) write: Science applications to natural resource issues are usually done in the glare of public conflict and controversy. Discrediting the science and even the scientist is a strategy sometimes used by antagonists on both sides of the issue (191). Pielke (2004) argues that the politicization of science is embedded in an overly simplistic ‘linear model’ where: “battles take place over whether the science is ‘sound’ or ‘junk’ instead of debating the value or practicality of specific policy outcomes” (409). 13 Politicized science creates risks for both the political process and the role of science (Lackey 2007; Mills 2000). As Bocking (2004) suggests: “if [the] science comes to be viewed as merely the expression of political preferences then its methods, results, and interpretations will all be open to question” (26). There is widespread concern that a loss of scientific credibility can compromise the utilization of research results (Scott et al. 2007; Lackey 2007; Bocking 2004; T.J. Mills 2000), and jeopardize important contributions science can offer to policy decisions (Pielke 2007). Credibility is critically important within the science community. According to Lach et al. (2003), “Scientists perceive scientific credibility to be a basic requirement for privileging their interpretation of research data over less favorable interpretations by other parties”(174). In the science community, scientific credibility is fostered by peer review. The practice of peer review, which can be traced back to the early scientific journals in the 17 th Century, calls on fellow scientists to evaluate the quality and objectivity of their peer’s research findings by asking such questions as: “Does the research meet accepted standards of objectivity, completeness, reproducibility, accuracy, and precision? Are the data, methods, arguments and other components sound and reliable?” (Bocking 2004,165). Peer review is essential for publication in scientific journals, and has also been adopted by government agencies and international bodies such as the International Panel on Climate Change (IPCC). Credibility is also important in the public sphere. The controversy that shrouds many environmental issues at the science-policy interface can generate a lot of attention, bringing the contributions of scientists into the public eye. Mills (2000) wrote: “perceptions of credibility come from the public’s trust in the scientific method and science institutions”(165). Jasanoff (1987) states that loss of credibility, as a consequence of advocacy, can destroy general perceptions of the authority of science and scientists in society in the eyes of the public. Lach et al. (2003) investigated many factors that might influence credibility and found significant differences between the perceptions of scientific credibility held by scientists, 14 managers, and the public. Although all groups ranked “reputation in specialization” as an important factor, scientists’ perception of credibility was focused on tenets of the scientific method (research methods, data), and the quality of peer-reviewed publications and interdisciplinary research contributions. The non-scientific respondents placed more importance on practicality of research, and experience/knowledge of place-specific sites (Lach et al. 2003). Although loss of scientific credibility has been identified as a legitimate threat to scientists who advocate policy preferences, there is little information available about perceptions of credibility beyond the research conducted by Lach et al. (2003), highlighting the need for further study in this area. There is, however, a variety of literature offering guidelines for scientists working at the policy interface. Some research in this area (Jasanoff 1987) makes an effort to draw boundaries between science and policy, and scientists and managers, in policy decisions to prevent such negative consequences as credibility loss, and the compromise of research results. A study by Mills and Clark (2001) uses outcomes from the Interior Columbia Basin Ecosystem Management Project to suggest propositions for research scientists in “high profile resource management decisions” and guidelines for scientists in these situations. The following propositions aim to protect and maintain scientific credibility: - Clarification of uncertainty and risk is essential to understand the degree of confidence in scientific conclusions - To protect credibility, science findings should be published and available for public review - Research scientists must be prepared to respond to harsh critiques and attacks meant to undermine their credibility or challenge their work. - Research scientists must remain independent from the final decision to maintain their credibility - When research scientists advocate a particular outcome or position, they place their credibility as an impartial scientist at risk (192-193) 15 Blockstein (2002) presents a more general set of guidelines for scientists about how to maintain scientific credibility when going beyond the reporting of research results: 1. Follow the facts and tell the truth 2. Obey the “rules” of science (base interpretation upon data/conclusions that are peer reviewed, explain how conclusions are reached, present margins of error) 3. Present caveats 4. Identify uncertainty 5. Help to distinguish between uncertainty and guesswork 6. Avoid hyperbole The need for a clear distinction between scientific results and policy preferences is common throughout the literature (Scott et al. 2007, Blockstein 2002, Lackey 2007). Lackey (2007) also presented stipulations for scientist involvement in advocacy: To Scientists, I say get involved, but play the appropriate role. If you choose to advocate your personal policy preferences, make it clear to everyone involved that you have stepped out of a scientific role and into the role of policy advocate, be aware that your values and preferences inherently are no more (or less) important than other participants in the policy debate (16). However, Mills (2000) argues that taking off one’s ‘science hat’ to put on a ‘personal value hat’ when advocating personal preferences to the public is equally problematic because the scientist gained access to the public communication platform by means of their scientific reputation. Alm (1998) suggests a separatist role where scientists who cross into the policy realm should cease active research and function solely as experts or consultants. In summary, loss of scientific credibility as a consequence of advocacy is a serious concern, and therefore, it is important to understand the associated risks and perceptions. Given the politically contentious, complex, and uncertain nature of natural resource policy problems, the involved science is highly susceptible to politicization, posing a significant threat to scientific credibility. While both the general public and science community perceive a scientist’s reputation in their specialization as an important component of credibility, adherence to the scientific method and quality of peer-reviewed publications remain vital components of scientific credibility perceptions within the science community. These components, along with a separation of results from advocacy 16 and the acknowledgement of uncertainty, are prevalent themes in the guidelines presented above, and throughout the literature. 1.4 Models of Advocacy As detailed in the literature review, a variety of categories have been developed, and interpretations made with regard to the roles of scientists in policy decisions (Lach et al. 2003; Steel et al. 2001; Steel et al. 2004; Scott et al. 2007; Brussard and Tull 2007; Blockstein 2002). For the purpose of this study, a framework identifying four different models of the role of scientists in policy has been developed and used in the survey instrument to gauge the preferences of respondents. Elements from the various potential policy roles for scientists in resource management (reviewed in section 1.3.1) are incorporated for operationalization in the questionnaire. The first model, referred to as the ‘traditional’ model, is adapted from Steel et al. (2001, 2004) and Lach et al. (2003). Similar to the ‘data’ component of Blockstein’s (2002) data to advocacy continuum, this model holds that the position of research scientists in policy decisions is to put forth objective scientific data, when pertinent to the process, and not engage in further management discussion or decision-making. Under this model, Lach et al. (2003) assert it is the responsibility of managers and policymakers to interpret such data and deliberate its weight in the decision process, emphasizing the distinction between science and politics. A second model, ‘science communication’ combines elements of Steel et al. (2001, 2004) and Lach et al.’s (2003) ‘interpret’ model. Lackey (2007) wrote that although scientists should be active in the deliberation of public policy by communicating information to policy-makers and the public, it is inappropriate for them to advocate personal preferences. Under this model, scientists should not go beyond communicating and interpreting results for decision makers (Lach et al. 2003). A third model, ‘expert advocacy’ is based on the tenet that scientists have ethical and moral obligations given their expertise to act as advocates in their area of expertise (Mills 17 and Clark 2001). Expert advocacy is analogous to Brussard and Tull’s (2007) definition of professional advocacy: "informing policy makers, managers, and the public about issues that arise in one's area of expertise" (21). The forth and final model, referred to as ‘political advocacy’, draws on elements of what Mills (2000) defines as position advocacy: A scientist advocates personal values outside of their core research area, from a science platform that can then be misconstrued as scientific information. In this case, according to Lackey (2007), the arguments: sound like science, read like science, are presented by people who cloak themselves in the accouterments of science but who are actually offering nothing but policy advocacy masquerading as science (15). This model of advocacy can be particularly precarious with respect to credibility. Freedman (2009) states: In general, scientists tend to steer clear of political advocacy, in part because of the risk that their subsequent work will be seen as conforming to their political agenda rather than being based on scientific evidence. Table 1.2 provides an overview of the four models, further characterized by policy continuum traits adapted from Scott et al. (2007). 18 Table 1.2 Models of Advocacy derived for survey instrument. Model Position Traits* 1) Traditional Scientists should provide objective scientific findings when relevant to their core area of expertise with no formal position - Conducting research on policy relevant issues - Publishing results in scientific journals - Bringing relevant science to the attention of managers and policy makers 2) Science Communication Scientists should interpret and explain their findings, but they should not advocate a preference for management. - Providing results of research to all stakeholders and the public - Giving interviews to the press about research results - Discussing research on radio or television shows 3) Expert Advocacy Scientists should act as policy advocates in their area of expertise. - Lobbying for or against specific policies or management outcomes – directly based on an individual’s research results - Providing advice to specific stakeholders about controversial issues (pertaining to results) 4) Political Advocacy Scientists should advocate their positions in any area of scientific research they feel is appropriate. - Lobbying for or against specific policies or management outcomes –outside of an individual’s research area - Use of value-laden language - Supporting a class of policies based on general beliefs or values *traits adapted from Scott et al. 2007 (some slightly modified) These models incorporate roles from the literature on advocacy in natural resource management science, as reviewed in this section (1.3). They can be applied to evaluate the behaviour/ roles of scientists at the science-policy interface. 1.5 Case Studies The cases of two prominent scientists, both known for their bodies of scientific research and their advocacy, exemplify the models introduced above. The careers of Dr. David Schindler, a Canadian ecologist, and Dr. James Hansen, an American climatologist, offer an interesting dialogue with regard to the both the benefits and drawbacks of advocacy at the science-policy interface. 1.5.1 Dr. David Schindler The case of Dr. Schindler has embodied the first three models of advocacy, and highlights the potential benefits expert advocacy can offer towards policy development. Working as both a government and academic research scientist, Schindler has embodied the ‘traditional’ and ‘science communication’ models for much of his career, making 19 valuable contributions to environmental policy. He founded and directed the Experimental Lakes Area (ELA) in Northwestern Ontario where the effects of eutrophication, acid rain, radioactive elements, and climate change on boreal ecosystems have been studied through interdisciplinary research over past decades (University of Alberta 2012). One of the many important policy outcomes of this project was acid rain mitigation in the United States. After more than two decades working as a government scientists at ELA, Schindler became a faculty member at the University of Alberta where he found himself, once again, at the science-policy interface studying the dioxin output of pulp mills in Alberta’s Athabasca River, which would eventually lead to new federal government regulations for mill effluent (Shwartz 2010). As a university scientist, Schindler has continued to focus much of his research on the implications of human activities, and climate change, in Canada’s boreal region. One specific area of Schindler’s research at the University of Alberta is the Athabasca watershed, the upper reaches of which are the location of massive industrial development associated with oil sands deposits. The oil sands are the world’s third largest proven reserve of crude oil and a driving economic force for investment, employment, and government revenue (Government of Alberta 2007). However, sizeable economic benefits do not come without environmental impacts, and the debate over management of this resource has become increasingly polarized politically. Research done by Schindler and his associates on water quality in regions of the Athabasca watershed surrounding oil sands development was published in Proceedings of the National Academy of Sciences. The first of two peer-reviewed articles detailed his team’s findings that concentrations of dissolved polycyclic aromatic compounds (PACs) in the Athabasca River were significantly higher downstream than upstream of oil sands operations (Kelly et al. 2009), and that seven chemical pollutants (cadmium, copper, lead, mercury, nickel, silver, and zinc) are present at concentrations exceeding Government guidelines downstream of oil sands development. The second published study concluded that oil sands development is contaminating the Athabasca watershed through air and waterborne conduits (Kelly et al. 2010). 20 Given the political controversy surrounding oil sands issues, Schindler’s findings had initially put him at odds with the Government of Alberta. They insisted the contamination Schindler’s research detected was from naturally occurring bitumen deposits along the river and not a product of oil sands development, a conclusion that was reached by the industry-funded Regional Aquatic Monitoring Program (RAMP), a body to which much of the region’s water monitoring had been designated (Shwartz 2010). Following the publication of his second research paper on oil sands contaminants, and the negative government response, he began to display many attributes of ‘expert advocacy’. Bringing a collection of visibly deformed fish caught in the Athabasca watershed downstream of oil sands development to a press conference, Schindler called for the Federal Government to fund a fish-monitoring program in the region (Nikiforuk 2010). A comment piece he authored in a 2010 issue of Nature pointed out deficiencies in current environmental monitoring programs and, similarly, called for Environment Canada to assume responsibility for industry- independent water monitoring through which results would be published in peer-reviewed scientific publications and communicated to the general public (Schindler 2010). The Royal Society of Canada echoed Schindler’s concerns in their report Environmental and Health Impacts of Canada's Oil Sands Industry. One of the conclusions of the 2010 report was that the environmental regulatory capacity of Federal and Provincial Governments has not “kept pace with the rapid expansion of the oil sands industry” and noted deficiencies with respect to data access for assessment of cumulative impacts and environmental monitoring (RSC 2010). With dozens of awards and honors to his name, Dr. Schindler has developed a strong scientific reputation through his decades of research in limnology and experimental ecology. Schindler believes that advocating the public policy implications of his research is part of his responsibility as a scientist (Shwartz 2010). Following years of controversy associated with Schindler’s oil sands research, the Government of Canada and Provincial Government of Alberta announced the 21 implementation of a joint monitoring plan for Alberta’s oil sands. In recognition of the need to better understand the cumulative environmental impacts of oil sands development, an adaptive monitoring plan with new, improved approaches to monitoring water, air, and biodiversity was introduced. As stated in the plan: “A strengthened scientific understanding of these impacts can help guide effective and responsible environmental management of this valuable resource” (Government of Canada 2011, 1). Canada’s Federal Environment Minister credited Dr. David Schindler as the ‘spark’ that triggered the development and implementation of the plan (Braid 2012). The Federal and Provincial government’s plans to, in partnership, develop a monitoring plan for water quality in the Athabasca watershed demonstrates the importance of scientific information in natural resource decision-making, and how the advocacy of scientists like Schindler can facilitate effective contributions to policy design. 1.5.2 Dr. James Hansen The case of climatologist James Hansen exemplifies the full set of advocacy models derived for the purpose of this study (Table 1.2). Hansen, head of the NASA Goddard Institute for Space Studies and an adjunct professor at Columbia University, is a renowned climatologist who has become well known for his advocacy. Applying these four models of advocacy to Hansen’s actions as a scientist and advocate offers a revealing case study of the implications and consequences of advocacy by scientists. Hansen operated under the ‘traditional’ model for much of his career as a climatologist, generating an impressive body of academic contributions to atmospheric science with dozens of publications beginning in the mid 1960s. Fashioning one of the world’s first climate models thirty years ago, he has since been responsible for groundbreaking developments in climate modelling and valuable contributions to knowledge of the earth’s changing atmospheric composition. In 1988, his findings took him to the US Senate where he brought the reality of anthropogenic global warming to the attention of the US Government. In an appeal 22 substantiated by future climate modelling projections, as well as global temperature and paleo-climatic data, Hansen urged a US Congressional Committee to take the threat of climate change seriously (Hansen 2009). This ground-breaking moment was Hansen’s transition from the ‘traditional’ model of observe and report to the ‘science communication’ model. Nearly two decades later, Dr. Hansen would find himself becoming an advocate for emissions reductions to mitigate anthropogenic climate change. Hansen’s research has led him to believe that 350 parts per million (ppm) 4 is the safe limit for atmospheric carbon dioxide (Hansen et al. 2008), and he began to lobby for the acknowledgment of this figure in emissions reductions policies, a characteristic of ‘expert advocacy’ (Table 1.2). Hansen, with help from others, has communicated this perceived quantity for target atmospheric CO2 to policy makers and the public (McKibben 2007). His findings have spawned new global initiatives for climate action (e.g. 350.org). Hansen’s foray into ‘political advocacy’ may have begun by targeting what has been commonly agreed-upon as the most dangerous source of anthropogenic emissions, coal (Hansen 2008). He has called for moratoriums on new coal-fired power plants and the phase-out of aging coal-fired generators (Hansen 2008). He has been arrested for protesting the extraction of coal (mountain-top removal), and its combustion in coal-fired generating plants. He would also begin to campaign against the development and exploitation of Canada’s extensive oil sands deposits, another potentially dangerous source of CO2 emissions, and was arrested in front of the White House as part of a campaign against the Keystone XL pipeline (Drajem 2011). Additionally, he writes letters to world leaders lobbying for a carbon taxation system as he believes it is the only legitimate way to wean society off of fossil fuels (Hansen 2009b). Hansen uses value-laden language when publically advocating for emissions reduction, a trait of ‘political advocacy’ (Table 1.2). He speaks of climate change as an ‘intergenerational injustice’, coal plants as ‘death factories’ (Hansen 2009a), and has 4 The acceptable limit was previously thought by many scientists to be in the range of 450 ppm. 23 suggested that fossil fuel company CEOs be ‘tried for crimes against humanity and nature’ (Revkin 2008). More recently, he has referred to Canada’s oil sands a ‘carbon bomb’, insisting that their continued exploitation will spell ‘game over’ for the climate (McGowan 2011). Hansen has personified each one of the advocacy continuum models at various stages of his career, but recently fits best under the ‘political advocacy’ model. He is well aware of his role as a political advocate. He believes that it is his duty, as a both scientist and a citizen, to speak to these issues (Hansen 2009b). However, it is important to reiterate that Hansen wasn’t always a political advocate, he was a scientist in the ‘traditional’ non- advocate sense of the word for a decades before speaking out. Underlying all of these models exemplified by Hansen, is the inherent threat of credibility loss. Hansen has made many valuable contributions to scientific knowledge, and enormously increased public awareness of anthropogenic climate change. However, his venture into the political realm has threatened his credibility as a research scientist, illustrating the consequences of increased levels of advocacy. In 2009, the American Meteorological Society’s decision to award Hansen with its top honour, The Carl-Gustaf Rossby Research Medal, came under fire. Specifically, the controversy surrounded the public communication component of the award and the argument that Hansen conforms scientific data to his political opinions (Freedman 2009). In Hansen’s defence it could be argued that his science has shaped his political views. Even if true, Hansen’s credibility as a scientist remains threatened. More recently, one of Canada’s prominent climate scientists announced the results of a study motivated by Hansen’s ‘carbon bomb’ and ‘game over for the climate’ references to Canada’s oil sands. Calculating the future greenhouse gas emissions, and resultant global warming that would occur if reserves of various fossil fuel resources were completely burned, Dr. Andrew Weaver found that the utilization of Canada’s oil sands deposits, in their entirety, would only increase the global climate by a minute amount in comparison to the combustion of coal and unconventional gas (Swart and Weaver 2012). These findings de-bunked Hansen’s claims and it has been 24 suggested that Hansen’s exaggerated statements undermine the credibility of the climate movement. The case of James Hansen demonstrates the valuable contributions scientists can make by venturing beyond the ‘traditional’ model. Yet, the higher risks to credibility as a consequence of political advocacy may be too great as they can compromise the contribution of potentially important research in policy-relevant issues. 1.6 Research Questions and Hypotheses The objectives of this study are to examine perceptions of advocacy and credibility in policy-relevant resource science from the perspectives of the science community, resource professionals, and the public. More specifically, the study seeks to determine what models of advocacy are deemed acceptable, what models pose a legitimate threat to scientific credibility, and where differences exist between these groups. If an individual’s support for scientists advocating on behalf of resource management policy issues determines what they perceive as credible then it is important to understand what underlying variables can influence their perceptions. Therefore, in addition to investigating what models of advocacy are acceptable publicly and professionally from a credibility standpoint, this research also examines the influences of socio-demographic characteristics, as well as science and environmental value orientations, on an individual’s support for advocacy. 1.6.1 Research Questions Two research questions frame this study: 1. How is an individual’s support for advocacy influenced by personal values or demographic characteristics? 2. Does advocacy affect a scientist’s credibility in the eyes of the science community, resource professionals and the general public? 25 1.6.2 Populations of Interest To address these research questions, questionnaire data was collected from four groups representative of the science community, resource professionals (foresters and biologists), and the public. University professors from the UBC Faculties of Science, Forestry, and Land and Food Systems were recruited to participate in this study in representation of the science community. 5 This group was selected as a population of interest for this study given the extent of previous research into support for advocacy and perspectives of credibility involving research scientists (Lach et al. 2003; Steel et al. 2001). With regard to socio- demographic characteristics and values, it is known that respondents from this sample will have the highest levels of education, and are expected to have higher environmental values, and the highest science values, given their profession. It is also expected that the majority of faculty members will support the ‘science communication’ and ‘expert advocacy’ models. This study surveyed members of two British Columbian professional associations: The British Columbia Association of Professional Biologists (APB) and the Association of British Columbia Forestry Professionals (ABCFP). 6 Professional societies were selected as a target group for this study given their role at the science policy interface. As stated by Scott et al. (2008): “Professional societies are uniquely situated to serve as bridges between scientists, science, and the policy form” (578). Although both are professional societies, it is expected they will exhibit different values and socio-demographic characteristics given the respective objectives of their societies. Taking into account the culture of environmental advocacy observed in conservation biology (Scott et al. 2007; Brussard and Tull 2007; Blockstein 2002; Foote et al. 2009), it is predicted that professional biologists (APB members) will support the expert advocacy model, have higher science values, and be more ‘biocentric’ than professional foresters (ABCFP members) with respect to environmental values. 5 Professors, assistant professors, associate professors, and emeritus professors were sampled. 6 Registered professional foresters (RPF), foresters in training (FIT), registered forest technologists (RFTs), and retired ABCFP members were sampled. 26 The fourth and final target group for this survey was the general public. Public perceptions of advocacy and credibility in policy-relevant resource science are critical because public opinion influences the salience of policy issues (Blake et al. 1997), as one of the primary objectives of public policy is to represent the best interest of the general public (Bocking 2004). However, given the numerous impediments to obtaining a representative sample of the general public by web survey, a cross-disciplinary sample of students from ten different undergraduate programs at the University of British Columbia were sampled. Conceptual arguments exist in the field of social psychology that the use of undergraduate student samples in experimental studies lack external validity (Sears 1986) 7 . However, a more recent publication, in the context of political science, presents empirical data suggesting that students are indistinguishable from non-students in many socio-demographic traits, and that ultimately, the use of student subjects is not an innate problem for experimental research (Druckman and Kam 2009).While the student sample may not be an accurate representation of the general public as there are notable socio- demographic differences (e.g. lower age, higher education), this sample may be more representative of the ‘attentive public’. With respect to socio-demographic characteristics and value orientations, this sample group will likely have the lowest levels of education and science values, their environmental values are expected to be lower than APB members and professors. 1.6.3 Hypotheses The over-arching hypothesis of this study is that the perceived credibility of scientists that engage in advocacy will decrease if they engage in increasing levels of advocacy. However, this exploratory component of the study is preceded by a series of hypotheses concerning the influence of value orientations and socio-demographic characteristics on a respondent’s support for advocacy. Taking past research into consideration (section 1.3.3), relationships are expected between support for advocacy and sample source, personal value orientations towards science and environmental issues, and socio- demographic factors (age, gender, education, political affiliation). Relationships between support for advocacy and additional variables tailored to each sample group: source of 7 The ability to make generalizations from a sample to a larger population. 27 employment (resource professionals), and faculty/department of study (academic scientists, undergraduate students) are also expected. 1.6.3.1 Support for Advocacy vs. demographics/ values 1.6.3.1.1 Sample source and support for advocacy The four groups of respondents surveyed in this study have variable socio-demographic attributes (age, education), and differences in value orientations (1.6.2) are also expected. Additionally, previous findings pointing towards a prominence of pro-advocacy views in conservation biology suggest that APB members could have a greater support for advocacy than the other sample groups (Scott et al. 2007; Blockstein 2002). Therefore, it is hypothesized that there will be significant differences in support for advocacy observed between the four sample groups. Faculty members and APB members are expected to have stronger science and environmental values than ABCFP members and Students and therefore may be more likely to support higher levels of advocacy. H1. Support for the degree of advocacy will be significantly different between sample groups. 1.6.3.1.2 Science values and support for advocacy Science values, measured on an index similar to that of the NEP scale (see section 2.4), are expected to correlate positively with support for advocacy when taking into account the findings of Steel et al. (2001, 2004) and Gray and Campbell (2009). Therefore, it is hypothesized: H2. Respondents that place a higher value on science/ the scientific method will support higher models of advocacy 1.6.3.1.3 Environmental values and support for advocacy Orientations towards the environment (environmental concern/ biocentric orientation) are measured by the NEP scale (see section 2.4). Consistent with the findings of Steel et al. (2004) it is predicted that respondents with higher NEP scores (more biocentric) will be 28 likely to support higher levels of scientist involvement in policy decisions (higher models of advocacy). H3. Respondents with higher environmental values (higher NEP scores) support higher models of advocacy. 1.6.3.1.4 Age and support for advocacy There are contradictory findings in the literature with regards to age and environmental values (which, as noted above, are expected to influence support for advocacy). Blake et al. (1997) found middle age cohorts to have higher NEP scores, while Inglehart (1989) suggested that younger people have more concern for environmental issues. Steel et al. (2001) found younger respondents were less likely to support the most dominant ‘make decisions’ role for scientists in natural resource policy. Due to the fact that Steel et al.’s (2001) findings relate directly to preferred roles for scientists, it is predicted (for this study) that younger individuals will have higher environmental values on average, and therefore more support for higher levels of advocacy. H4. Age will be negatively associated with support for advocacy. 1.6.3.1.5 Gender and support for advocacy The literature with regard to gender and support for advocacy suggests the relationship is different among the various sample groups. Steel et al. (2010) found gender did not have much influence on the personal preferences of scientists and managers, while Steel et al. (2001) found that, within the general public, women were more likely than men to support the lower ‘interpret’ model for scientists, and less likely to support the more involved ‘advocate’ and ‘make decisions’ roles. For this study, it is predicted that: H5. Men will demonstrate support for higher levels of advocacy than women. 29 1.6.3.1.6 Education and support for advocacy Although every respondent selected into the sample has some extent of post secondary education, a variety of levels of education will be present in the sample. With respect to this study, it is difficult to predict the influence of education on support for advocacy. Taking into account Steel et al.’s (2001) findings that respondents with higher education support higher levels of scientist involvement in policy, it is hypothesized: H6. Level of education will be positively associated with support for advocacy. 1.6.3.1.7 Political affiliation and support for advocacy The political affiliation of a respondent can be correlated with how much value they place on science, or their level of environmental concern. Consistent with the findings of Steel et al. (2001), it is expected that respondents on the far right of the political spectrum will be less likely to support higher levels of advocacy, and vice versa. Therefore: H7. Respondents further left on the political spectrum will be more likely to support higher models of advocacy. 1.6.3.1.8 Source of employment and support for advocacy Consistent with the findings of Steel et al. (2001), it is expected that those employed by, or working closely with industry will be less likely to support higher models of advocacy. H8. Respondents employed by primary resource industries will be less likely to support higher models of advocacy. 1.6.3.1.9 Faculty/ department of study and support for advocacy Academic scientists and students studying more applied/ theoretical sciences may be less likely to support higher models of advocacy than those studying more natural or multidisciplinary sciences. This prediction is supported by the findings of Scott et al. (2007) that advocacy was most prevalent in natural resource science journals related to conservation biology, and supported by most conservation biologists surveyed. It is also 30 supported by Barke and Jenkins-Smith’s (1993) findings that life scientists perceive higher environmental risks, prefer stronger environmental management requirements, and were more likely to support calls for government action in contrast with physical scientists. H9. Support for advocacy will be significantly different between faculties. 1.6.3.2 Credibility It is hypothesized that, with the four models of advocacy arranged on a continuum from low (traditional) to high (political advocacy) the threat of credibility loss will increase with increasing levels of advocacy. In other words, the perceived credibility of scientists who engage in advocacy will decrease if they engage in increasing levels of advocacy. Science Expert Political Traditional Communication Advocacy Advocacy Figure 1.1. Advocacy model continuum. The traditional model, the idea that scientists should report only objective scientific results, is thought to be the lowest risk behaviour. Political advocacy, on the opposite end of the continuum, is thought to be the highest risk behaviour with respect to credibility as it holds that scientists can advocate positions in any area. H10. Perceived credibility will be negatively associated with increasing levels of advocacy. ç 31 Chapter 2: Methods 2.1 Research Design The objective of this study is to examine perceptions of advocacy and credibility. More specifically, this research investigates the influence of values, and socio-demographic characteristics (independent variables) on support for advocacy (dependent variable), and the influence of advocacy on perceived credibility. The data for this study was collected by means of a self-administered online survey given to the following groups: members from science and resource science-related faculties at UBC (Science, Forestry, Land and Food Systems, and the Institute for Resources, Environment, and Sustainability), professional foresters registered in British Columbia (ABCFP members), professional biologists registered in British Columbia (APB members), and UBC undergraduate students in 2011. This survey instrument engaged respondents on such topics as: values of science and the scientific method in society, environmental issues, models of advocacy, and the perceived credibility of science and sources of scientific information in hypothetical situations. Respondents were also asked to provide socio-demographic information such as: age, gender, education, source of employment (foresters and biologists), faculty and department of study (students), and political affiliation (see Appendix A for complete questionnaire). The indexes used in the survey instrument (see section 2.3) were adapted from the literature and tailored to fit the research design, with the exception of ‘environmental values’ which directly employs the NEP scale (Dunlap et al. 2000). 2.2 Sampling Questionnaire data was collected from Registered Forest Professionals, Registered Professional Biologists, British Columbia Association of Professional Biology (APB), UBC Faculty Members, and UBC undergraduate students from April through December of 2011 by means of an online survey hosted by fluidsurveys.com. The utilization of a web questionnaire as a mode of data collection offers both benefits and drawbacks. Web surveys provide many advantages such as possibilities for large 32 numbers of respondents at a low cost with fewer restrictions, and in a shorter time period than other modes such as mail (Couper 2000; Dillman, Smyth, and Christian 2009). However issues such as coverage 8 , privacy, spam filters, and a greater potential for non- response are notable limitations (Couper 2000). For this study, a web survey was the most effective way to sample the broad range of participants as mailing addresses for students, and members of professional associations would not have been otherwise accessible. 2.2.1 Resource Professional Sample: Foresters and Biologists The resource professional samples include members of two British Columbia professional associations: The British Columbia Association of Professional Biologists (APB) and The Association of British Columbia Forest Professionals (ABCFP). Questionnaire respondents from the APB group were recruited through the Association’s administration. Data for this sample was collected between June and August of 2011. E- mail contact was established with the managing director of the APB who agreed to distribute an initial ‘request for participation’ and two successive follow-up reminders through the association’s ‘bioserve’ list serve. The initial request was distributed to members in late June 2011 with two follow-ups, spaced at three-week intervals, distributed in July and August. Respondents from the ABCFP group were recruited through the association’s bi-monthly member newsletter. Initial e-mail contact with the association’s CEO was made on the 22 nd of June 2011. The request to distribute an initial participation request and two follow-up reminders was denied. However, the communications director agreed to include the questionnaire URL and information in two issues of the bi-monthly ABCFP newsletter. The information was distributed to potential ABCFP respondents in mid- August 2011, with one follow-up two weeks later. 8 Couper (2000) defines coverage error as “a function of the mismatch between the target population and the frame population” (467). There is potential for coverage error when using online survey methods as individuals without computer/internet access are excluded from the sample. 33 2.2.2 Faculty Member Sample This sample includes faculty members from the UBC Faculties of Science, Forestry, and Land and Food Systems. Faculty members from the Institute for Resources, Environment, and sustainability (IRES) were also included. These Science and natural resource – related Faculties were selected to represent the ‘scientific community’. Unlike the other sample groups, the email addresses of UBC Faculty members are publicly available, which allowed potential respondents to be contacted directly. All available e-mail addresses for professors, assistant professors, associate professors and professors emeriti from these faculties (and IRES) were collected from their respective departmental websites. The e-mail addresses were sorted and imported into the fluidsurveys questionnaire account for distribution through the survey provider’s e-mail invitation tool. Potential respondents received a personalized ‘request for participation’ e-mail at the end of November 2011. A single follow-up was distributed in mid- December. 2.2.3 Undergraduate Student Sample The student sample includes undergraduate students from various UBC faculties and departments. Ten undergraduate departments/programs were sampled. Two of these ten groups, the applied biology and forestry undergraduate programs, were intentionally selected into the sample to allow for comparisons between faculty members, professional associations, and students in these fields in future analyses. The other eight undergraduate departments/programs were sampled using a random number generator. The email addresses for UBC Students are not publicly available. Therefore, members of this sample population were recruited through their respective departmental administrations. Each departmental/program administrator was personally approached with a hard copy of a letter detailing the project and asking them to distribute a ‘request for participation’ letter followed by two ‘reminders’ to their undergraduate student list- serve. Hard copies were followed up by e-mail correspondence with administrators: electronic versions of request letters, follow-ups and a document with questionnaire 34 content were provided. In some cases, personal correspondence with department/program heads was required to obtain permission for distribution of the ‘request for participation’ letters to the undergraduate e-mail list serves. Of the original random sample of programs/departments, four declined to participate in the survey. Four replacement departments were randomly selected and approached in the same manner. Subsequently, the final random sample included: The School of Social Work, and the Departments of Geography, Math, English, Sociology, Biological & Chemical Engineering, Physics and Astronomy (including Engineering Physics), and Central, Eastern, and Northern Europeans Studies (CENES). Recruitment of the student sample through departmental/ program administrations took place in April of 2011. Administrators were asked to provide a ‘send confirmation’ for the initial request and two successive follow-ups, and disclose the number of students on their list serve. Follow-up/reminder e-mails were closely spaced at 2-3 days to encourage higher response rates before the end of the academic semester. 2.3 Survey Instrument Development Development of the survey instrument began with a literature review of past research on environmental and science values, advocacy, and credibility. Using relevant past research as a foundation, the survey instrument was designed to measure support for advocacy, perceptions of credibility, and any personal values or socio-demographic attributes that may influence a respondent’s support for advocacy or perceptions of credibility. The literature identified attitudes toward science and the scientific method (science values) as an influence on people’s preferred roles for scientists in resource management policy decisions (Steel et al. 2001; Steel et al. 2004). Environmental values (biocentrism), as measured by the NEP scale has also been found to influence preferred roles for scientists (Steel et al. 2004). With regard to socio-demographic characteristics, the findings of Steel et al. (2001, 2004, 2009) suggest attributes as age, gender, education, and political affiliation will influence support for advocacy. Additionally, there is an extensive body of literature that has explored the relationship between NEP scores (biocentrism) and such socio-demographic characteristics as age, gender, education, political affiliation, and 35 source of employment, suggesting similar effects may be observed with support for advocacy (Blake et al. 1996; Inglehart 1989; Tindall et al. 2010; Jones and Dunlap 1992; Milbrath 1984) (see section 1.3.3). Additionally, the survey instrument was constructed utilizing elements of the Tailored Design Method (Dillman, Smyth, and Christian 2009). Dillman et al.’s (2009) Tailored Design Method (TDM) is underlain by principles of social exchange theory, which holds that the benefits that a survey provides to the respondents should outweigh the costs to achieve desired response rates. This method suggests a holistic approach towards developing effective survey questions with appropriate choices for the wording, visual design and implementation of the questionnaire (Dillman et al. 2009). Taking into account these past research findings, and TDM design elements, the final product was a six-section survey instrument. Four similar versions of the web questionnaire were used, tailored specifically to each group and hosted online by Fluidsurveys. The web questionnaire is comprised of six sections. Part 1 evaluates the respondents’ science values with a series of statements. Similarly, Part 2 asked the respondents a series of statements pertaining to environmental vales (NEP scale). Part 3 asked the respondents a single question to evaluate support for advocacy. Parts 4 ‘credibility of source’ and 5 ‘credibility of scientists who advocate positions on environmental issues gauges the respondents’ perceptions of credibility. Lastly, Part 6 consists of a detailed demographic information section. Five of the six sections are nearly identical between versions. The only differences exist in the final ‘demographic information’ section. Modifications are as follows:  Resource professionals (ABCFP and APB members) are asked to disclose source of employment, students and professors were asked to list the UBC faculty and department they are associated with.  Resource professionals and students were asked to disclose their highest completed level of education, and professors were asked to disclose their title as a faculty member. 36  An additional question was added to the ABCFP version for respondents to indicate their title as a member because both registered professional foresters (RPFs) and registered forest technologists (RFTs) were included in the sample. 2.4 Measures This section describes how the variables of interest for this study were operationalized and measured in the survey instrument (refer Appendix A for a copy of the complete questionnaire). 2.4.1 Science Values Section 1 of the survey instrument measures how much value the respondent places on science. Taking in account past research that has used elements of ‘objectivity’ (Steel et al. 2001) and 'positivism’ (Steel et al. 2004) to measure respondents’ attitudes toward science, a somewhat similar approach is taken in this research. To assess science values, respondents were presented with a series of 10 statements pertaining to scientific theory, inquiry, and the role of science and scientists in society. The statements were adapted from a 2008 United States National Science Foundation (NSF) questionnaire that surveyed the general public’s attitudes toward, and understanding of, science and technology (National Science Foundation 2009). Ten Likert-style items correspond with five categories: benefits of science in society, the scientific method, science investment, science and policy, and personal science value (Table 2.1). Two items from each category are presented. One is worded positively – where agreement would reflect high science values and the other is worded negatively. All odd-numbered statements correspond with positively worded statements. Questionnaire respondents are asked to rank these items on a 4-point scale (strongly disagree, mildly disagree, mildly agree, strongly agree) a fifth response ‘don’t know’ was also available in this section. 37 Table 2.1 Science Value Index. # Science Value Item Category Wording 1 Science and technology are making our lives healthier, easier, and more comfortable. Benefits of science in society Positive 2 Bias in scientific inquiry is expected and therefore accepted, it is human nature. Scientific method Negative 3 Even if it brings no immediate benefits, scientific research that advances the frontiers of knowledge is necessary and should be supported by the Federal Government. Science investment Positive 4 I do not find knowledge of science to be important in my daily life. Personal science value Negative 5 Decision-making in the field of natural resource management should reflect the best available science. Science and policy Positive 6 It has been noted that scientific research has produced both beneficial and harmful results in society. Overall, harmful results have outweighed the benefits. Benefits of science in society Negative 7 Adherence to the scientific method (propose hypothesis, develop experimental studies to test hypotheses) is essential in the development of scientific knowledge. Scientific method Positive 8 Our society allocates too much time and resources toward scientific inquiry. Science investment Negative 9 I take interest in new scientific discoveries and innovations. Personal science value Positive 10 Science should never take precedence over other issues in decision-making (social, economic) Science and policy Negative An index was created by averaging responses, and reverse scoring the odd ‘anti’ items to yield a numerical value ranging from 1.0 - 4.0. The lowest possible value 1.0 would be indicative of a respondent that places no value on science in his/her daily life. On the other hand a value of 4.0 would mean the respondent greatly values science and respects its role in society. Reliability tests (Cronbach’s alpha) and principle component analysis were performed as diagnostics to test the effectiveness of the scale in measuring science values (see section 2.5.3). 2.4.2 Environmental Values Section two of the questionnaire utilizes the New Ecological Paradigm (NEP) index (Dunlap et al. 2000), an extensively used measure of environmental attitudes (Dunlap 2008). The NEP index consists of 15 items addressing five facets of a pro-ecological worldview: limits to growth, the fragility of nature’s balance, the possibility of an eco- crisis, anti-anthropocentrism, and rejection of exemptionalism (Dunlap et al. 2000). 38 There are three items connected to each facet, with the odd items worded to support a pro-ecological view and the even numbers in disagreement (Table 2.2). Table 2.2 Environmental value index (NEP). # NEP Item Ecological Worldview Wording 1 We are approaching the limit of the number of people the earth can support. Limits to growth Positive 2 Humans have the right to modify the natural environment to suit their needs. Anti- anthropocentrism Negative 3 When humans interfere with nature it often produces disastrous consequences. Nature’s fragile balance Positive 4 Human ingenuity will insure that we do NOT make the earth unliveable. Anti- exemptionalism Negative 5 Humans are severely abusing the environment. Eco-crisis Positive 6 The earth has plenty of natural resources if we just learn how to develop them. Limits to growth Negative 7 Plants and animals have as much right as humans to exist. Anti- anthropocentrism Positive 8 The balance of nature is strong enough to cope with the impacts of modern industrial society Nature’s fragile balance Negative 9 Despite our special abilities humans are still subject to the laws of nature. Anti- exemptionalism Positive 10 The so- called “ecological crisis” facing humankind has been greatly exaggerated. Eco-crisis Negative 11 The earth is like a closed system with very limited room and resources. Limits to growth Positive 12 Humans were meant to rule over the rest of nature. Anti- anthropocentrism Negative 13 The balance of nature is very delicate and easily upset. Nature’s fragile balance Positive 14 Humans will eventually learn enough about how nature works to be able to control it. Anti- exemptionalism Negative 15 If things continue on their present course, we will soon experience an ecological catastrophe. Eco-crisis Positive NEP index scores are calculated on a numerical scale similar to the first section, respondents to the survey are asked to rank a series of statements on a 4-point scale (strongly disagree, mildly disagree, mildly agree, strongly agree). A fifth ‘don’t know’ response is also available in this section. The ‘anti- NEP’ statements are reverse-scored and the responses summed and divided by 15 to produce a numerical score from 1.0- 4.0 with a higher score representative of high environmental values. 39 2.4.3 Support for Advocacy Support for advocacy was measured in the third section of the questionnaire. It is important to measure support for varying degrees of advocacy to gain an understanding of what level of advocacy the respondent deems acceptable. This section was arranged as a single question in which a short definition of each model of advocacy (traditional, science communication, expert advocacy, political advocacy - see Figure 1.1) is listed and respondents must indicate which statement they most agree with. 2.4.4 Credibility of Source ‘Credibility of source’ is measured by a single question in section four, and asks respondents to rank their perceived credibility of four sources of scientific information: industry, university, government, or ENGO (e.g. would they believe a government scientists to be more credible than a university research scientist, etc.). 2.4.5 Perceived Credibility Section five is designed to assess the respondents’ perceptions of scientific credibility. Respondents are asked whether they would perceive a series of hypothetical actions of scientists in different areas of resource management science in various hypothetical situations as credible or not credible. In other words, would they trust the information being put forth by the scientist(s) based on their actions. The hypothetical actions are derived from a continuum of policy advocacy traits developed by Scott et al. (2007) and adapted to fit the four models of advocacy this study employs for analysis. Each model of advocacy has two corresponding policy advocacy continuum traits, eight in total (Table 2.3). 40 Table 2.3 Hypothetical perceived credibility scenarios. # Scenario Policy Advocacy Continuum Traits* Advocacy Model 1 A scientist measures heavy metal accumulation in a wetland area receiving acid mine drainage from an abandoned copper mine. The results are published in the Journal of Ecological Engineering. Conducting research on policy relevant issues and publishing results in scientific journals Traditional 2 The results of a study done by a team of wildlife biologists evaluating habitat fragmentation as a result of above- ground natural gas pipeline construction are brought to the attention of relevant managers, such as Environment Canada Bringing relevant science to the attention of managers and policy makers 3 A scientist specializing in watershed biogeochemistry is appointed to an advisory group monitoring water quality downstream from the Athabasca oil sands. He/she announces the results at a press conference Giving interviews to the press about research results Science Communication 4 A scientist from the BC Ministry of Forests, Mines and Lands discusses recent research initiatives in BC’s forests pertaining to the Mountain Pine Beetle Stewardship Research Strategy on CBC Radio Discussing research on radio or television shows 5 On the local news, a meteorologist reports climate change as the cause for the lack of snow on Cypress mountain during the 2010 Vancouver Olympic Winter Games Failing to acknowledge the full range of potential consequences of scientific uncertainty on interpretation of research Expert Advocacy 6 A university scientist argues that more stringent regulations be applied to logging road construction under the province’s Forest and Range Practices Act based on his/her research results with respect to soil erosion on steep slopes following logging activities. Lobbying for or against specific policies or management outcomes – based on an individual’s research results 7 A fish biologist from a local university publically denounces BC electricity policy and BC Hydro’s supply calculations that support the construction of run of the river power projects to maintain secure electricity supplies. Lobbying for or against specific policies or management outcomes – outside of an individual’s research area Political Advocacy 8 A Federal Government scientist criticizes Canadian climate policy citing the issue as “intergenerational injustice” and lobbies in favor of an alternative class of policies for CO2 emission reduction. Supporting a class of policies based on general beliefs or values (can be evidenced by use of ‘value-laden’ language) *(Scott et al. 2007 p. 34) 2.4.6 Demographic Information The final section measures a series of socio-demographic variables: source of employment (or faculty/dept of study), gender, age, education, and political affiliation. Respondents from the resource professional samples (ABCFP and APB members) were 41 asked to disclose their source of employment (industry, consulting, academic, government, other). ABCFP members were also asked to specify their title (professional forester, forest technologist, forester in training, or retired member). The other two samples, UBC students and faculty members, were instead asked to enter their faculty and department of study. Additionally, faculty members were asked to indicate their title (professor, associate professor, assistant professor, professor emeritus). All respondents were asked to specify their gender (male, female, other), and enter their age. Education was measured nominally for all samples except faculty members (some post secondary, completed post secondary, some graduate school, completed masters degree, completed doctoral degree). Political affiliation was measure by a contingency question. Initially, respondents were asked whether or not they voted in the last election. If they answered yes, they were asked to specify which federal political party they voted for (Conservative, Liberal, NDP, Green, other). 2.5 Data Analysis Upon completion of the data collection, an SPSS data file was imported from the fluidsurveys.com sever, and the research account was terminated. The final number of cases analyzed was N= 836 (ABCFP members: n = 68; APB members: n = 112; faculty members: n = 127; students: n = 529). SPSS version 20 was used to test for non-response bias, and then to perform descriptive, validity, reliability, and inferential statistical analysis. 2.5.1 Response Rates 12,573 individuals were invited to participate in the questionnaire. A total of 836 surveys were completed giving a response rate of 6.4%. The overall response rate was very low due to numerous sampling constraints. Although a three contact strategy (initial contact + two reminders) was desired, it was not always feasible. Table 2.4 summarizes, and gives a comparative view of, recruitment methods, reminders, and response rates specific to each group. 42 Table 2.4 Response rates (by sample group). Group Recruitment Method Contact* Sent Complete Response rate (%) ABCFP Members via ABCFP e-mail newsletter (ABCFP communications) 2 5100 68 1.3 APB Members via APB list serve (APB managing director) 3 977 112 11.5 Faculty via fluidsurveys invitation tool (direct contact) 2 552 127 13.0 Students via undergraduate list serves (dept. administrations) 1-3* 5944 529 8.9 Total 12573 836 6.4 *initial contact + reminders, **varies by department (see Table 2.5) Response rates varied between the four sample groups given the different methods of recruitment employed. The student response rates were lower (8.9%), given there was less control over the sampling procedures, and the number of contacts varied by department (Table 2.5). The ABCFP sample has the lowest response rate (1.3%), as the association would not send out emails to their list serve of members, and instead attached the project URL and description to two instalments of their bi-weekly e-newsletter. Table 2.5 Student sample response rates (by department). Department/ School Contact Sent Complete Response Rate (%) Bio-Chem Engineering 1 300 29 9.6 Biology 2 750 103 13.7 CENES* 1 1800 79 4.3 Physics and Astronomy 2 500 29 5.8 English 1 617 33 5.3 Forestry 3 593 97 16.6 Geography 3 468 66 14.1 Math 3 343 44 12.8 Social Work 1 70 4 5.7 Sociology 3 503 45 8.9 Total 5944 529 8.9 *CENES administration misunderstood the request and distributed the questionnaire URL to any student enrolled in courses offered by the dept. (1800 students) resulting in responses of students from more departments/programs than the 10 sampled, all responses that didn’t fit into other dept. categories were labeled as ‘CENES’ for the purpose of calculating approx. response rates. 43 The faculty member sample has the highest response rate (13.0%) as invitation emails were distributed directly, and individually. The APB sample response rate was also among the highest (11.5%) as a three contact strategy was applied through the association’s e-mail list serve. When low response rates are observed, there is always the potential for non-response error. Overton and Armstrong (1977) suggested a time trends method of extrapolation for estimating non-response error in which the mean responses of early and late responders are compared because of the idea that respondents who respond less readily are more like non-respondents. Applying the time trend extrapolation method, the range of questionnaire return dates were identified for all sample sources, and each was divided into thirds (early, mid, late respondents). 9 Independent sample t-tests were performed, by sample source, 10 to compare the means of early vs. late respondents for a selection of key variables. T-tests were performed on the independent variables: support for advocacy and perceived credibility responses. Odd-numbered science and NEP value items, as well as two randomly selected demographic variables: age and education, were also tested for non-response bias. No significant differences were found. 11 2.5.2 Descriptive Statistics Descriptive statistics (frequencies, mean, standard deviations, and 95% confidence intervals) were calculated for the complete sample, and by sample source (see results and Appendix B). For each variable, the mean responses of each of the four sample groups were compared to test for significant differences. Analysis of variance (ANOVA) tests were performed on the continuous variables: science value items/scale, NEP items/ scale, perceived credibility, and age. Because one of the assumptions of ANOVA is that the variances of the samples being compared are equal, all ANOVA tests were preceded by Levene’s test for homogeneity of variance. If 9 Student samples were further divided by department for analysis. 10 Tests were not performed on ABCFP sample, statistical power was insufficient due to the small sample size. 11 Some limitations with student sample (includes only sociology, engineering, forestry, geography, math; n= 461). Results may be less representative. 44 the Levene test was not significant, the ANOVA results were used. If statistically significant differences were located by the ANOVA test, the Scheffe post hoc test was used to determine the location of the differences (ie: which sample group means were significantly higher/lower than others). When Levene’s test was statistically significant (indicating that the variances were not equal, and the assumption needed for a valid ANOVA was not met) the procedure was modified to replace the ANOVA with a Welch F test, as it does not require equal variances. The Welch F test, when used, was followed by the Games-Howell post hoc test to identify the location of statistically significant differences. Chi-squared tests of independence were performed on the categorical variables (support for advocacy, gender, education, political affiliation) to identify statistically significant differences between samples. Chi-squared results are not able to determine the location of the significant differences; however Cramer’s V was used to interpret the strength of the relationship. 2.5.3 Principle Component and Reliability Analysis Principle component and reliability analysis were performed on the science and NEP items prior to scale composition. These methods of analysis offer a means through which to test the construction of index variables from a combination of items. Principle component analysis (PCA) reduces a large set of data into a smaller subset of measurement variables to reveal the underlying components (Field 2009). Component loadings gauge the substantive importance of each variable to each component. Varimax is a method of orthogonal rotation that attempts to maximize the dispersion of component loading, it can be used to provide factor clusters that are easier to interpret than the unrotated solution (Field 2009). A value greater than .4 is generally considered to be significantly correlated with a given factor and acceptable for interpretive purposes (Stevens 2002). Reliability analysis measures internal consistency, which is the extent to which the items reflect the construct of the scale they are intended to measure (Field 2009). Cronbach’s 45 alpha is the most common measure of scale reliability. Generally, a Cronbach’s alpha value greater than .7 is considered acceptable, while values below .7 are questionable, and below .6 are considered poor or unacceptable (Field 2009). 2.5.3.1 Science Value Scale Factor and reliability analysis was performed with the standardized scores of the ten questionnaire items intended to compose the science value scale. 12 Cronbach’s alpha for the ten science value questions was initially calculated to be 0.698, a moderate level of reliability. Cronbach’s alpha for the five sub-categories were found to be much weaker individually then for all items together (Benefits of Science in Society, = 0.464; Scientific Method = 0.329; Science Investment = 0.552; Personal Science Value =0.512; Science and Policy =0.321). This further supports the use of all science values statements in aggregate. However, the analysis revealed that the value of Cronbach’s alpha would rise if the second item was removed (Bias in scientific inquiry is expected and therefore accepted, it is human nature). The reliability of this scale was re-examined by splitting the data by sample group. The second item was observed to be detrimental to the alpha values of three of the four sample groups (the exception was ABCFP members), and was subsequently removed from the scale. Item 7 (Adherence to the scientific method is essential in the development of scientific knowledge), the item corresponding with item 2 in the “scientific method” sub-category, was also removed from the scale as the removal of the single item unbalanced the rotated PCA solution. Cronbach’s alpha for the eight- item scale (excluding the “scientific method” sub-category) was .699. Two factors were identified in the PCA for the eight science value items retained following reliability analysis. The two factors explained 45.87% of the variance (factor #1 = 32.62%, factor #2 = 13.25%). In the rotated PCA solution, six of the eight 12 The even numbered negatively worded items were reversed scored prior to obtaining the standardized scores. 46 statements loaded on the first component. The items composing the “benefits of science in society” sub-index (item 1, item 6) loaded on the second component. Table 2.6 presents the questionnaire items used to construct the science value scale, with factor loadings based on varimax rotation, and the Cronbach’s alpha for each item if deleted. Table 2.6 Science value scale construction. Science Value Item Factor Loading (varimax rotation) Cronbach’s alpha if deleted (α) Factor 1 Factor 2 1. Science and technology are making our lives healthier, easier, and more comfortable. - .839 .691 3. Even if it brings no immediate benefits, scientific research that advances the frontiers of knowledge is necessary and should be supported by the Federal Government. .630 - .667 4. I do not find knowledge of science to be important in my daily life. .602 - .672 5. Decision-making in the field of natural resource management should reflect the best available science. .591 - .677 6. It has been noted that scientific research has produced both beneficial and harmful results in society. Overall, harmful results have outweighed the benefits. - .737 .669 8. Our society allocates too much time and resources toward scientific inquiry. .659 - .627 9. I take interest in new scientific discoveries and innovations. .537 - .661 10. Science should never take precedence over other issues in decision-making (social, economic) .574 - .680 Variance explained by factors 1 &2 32.62% 13.25% - Total reliability of scale (α) .699 The general consistency of the PCA results and acceptable Cronbach’s alpha score supports the unidimensionality of these statements as measurement of science values. 2.5.3.2 Environmental Value (NEP) Scale Factor and reliability analysis was performed with the standardized scores of the fifteen questionnaire items that traditionally compose the NEP scale, to measure environmental values. 13 Cronbach’s alpha was calculated to be 0.836, suggesting the unidimensionality of the NEP scale. This is suggestion is supported by individual item totals as there were 13 The even numbered negatively worded items were reverse scored prior to obtaining the standardized scores. 47 not any gains in Cronbach’s alpha if any items were removed with one exception: Despite our special abilities humans are still subject to the laws of nature (item 9). If this particular item were deleted Cronbach’s alpha would increase to 0.842. Cronbach’s alpha for the five categories individually was not as strong as for all items together (Limits to Growth  = 0.569; Anti-anthropocentrism  = 0.592; Nature’s Fragile Balance  = 0.520, Anti-exemptionalism  = 0.458; Possibility of an Eco-crisis  = 0.758). This further supports the unidimensionality of the NEP scale as a whole. Four components were identified in the Principal Components Analysis (PCA) of the fifteen NEP items. The four components explain a total of 54.8% of the variance (factor 1= 31.43%, factor 2 = 8.51%, factor 3= 7.71%, factor 4= 7.12%). In the unrotated PCA solution, thirteen of the fifteen statements loaded on the first component. 14 Two of these items (item 11, item 15) were cross-loaded with the second and forth components respectively. Item 9 loaded on component #3, which, building on the reliability results above, suggested respondents answered this particular question inconsistently. Although the inconsistencies observed in both principal component and reliability analysis with respect to item 9 could potentially compromise the unidimensionality of the scale, it was not discarded from the scale. Because the NEP scale (unlike the science value scale derived for this study) is such a widely used measure, and the decline in Cronbach’s alpha by including item 9 was not large, the theoretical arguments for leaving the 15-item scale intact superseded the PCA and reliability results. Table 2.7 presents the questionnaire items used to construct the science value scale, with factor loadings based on varimax rotation, and the Cronbach’s alpha for each item if deleted. 14 The unrotated NEP PCA component matrix was found to be the most practical for interpretation. 48 Table 2.7 Environmental value (NEP) scale construction. NEP Item Factor Loading (unrotated matrix) Cronbach’s alpha if deleted (α) Factor 1 Factor 2 Factor 3 Factor 4 1. We are approaching the limit of the number of people the earth can support. .599 - - - .824 2. Humans have the right to modify the natural environment to suit their needs. .577 - - - .825 3. When humans interfere with nature it often produces disastrous consequences. .560 - - - .826 4. Human ingenuity will insure that we do NOT make the earth unlivable. .583 - - - .825 5. Humans are severely abusing the environment. .661 - - - .821 6. The earth has plenty of natural resources if we just learn how to develop them. .528 - - - .828 7. Plants and animals have as much right as humans to exist. .555 - - - .826 8. The balance of nature is strong enough to cope with the impacts of modern industrial society. .610 - - - .824 9. Despite our special abilities humans are still subject to the laws of nature. - - .763 - .842 10. The so- called “ecological crisis” facing humankind has been greatly exaggerated. .721 - - - .817 11. The earth is like a closed system with very limited room and resources. .444 - - .540 .832 12. Humans were meant to rule over the rest of nature. .495 - - - .830 13. We are approaching the limit of the number of people the earth can support. .400 .487 - - .835 14. Humans will eventually learn enough about how nature works to be able to control it. .520 - .828 15. If things continue on their present course, we will soon experience an ecological catastrophe. .724 - - - .817 Variance explained by factors 1 &2 31.43% 8.51% 7.71% 7.12% - Total reliability of scale (α) - - - - .836 2.5.4 Support for Advocacy vs. Value Orientations/ Demographics A series of inferential statistical methods were used to investigate the influence of value orientations and socio-demographic characteristics on the dependent variable ‘support for advocacy’. Bivariate analyses were performed as a precursor to multivariate analysis to explore the relationships between the support for advocacy and the variables of interest 49 (corresponding with the nine hypotheses listed in 0). Similar to the comparisons of mean responses between sample groups, ANOVA tests were used to examine the relationships between support for advocacy and the continuous variables of interest: science value scores, NEP scores, and age. Chi-squared tests of independence were performed on a series of categorical variables: sample source, gender, age, education, political affiliation 15 , faculty/department, and source of employment. Tests were performed on the complete sample, and again, split into sample groups. 16 Taking into account the results of the bivariate analysis, a series of ordinal regression models were derived with support for advocacy as the independent variable. Ordinal regression modeling was selected as the method of multivariate analysis as ‘support for advocacy’ is an ordinal variable.17 Regression modeling was carried out for each sample groups, and for the complete sample. 2.6 Limitations The web survey was determined to be the most viable mode of data collection for this research. However, there are still many limitations to sampling, specifically involving the external validity of the undergraduate student sample and the potential for non-response error, which could impact the representativeness of the data. It has been argued that the use of undergraduate student samples in experiment studies lacks external validity (Sears 1986). While the student sample may not be an accurate representation of the general public in some aspects, notably age and education, Druckman and Kam (2009) suggest that students are indistinguishable from non-students in many socio-demographic traits, and that ultimately, the use of student subjects in experimental research is not a serious concern. 15 For bivariate and multivariate analysis, political affiliation was recoded from federal political party (Conservative, Liberal, NDP, Green) to ‘right’ (Conservative), ‘center’ (Liberal), and ‘left’ (NDP, Green). 16 Tests involving support for advocacy vs. sample source, education, faculty/department, and source of employment were performed only where applicable. 17 Ordinal variables have an order, yet the distance between measures cannot be quantified (unlike interval/scale variables that are ordered with equal intervals, and can be measured on a linear scale). 50 Disproportionate response rates were observed between the four sample groups due to constraints on sampling procedures. The ABCFP recruitment process faced the most limitations, as the association would not agree to distribute ‘request for participation’ emails separately from their e-newsletter, resulting in a substantially low response rate. Dillman et al. (2009) advise to “actively seek means of using follow-up reminders in order to reduce non-response error” (360), however follow-up reminders were constrained by the ABCFP administration who would not agree to send more than one reminder, and a number UBC departmental administrations that were not willing distribute first or second follow up reminders to students. The use of Armstrong and Overton’s (1977) time trends method of extrapolation for estimating non-response error gives some assurance that non-response bias is not a serious concern. Further precautions to ensure the representativeness of the sample could not be taken as personal information for non-respondents was not available (with the exception of Faculty member email addresses). Although these results may not be generalizable for the larger population, all possible steps were to ensure as representative of a sample as possible. 51 Chapter 3: Results This section presents an overview of the sample and demographic characteristics followed by a series of descriptive, bivariate, and multivariate statistical results. Four groups were sampled: ABCFP members, APB members, UBC faculty members, and UBC undergraduate students. It was expected that an individual’s support for advocacy would be influenced by personal values and demographic characteristics. Support for advocacy was expected to be positively associated with science values, environmental values, and education; and negatively associated with age. Respondents further left in their political affiliation were expected to be more likely to support higher models of advocacy, and women more likely than men. Some variation was also expected between the four groups sampled. Faculty members and APB members were anticipated to have higher science and environmental values, with a majority supporting ‘expert advocacy’. Foresters and students were expected to have lower environmental and science values than the faculty members and biologists, and show less support for models of advocacy beyond ‘science communication’. 3.1 Sample Overview/ Demographics In total, 836 professional foresters, professional biologists, faculty members, and undergraduate students completed the questionnaire. The complete sample is 51% male and 49% female (SD= 0.51), with an average age of 32 (SD= 14.80, range = 18-79). The demographic characteristics of each sample group are reviewed and analyzed individually to further describe the groups. 3.1.1 ABCFP Member Sample Respondents in the ABCFP sample (n= 68) were predominantly male (73.5%) with an average age of 45 (SD= 12.46, range: 27-79). When asked to report the highest level of education they had completed, 70.5% had completed post-secondary (undergraduate degree/diploma), 17.6% completed a Masters degree, and 8.8% hold a Doctoral degree. A majority of the ABCFP respondents reported voting in the last federal election (92.6%), with 33.8% voting NDP, the political party with the highest proportion of votes (see Appendix B for supplementary tables). 52 The majority of ABCFP respondents, by membership title, are registered professional foresters (55%). Other membership titles represented in the sample are: registered forest technologists (10.3%), retired professional foresters (4.4%), and foresters in-training (4.4%). When asked to disclose their source of employment, 41.2% indicated they were employed by the government, 23.5% by consulting agencies, 22.1% by industry, and 2.9% by academic institutions. 3.1.2 APB Member Sample The majority of the APB sample respondents (n= 112) were male (61.6%), with an average age was 47 (SD= 11.42, range: 25-72). When asked to report the highest level of education they had completed, 43.7% hold an undergraduate degree, 43.8% have obtained Masters degree, and 12.5% have completed a Doctoral degree. In comparison with the ABCFP sample frequencies, more than twice as many APB members had completed graduate degrees. The majority of the APB respondents indicated they voted in the last federal election (92.9%), the highest frequency of respondents voted for the federal NDP (35.7%). Although similar frequencies of ABCFP members and APB members voted, and voted NDP, less APB members voted Conservative (15.2%) and more voted Green (16.1%) (Appendix B). When asked to indicate their source of employment, more than half of APB members reported they were employed by consulting agencies (53.6%). Approximately one-quarter of the sample is employed by government (25.9%), 8.0% by industry, and 2.7% by academic institutions. Notably, more than twice as many of the APB members sampled were employed by consulting agencies in comparison with ABCFP members (only 23.5%). On the other hand, fewer APB members were employed by industry and government than ABCFP members. 3.1.3 Faculty Member Sample This sample was comprised of professors, of various ranks, from Science and science- affiliated Faculties at the University of British Columbia (n= 127). More than three- quarters of the sample was male (80.3%), giving this sample the most uneven gender 53 ratio of all groups. The average age of this sample, 52 (SD = 10.80, range: 31-78), was significantly higher than the other groups (F (3, 808) = 718.858). 18 In comparison with the ABCFP and APB member groups, a much smaller proportion of respondents from this group voted in the last federal election (70.9%). More than one- third of respondents voted for the Liberal party (35.4%), which was more than twice as many in comparison with the ABCFP and APB members. Only 13.4% of the faculty members surveyed voted NDP, and a small portion (5.9%) voted conservative. The majority of faculty members, by title, identified themselves as professors (56.7%). 22.8% were associate professors, 10.2% assistant professors, and 7.9% professors emeritus. Table 3.1 UBC Faculty Member departments (n=127). Department Frequency % Physics and Astronomy 17 13.4% Earth and Ocean Science 12 9.4% Land and Food Systems* 12 9.4% Forest Resources Management 11 8.7% Mathematics 11 8.7% Zoology 11 8.7% Forest Science 9 7.1% Chemistry 8 6.3% Computer Science 8 6.3% Institute for Resources, Environment, and Sustainability 7 5.5% Botany 5 3.9% Microbiology and Immunology 5 3.9% Statistics 4 3.1% Wood Science 4 3.1% Fisheries 2 1.6% Radiology 1 0.8% * Not a department (no departments specified for Faculty of Land and Food Systems) The sample was distributed by faculty as follows: Science (63.8%), Forestry (19.7%), Land and Food Systems (10.2%), and the Institute for Resources, Environment, and Sustainability (College for Inter-disciplinary studies) (5.5%). Within these Faculties, the 18 Significant differences identified by the Games-Howell post hoc test. 54 respondents represented 17 departments (Table 3.1). The most frequently identified departments were Physics and Astronomy (13.4%), Earth and Ocean Science (9.4%), Forest Resources Management (8.7%), Mathematics (8.7%), and Zoology (8.7%). 3.1.4 Undergraduate Student Sample The student sample is comprised of 539 students from 24 programs/departments at the University of British Columbia. 19 Unlike the other three groups, the student sample is predominantly female (61.2%). The mean age was 23 (SD= 4.70, range: 18-77), significantly lower than the other three sample groups (F (3,808) = 718.858, p < .05). 20 The majority of respondents in this sample indicated they have yet to complete a post secondary program (69.4%), while 27.6% have a post secondary degree/diploma. As expected, there were significant differences between the mean levels of education for the three sample groups (2 = 396.975, df= 8, p < .05; Cramer’s V = 0.529).21 Slightly more than half of respondents in this sample group indicated that they voted in the last federal election. This is the lowest observed frequency of the three groups, however many students specified that they were unable to vote (age and/or not a Canadian citizen). Of the respondents that voted, political affiliation was more evenly dispersed amongst the four major federal political parties than observed in the other three groups. The highest frequency of students voted Liberal (16.8%), followed by NDP (14.2%). Similar percentages of respondents voted for Green (9.8%) and Conservative (10.4%). There were significant differences in political affiliation between the four samples (2 = 48.732, df= 9, p < .05; Cramer’s V = 0.182). Respondents from the student sample reported studying in a total of eight different UBC Faculties. The Faculties of Arts (37.8%), Science (33.7%), and Forestry (19.1%) were the most frequently identified. Out of the 24 different departments listed by respondents (Table 3.2), the highest frequencies were from the applied biology (19.7%), forest resources management (12.7%), and geography (12.3%). 19 Although students were only recruited through 10 departments, many respondents identified themselves with alternate departments in the questionnaire 20 Differences identified by the Games-Howell post hoc test. 21 Faculty members were not asked to indicate their highest level of education. 55 Table 3.2 Student sample departments (n= 529). Department Frequency % Applied Biology 104 19.7% Forest Resources Management 67 12.7% Geography 65 12.3% Sociology 50 9.5% Mathematics 46 8.7% English 38 7.2% Chemical and Biological Engineering 28 5.3% Forest Science 24 4.5% Physics and Astronomy 19 3.6% Engineering Physics 11 2.1% Undecided/unknown 11 2.1% Psychology 10 1.9% CENES 9 1.7% Wood Science 8 1.5% History 7 1.3% Music 6 1.1% Political Science 5 0.9% Computer Science 4 0.8% Social Work 4 0.8% Business 4 0.8% Earth and Ocean Science 3 0.6% International Relations 3 0.6% Economics 2 0.4% Forest Operations 1 0.2% 3.2 Support for Advocacy vs. Value Orientations/Demographics This section presents the descriptive, bivariate, and multivariate statistical results associated with the first research question: “How is an individual’s support for advocacy influenced by personal values or demographic characteristics?” 3.2.1 Descriptive Statistics: Science/Environmental Values, Support for Advocacy The descriptive statistics for science and environmental (NEP) value items and scales are presented, followed by the descriptive statistics for the support for advocacy variable. Significant mean comparisons between sample groups are also included. 3.2.1.1 Science Value Items These items evaluated respondents’ attitudes toward science according to five sub- categories: benefits of science in society, science investment, personal science value, science and policy. Table 3.3 provides an overview of frequency distributions and descriptive statistics for the science value items (mean, 95% CI, standard deviation). 56 Respondents generally agreed with the positively worded (pro-science) questions, and disagreed with the negatively worded (anti-science) questions. ANOVA/ Welch F results indicated there were statistically significant differences between mean responses of one or more of the sample groups for eight of the ten items. Items 1 and 6 represent the ‘benefits of science in society’ sub category. The majority of respondents agreed with the first item: Science and technology are making our lives healthier, easier, and more comfortable. The mean faculty member response (3.55) was found to be significantly higher (more agreeable) than the APB (3.29) and student (3.36) groups (F (3, 817)= 3.773, p < .05). 22 The majority of respondents disagreed with the sixth item: it is often thought that scientific research has produced both beneficial and harmful results in society. Overall, harmful results have outweighed the benefits (item 6). The mean student response to the corresponding anti-science statement (1.86) was found to be significantly higher than the mean responses of ABCFP (1.47), APB (1.58), and faculty members (1.33) (F= 20.441, df= 3, p < .05). 23 Items 2 and 7 represented the ‘scientific method’ category. Responses to item 2: Bias in scientific inquiry is expected and therefore accepted, it is human nature, varied between sample groups. Overall, a majority of ABCFP (67.6%), APB (64.3%) and faculty members (58.3%) strongly/mildly disagreed while less than half of students (43.9%) strongly or mildly disagreed. More than three-quarters of respondents were in strong or mild agreement with the statement: Adherence to the scientific method (propose a hypothesis, develop experimental studies to test hypotheses) is essential in the development of scientific knowledge (item 7). 22 Differences were identified by the Scheffe post hoc test, 23 The Welch F test was used because the Levene statistic (3.610, p < .05) indicated the variances of mean reponses were not equal. Differences were identified by the Game-Howell post hoc test. 57 Table 3.3 Science value item frequencies (%) and descriptive statistics (ABCFP n=68, APB n=112, Professor n= 127, Student n= 529, mode values in bold). Item Strongly Disagree (1) Mildly Disagree (2) Mildly Agree (3) Strongly Agree (4) Don’t know Mean 95% CI SD 1. Science and technology are making our lives healthier, easier, and more comfortable. 1.3% 6.5% 43.2% 47.2% 1.8% 3.39 * ± .05 .671 2. Bias in scientific inquiry is expected and therefore accepted, it is human nature. 22.7% 28.0% 34.8% 10.5% 3.9% 2.34 † ± .07 .959 3. Scientific research that advances the frontiers of knowledge is necessary and should be supported by the federal government. 1.1% 3.1% 28.9% 65.0% 1.9% 3.61 § ± .04 .607 4. I do not find knowledge of science to be important in my daily life. 76.1% 17.1% 3.9% 1.4% 1.4% 1.30 ¶ ± .04 .614 5. Decision-making in the field of natural resource management should reflect the best available science. 0.8% 3.1% 25.2% 66.0% 4.8% 3.64 * ± .04 .590 6. It is often thought that scientific research has produced both beneficial and harmful results in society. Overall, harmful results have outweighed the benefits. 45.0% 32.8% 10.3% 3.9% 8.0% 1.71 ** ± .06 .830 7. Adherence to the scientific method (propose a hypothesis, experimental design to test hypotheses) is essential in the development of scientific knowledge. 2.4% 10.5% 36.6% 47.1% 3.3% 3.33 ± .05 .767 8. Our society allocates too much time and resources toward scientific inquiry. 54.7% 30.3% 6.5% 1.8% 6.8% 1.52 ± .05 .710 9. I take interest in new scientific discoveries and innovations. 0.4% 2.9% 34.0% 61.6% 1.2% 3.59 †† ± .04 .568 10. Science should never take precedence over other factors in decision-making (economic, social, etc.) 27.3% 39.1% 19.1% 7.1% 7.4% 2.06 §§ ± .06 .896 * Faculty mean response significantly higher than students and APB. † Student mean response significantly higher than ABCFP and APB. § Faculty mean response significantly higher than ABCFP. ¶ Student mean response significantly higher than faculty. ** Student mean response significantly higher than ABCFP, APB, and faculty. †† Faculty mean response significantly higher than student. §§ ABCFP response significantly higher than APB and Faculty, Student mean response significantly higher than APB. 58 There were significant differences between the mean responses of the four sample sources for item 2, but not for item 7. For the second item, the mean student response (2.50) was found to be significantly higher (more agreeable) than the ABCFP and APB samples (1.97) (F= 13.313, df=3, p < .05).24 Items 3 and 8 correspond with the ‘science investment’ category. The majority of respondents from all sample groups agreed: Scientific research that advances the frontiers of knowledge is necessary and should be supported by the federal government (item 3). The mean response of the faculty sample (3.93) was significantly higher (more agreeable) than those of the ABCFP (3.54), APB (3.69) and student samples (3.52) (F=16.836, df= 3, p < .05). The majority of respondents strongly/mildly disagreed with the statement: Our society allocates too much time and resources toward scientific inquiry (item 8). Statements relating to ‘personal science value’ were presented in items 4 and 9. The majority of respondents from all groups disagreed with the statement posed in item 4: I do not find knowledge of science to be important in my daily life. The student mean response (1.34) was found to be significantly higher (more agreeable) than the faculty member mean response (1.15) (F (3,820) = 4.070, p < .05).25 For item 9, ABCFP and APB members were in unanimous agreement (strongly or mildly) in response to the statement: I take interest in new scientific discoveries and innovations. The majority of faculty members and students were in strong or mild agreement with this statement. The mean professor response (3.85) was significantly higher (more agreeable) than the student response (3.49) (F= 16.822, df= 3, p < .05).26 Items 5 and 10 correspond with the final category, Science and Policy. The majority of respondents were in strong or mild agreement with the statement: Decision-making in the 24 The Welch F test was used as the Levene statistic (0.476, p < .05) indicated, the variances of mean responses between groups were not equal, the Games-Howell post hoc test was used to identify where the differences lay. 25 The Scheffe post hoc test was used to identify the differences. 26 The Welch F test was used as the Levene statistic (56.816, p < .05) indicated the variances between mean responses were not equal. Differences were located by the Games-Howell post hoc test. 59 field of natural resource management should reflect the best available science (item 5). The mean response of faculty members (3.88) was significantly higher (more agreeable) than students (3.56) and ABCFP members (3.56). It was also revealed that the mean response of APB members (3.79) was significantly more agreeable than students (3.56) (F= 22.011, df= 3, p < .05).27 More than half of respondents were in strong/mild disagreement with the statement presented in item 10: Science should never take precedence over other factors in decision-making (economic, social, etc.). The mean ABCFP response (2.31) was significantly higher (more agreeable) than the mean responses of APB members (1.75) and professors (1.93). The mean student response (2.14) was significantly higher than the mean APB response (1.75) (F (3,770)= 8.449).28 Overall, the mean responses of the faculty member sample were found to be generally more agreeable than one or more of the other groups for most of the pro-science items. The mean responses from the student sample were found to be significantly more agreeable than one or more of the other groups for every negatively worded question. These findings suggest that students have the lowest science values of the group, and faculty members the highest. 3.2.1.2 Science Value Scale Applying Science Values as a summative scale indicated that generally, respondents strongly value science. 29 The minimum score was 1.88 and the maximum was 4.00 (top bound). The mean score for respondents from all sample groups was 3.49  0.03 (n= 658, SD= .369). The means scores of the four sample groups were compared (Table 3.4) and Welch F test results indicated the presence of statistically significant differences (F= 30.251, df=3, p < .05). 30 27 The Welch F test was used as the Levene statistic (46.720, p < .05) indicated the variances between mean responses for the statement. Differences were located by the Games-Howell post hoc test. 28 The Scheffe post hoc test was used to identify the differences. 29 See methods for scale construction. 30 The Welch F test was used as the Levene statistic (7.584, df=3, p < .05) indicated the variances between mean responses were not equal. 60 Table 3.4 Science value scale scores (descriptive statistics). Sample Source n Mean 95% CI SD ABCFP Members 59 3.47  .08 .327 APB Members 101 3.58  .05 .283 Faculty Members 107 3.71*  .05 .264 Students 391 3.40†  .04 .386 Total (All Samples) 658 3.49  .03 .369 * Significantly higher than ABCFP, APB, and students (p < .05) † Significantly lower than APB and UBC Faculty (p < .05) The mean faculty score (3.71) was significantly higher than the mean ABCFP (3.47), APB (3.58), and student (3.40) scores. The mean student score was found to be significantly lower than the APB and faculty member mean scores. 31 These findings support the prediction that faculty and professional biologists will have the highest science values, and students the lowest. 3.2.1.3 Environmental Value (NEP) items The new ecological paradigm (NEP) index was employed to measure respondents’ environmental values. The 15- item index, as derived by Dunlap et al. (2000), evaluated respondent’s beliefs and opinions with respect to five facets of an ecological worldview: Limits to Growth, Anti-anthropocentrism, Nature’s Fragile Balance, Anti- exemptionalism, and the Possibility of an Eco-crisis. Table 3.5 provides an overview of frequency distributions (%) and descriptive statistics for the NEP items (mean, 95% CI, standard deviation). Respondents from the APB and Professor samples generally agreed with the odd-numbered pro-biocentric items and disagreed with the even-numbered anti-biocentric items. The frequencies of ABCFP and student sample responses were less consistent with this pattern. ANOVA/ Welch F results indicated there were statistically significant differences between sample group mean responses for 14 of the 15 NEP items. 31 Differences were identified by the Games-Howell post hoc test. 61 NEP items 1, 5, and 11 represent the ‘limits to growth’ category. For NEP item 1, the mean response for the APB sample (3.61) was significantly higher (more agreeable) than the ABCFP (2.98), faculty (3.29), and students (3.38) mean responses (6.687, df= 3, p < .05). 32 The mean ABCFP (2.67) and student responses (2.68) for NEP item 5 were significantly higher than those of APB (2.00) and faculty members (2.12) (F (3,800) = 23.444, p < .05). 33 For the third and final ‘limits to growth’ item (11), the mean response of the APB sample (3.53) was significantly higher than the mean responses of the ABCFP (2.88), faculty (3.21), and student (3.12) samples (F= 11.403, df= 3, p < .05). 34 The ‘anti-anthropocentrism’ category was represented by items 2, 7, and 12. Significant differences were found between mean responses. For NEP item 2, the mean responses for APB members (1.81) and students (2.17) were significantly lower (less agreeable) than RPFs (2.62), and faculty members (2.49). Additionally, student mean responses were found to be significantly higher than APB members (F (3,798) =16.488, df= 3, p < .05). 35 The mean responses for APB members (3.62) and students (3.50) for NEP item 7 were significantly higher (more agreeable) than ABCFP members (3.12) and professors (3.10) (F= 10.880, df= 3. p < .05). 36 For NEP item 12, the mean APB response (1.30) was significantly lower (less agreeable) than ABCFP members (1.69) and students (1.55) (5.481, df= 3, p < .05). 37 32 A Welch F test was used as the Levene statistic (9.40, p < .05) indicated the variances between mean responses were not equal. Differences were identified by the Games-Howell post hoc test. 33 Differences were identified by the Scheffe post hoc test. 34 The Welch F test was used as the variances between mean responses were not equal among sample groups (Levene statistic 5.805, p <.05). Differences were identified by the Games-Howell post hoc test. 35 The Scheffe post hoc test was used to identify the differences. 36 The Welch F Test was used as the Levene statistic (5.904, p < .05) indicated the variances between the four sample means were not equal. Differences were located by the Games-Howell post hoc test. 37 The Welch F test was used as the Levene statistic (9.498, p < .05) indicated variances between the mean responses among the four sample groups were not equal. Differences were identified by the Games-Howell post hoc test. 62 Table 3.5 NEP item frequencies (%) and descriptive statistics (ABCFP n=68, APB n=112, Professor n= 127, Student n= 529, mode responses in bold). NEP Item Strongly Disagree (1) Mildy Disagree (2) Mildly Agree (3) Strongly Agree (4) Don’t know Mean 95% CI SD 1. We are approaching the limit of the number of people the earth can support 3.8 9.8 28.7 52.2 5.5 3.37 *  .06 .827 2. Humans have the right to modify the natural environment to suit their needs 23.4 36.7 28.1 7.7 4.1 2.21 †  .06 .902 3. When humans interfere with nature it often produces disastrous consequences 3.7 17.1 45.3 31.7 2.2 3.07 *  .05 .804 4. Human ingenuity will ensure that we do NOT make the earth unliveable 26.9 31.5 22.5 6.7 12.4 2.10 §  .07 .928 5. Humans are severely abusing the environment 3.0 5.6 29.1 61.2 1.1 3.50 ¶  .05 .739 6. The earth has plenty of natural resources if we just learn how to develop them 19.0 25.8 35.2 16.1 3.8 2.50 **  .07 .991 7. Plants and animals have as much right as humans to exist 3.6 10.2 24.0 58.9 3.3 3.43 ††  .06 .824 8. The balance of nature is strong enough to cope with the impacts of modern industrial nations 52.8 28.6 8.5 4.5 5.6 1.63 §§  .06 .839 9. Despite our special abilities humans are still subject to the laws of nature 1.2 1.6 20.0 75.1 2.2 3.73  .04 .551 10. The So-called “ecological crisis” facing humanity has been greatly exaggerated 53.5 27.6 10.3 3.0 5.6 1.61 ¶¶  .06 .805 11. The earth is a closed system with limited room and resources 4.3 16.0 35.5 40.9 3.2 3.17 *  .06 .860 12. Humans were meant to rule over the rest of nature 62.4 17.5 8.0 4.2 7.9 1.50 §§  .06 .835 13. The balance of nature is very delicate and easily upset 4.9 23.6 42.3 25.8 3.3 2.92 ***  .06 .842 14. Humans will eventually learn enough about how nature works to be able to control it 40.1 35.4 13.0 3.7 7.8 1.79 §  .06 .832 15. If things continue on their present course we will soon experience an ecological catastrophe 3.7 8.3 35.2 46.8 3.9 3.33 ¶  .05 .797 * APB mean response significantly higher than ABCFP, faculty and students † ABCFP and faculty mean responses significantly higher than APB and student groups § ABCFP, faculty, student mean responses significantly higher than APB. ¶ APB, faculty and student mean responses significantly higher than ABCFP. ** ABCFP and student mean responses significantly higher than APB and faculty †† APB and student mean responses significantly higher than ABCFP and faculty. §§ ABCFP and student mean responses significantly higher than APB ¶ ¶ ABCFP mean response significantly higher than APB, faculty, and students. *** APB and student mean responses significantly higher than ABCFP 63 Items 3, 8, and 13 relate to the category ‘nature’s fragile balance’. For NEP item 3, the mean response of APB members (3.35) was significantly higher (more agreeable) than ABCFP members (2.70), professors (2.88), and students (3.11). The student mean response was also found to be significantly higher than ABCFP members and professors (F= 11.165, df= 3, p < .05). 38 The majority of respondents were in disagreement with NEP item 8. The mean response of APB (1.43) was significantly lower (less agreeable) than ABCFP members (1.89) and students (1.65) (F= 4.820, df= 3, p < .05). 39 The mean response of ABCFP members (2.53) for NEP item 13 was significantly lower (less agreeable) than the mean responses of APB members (2.88) and students (3.01) (F= 7.671, df=3, p < .05). 40 Items 4, 9, and 14 represented the ‘anti-exemptionalism’ category. Significant differences were found between groups for items 4 and 14. For NEP item 4, the mean response of the APB sample (1.66) was significantly lower (less agreeable) than the ABCFP (2.48), faculty (2.13) and student samples (2.15). The mean response of APB members (1.39) for NEP item 14 was significantly lower (less agreeable) than ABCFP members (1.73), faculty (1.86), and students (1.87) (F= 17.653, df= 3, p < .05). 41 Items 5, 10, and 15 deal with the final NEP category: ‘possibility of an eco-crisis’. For item 5, the mean response of the ABCFP sample (2.97) was significantly lower (less agreeable) than APB (3.68), professors (3.49), and student samples (3.53) (F= 10.353, df= 3, p < .05). 42 The mean response of the ABCFP sample (2.13) for NEP item 10 was significantly higher (more agreeable) than the APB (1.41), faculty (1.54), and student samples (1.60). Additionally, the student mean response was found to be significantly higher than the APB 38 The Welch F test was used as the Levene statistic (6.063, p < .05) indicated variances were not equal among mean responses. Differences were identified by the Games-Howell post hoc test. 39 The Welch F test was used as the Levene statistic (3.194, p < .05) indicated variances were not equal among mean responses. Differences were identified by the Games-Howell post hoc test. 40 The Welch F test was used as the Levene statistic (3.742, p < .05) indicated variances between the mean responses among the four sample groups were not equal. Differences were identified by the Games-Howell post hoc test. 41 The Welch F Test was used as the Levene statistic (3.796, p < .05) indicated variances between the mean responses among the four sample groups were not equal. 42 The Welch F Test was used as the Levene statistic (3.606, p < .05) indicated variances between the mean responses among the four sample groups were not equal. 64 mean response (F= 8.560, df= 3, p < .05). 43 For NEP item 15, the mean response of ABCFP members (2.83) was significantly lower than APB members (3.47), faculty members (3.38), and students (3.38) (F= 7.355, df= 3, p < .05). 44 Overall, the mean responses of the APB sample were found to be generally more agreeable than one or more of the other groups for most of the positive (biocentric) items. The mean responses from the ABCFP sample were found to be significantly more agreeable than one or more of the other groups for most negatively worded questions. These findings suggest that professional biologists will have the highest NEP values, forest professionals the lowest. 3.2.1.4 Environmental Values (NEP) Scale The application of NEP as a summative scale indicates that respondents from all sample groups generally had higher environmental values, and therefore, more biocentric attitudes. The mean score was 3.25  0.04 (n= 573, SD= .460). The minimum score was 1.40 and the maximum score was 4.00 (the top bound). Table 3.6 details NEP scores by sample. Table 3.6 Environmental value (NEP) index scores. Sample Source n Mean 95% CI SD ABCFP Members 45 2.93*  0.16 .533 APB Members 89 3.50 †  0.07 .364 Faculty Members 88 3.26 §  0.09 .441 Students 351 3.23 §  0.05 .445 Total (All Samples) 573 3.25 0.04 .460 * Significantly lower than all other sample groups (p < .05) † Significantly higher than all other samples groups ( p < .05) § Significantly higher than the ABCFP sample, significantly lower than then APB sample (p < .05) 43 The Welch F Test was used as the Levene statistic (5.805, p < .05) indicated variances between the mean responses among the four sample groups were not equal. Differences were identified by the Games-Howell post hoc test. 44 The Welch F Test was used as the Levene statistic (3.245, p < .05) indicated variances between the mean responses among the four sample groups were not equal. Differences were identified by the Games-Howell post hoc test. 65 The mean score of ABCFP members (2.93) was significantly lower (less biocentric) than APB members (3.51), faculty members (3.26), and students (3.23). The mean score for the APB sample was found to be significantly higher (more biocentric) than the other three sample groups. 45 The scores of faculty members and students were both significantly higher than ABCFP members, yet significantly lower than APB members. These results support the prediction that forest professionals will have the lowest NEP scores, and professional biologists will have the highest. 3.2.1.5 Support for Advocacy To measure support for advocacy, respondents were presented with four statements corresponding with models of advocacy (low to high) and asked to select which one they most agreed with (Table 1.2). The highest frequency of respondents in each sample group supported the statement: Scientists should act as policy advocates in their area of expertise (expert advocacy model). More than half of APB respondents agreed most with this statement (59.8%), while nearly half of ABCFP members (47.1%), students (49.6%), and professors (48.2%) also agreed most with this statement (Table 3.7). Table 3.7. Support for advocacy: frequencies (%) and descriptive statistics. (ABCFP n=68, APB n=112, Faculty n= 127, Student n= 529). Sample Source Traditional (1) Science Communication (2) Expert Advocacy (3) Political Advocacy (4) Mean 95% CI SD ABCFP 11.8 32.4 47.1 8.8 2.53 0.19 .819 APB 3.6 30.4 59.8 6.3 2.69 0.12 .644 Faculty Members 10.2 23.6 49.6 16.5 2.72 0.15 .861 Students 11.5 24.0 48.2 16.3 2.69 0.07 .878 Similar proportions of respondents from the ABCFP (32.4%) and APB (30.4%) samples supported the statement: Scientists should interpret and explain their findings, but they 45 The Welch F test was used as the Levene statistic (3.408, p < .05) indicated variances between the mean responses among the four sample groups were not equal. Differences were indentified by the Games-Howell post hoc test. 66 should not advocate a preference for management (science communication). A lower, very similar proportion of professors (23.6%) and students (24.0%) supported this level of advocacy. Chi-squared test of independence results indicated there were statistically significant differences between the four sample groups (2 = 19.736, df=9, p < .05; Cramer’s V = .089). Cramer’s V indicated a weak association. 3.2.2 Bivariate Analyses (Dependent Variable: Support for Advocacy) Tables 3.8 – 3.21 detail a series of bivariate analyses that were performed to examine the relationships between support for advocacy (preferred model, from low to high: traditional, science communication, expert advocacy, political advocacy) and a series of scale and categorical variables. For the complete sample, science values (H2), environmental values (H3), age (H4), gender (H5), education (H6), political affiliation (H7). Relationships with support for advocacy were further examined by sample group, as significant differences in support for advocacy were found between sample groups. 46 Source of employment (H8), and UBC faculty of study (H9) added to the analysis for applicable sample groups. 47 3.2.2.1 Science Values The science values of respondents were expected to increase with support for advocacy. Mean science value scores were observed to increase for models 1-3, while the forth model (political advocacy) has a slightly lower mean science value than the third (Table 3.8). F test results (F= 7.340, df=3, p < .05) 48 identified statistically significant differences in mean science values for the support for advocacy response categories. The mean science value scores of respondents who supported the traditional model (3.32) were significantly lower than respondents who support the higher expert advocacy (3.54) and political advocacy (3.53) models. Additionally, respondents who supported the science 46 See section 3.2.1.5 above (2 =19.736, df = 9, p < .05) 47 Source of employment was examined for APB and ABCFP groups, and faculty for the Faculty and Student samples. 48 The Welch F Test was used as the Levene Statistic (4.65, p < .05) revealed the variances of the mean science values between response categories were not equal. 67 communication model (3.42) had significantly lower mean science value scores than those who supported the expert advocacy model. 49 Table 3.8 Support for advocacy vs. science value scores (n= 658). Advocacy model n mean science value scores Traditional 67 3.32 Science Communication 165 3.42 Expert Advocacy 338 3.54 Political Advocacy 88 3.53 Total 658 3.49 F= 7.340, df = 3, p < .05 When further analyzed by sample group, mean science value scores were observed to increase from low to high models of advocacy (1- 4) for the ABCFP and APB samples. Like the complete sample, mean science value scores for the student sample increased for models 1-3 and levelled off for the forth model (Table 3.9). Table 3.9 Support for advocacy vs. science value scores by sample source (significant results in bold). Advocacy model ABCFP APB Faculty Students n mean n mean n mean n mean Traditional 8 3.30 4 3.12 10 3.78 45 3.24 Science Communication 20 3.43 32 3.48 25 3.60 88 3.34 Expert Advocacy 26 3.54 60 3.65 54 3.73 198 3.46 Political Advocacy 5 3.60 5 3.70 18 3.76 60 3.44 Total 59 3.47 101 3.58 107 3.71 391 3.40 F= 1.127 p > .05 F(3, 97)= 7.889 p < .05 F(3,103)= 2.051 p > .05 F (3, 387)= 4.216 p < .05 Statistically significant differences were identified in the ABP and student groups. ANOVA results (F (3, 97) = 7.889, p < .05) identified statistically significant differences in the APB sample. The mean science value scores of respondents who supported the traditional model (3.30) were significantly lower than respondents who supported the higher expert advocacy (3.54) and political advocacy (3.60) models. Respondents who supported the science communication model had significantly lower mean science value scores (3.48) than those who supported the expert advocacy model. 50 ANOVA results for the student group (F (3, 49 Statistically significant differences were identified by the Games Howell post hoc test. 50 Differences identified by the Scheffe post hoc test. 68 387) = 4.216, p < .05) revealed there were significant differences, and that the mean science value scores of students who supported the traditional model of advocacy (3.24) were significantly lower than students who supported the expert advocacy model (3.46). 51 Overall, these bivariate results support the hypothesized relationship between science values and support for advocacy for the complete sample, and within the APB and Student groups. However, the highest model (political advocacy) appears to be an exception to this relationship for the complete sample, and student group, as science values stop increasing at the third model (expert advocacy). 3.2.2.2 Environmental (NEP) Values Environmental values, measured by NEP scores, were expected to increase with support for advocacy. Similar to the results detailed above (section 3.2.2.1), environmental values were observed to increase for advocacy models 1-3, yet decreased slightly for the forth model (Table 3.10). ANOVA results (F= 5.624 p < .05) identified statistically significant differences between mean science values and preferred advocacy models. The mean NEP scores of respondents supporting the expert advocacy model (3.32) were found to be significantly higher than those supporting the science communication (3.18) and traditional models (3.09). 52 Table 3.10 Support for advocacy vs. NEP scores (n=573). Advocacy model n mean NEP score Traditional 57 3.09 Science Communication 144 3.18 Expert Advocacy 295 3.32 Political Advocacy 77 3.24 Total 573 3.25 F (3, 569) = 5.624 p < .05 51 Differences identified by the Scheffe post hoc test. 52 Differences indentified by the Scheffe post hoc test. 69 The mean NEP scores of ABCFP and Faculty samples increased from low to high models of advocacy. The mean NEP scores for APB and Student samples increased for models 1-3, with mean NEP scores dipping lower for political advocacy (Table 3.11). Table 3.11. Support for advocacy vs. NEP scores by sample source (significant results in bold). Advocacy model ABCFP APB Faculty Students n Mean n mean n mean n mean Traditional 7 2.90 4 3.35 9 2.93 37 3.14 Science Communication 16 2.78 24 3.38 19 3.20 85 3.20 Expert Advocacy 18 3.01 55 3.57 45 3.29 177 3.28 Political Advocacy 4 3.22 6 3.46 15 3.47 52 3.15 Total 45 2.93 89 3.50 88 3.26 351 3.28 F (3,41)= .969 p > .05 F= 3.405 p > .05 F= 1.583 p < .05 F(3,347)= 2.014 p > .05 When analyzed by sample source, statistically significant differences were found only in the faculty group (F= 1.583, df =3, p < .05) 53 , and could not be identified by the Games-Howell post hoc test. The bivariate results for environmental values support the hypothesized relationship between environmental values and support for advocacy for the complete sample, and within the faculty sample group. However, the highest model (political advocacy) appears to be an exception to this relationship for the complete sample. NEP values increase from the first to third models, with the mean NEP values of respondents supporting the at the third model (expert advocacy) are significantly higher than the mean NEP values of respondents supporting the first two models. The mean NEP scores for political advocacy are lower than expert advocacy (third model), but still higher than the first two models. 3.2.2.3 Age Although a relationship between age and support for advocacy was hypothesized, the bivariate results do not show a visible trend (Table 3.12). Welch F test results (F= 2.507, 53 The Welch F Test was used as the Levene Statistic (6.453, p < .05) revealed the variances of the mean science values between response categories were not equal. 70 df=3, p > .05) 54 did not indicate a statistically significant relationship between age and support for advocacy. Table 3.12. Support for advocacy vs. age (n=808). Advocacy Model n mean age Traditional 84 29.40 Science Communication 204 33.43 Expert Advocacy 403 31.90 Political Advocacy 117 29.49 Total 808 31.67 F= 2.507, df=3, p > .05 Similarly, further analysis of support for advocacy vs. age by sample group did not reveal any visible trends or statistically significant differences for the ABCFP, APB, faculty, or student sample groups. 3.2.2.4 Gender With regard to gender, Males were expected to be more supportive of higher levels of advocacy than females. A cross-tabulation of models of advocacy with gender indicates a slightly higher proportion of females supported the first model, and a slightly higher proportion of males supported the forth model (Table 3.13). However, no statistically significant differences were identified for the complete sample, or when examined by sample source. Table 3.13 Support for advocacy vs. gender (n=830). Advocacy Model male female total Traditional 34 8.0% 50 12.3% 84 10.1% Science Communication 116 27.4 % 95 23.3% 211 25.4% Expert Advocacy 209 49.4% 206 50.6% 415 50.0% Political Advocacy 64 15.1% 56 13.8% 120 14.5% Total 423 100 % 407 100% 830 100.0% 2= 5.386, df= 3, p > .05 54 The Welch F Test was used as the Levene Statistic (5.153, p < .05) revealed the variances of the mean science values between response categories were not equal. 71 3.2.2.5 Education Respondents with higher levels of education were hypothesized to support higher models of advocacy. Chi-square test of independence results (2= 22.949, df= 12, p < .05) indicate there were significant differences between levels of education and support for different models of advocacy for the sample as a whole, although Cramer’s V (.096) suggests a weak association (Table 3.14). Lower proportions of respondents with higher levels of education support the lowest model of advocacy. Table 3.14. Support for advocacy vs. education (n= 836). Advocacy Model some post secondary completed post secondary some graduate school completed masters degree completed doctoral degree Total Traditional 41 11.1% 30 12.9% 1 5.6% 1 1.4% 13 8.8% 86 10.3% Science Communication 86 23.3% 62 26.7% 5 27.8% 24 34.8% 36 24.3% 213 25.5% Expert Advocacy 176 47.7% 117 50.4% 11 61.1% 39 56.5% 74 50.0% 417 49.9% Political Advocacy 66 17.9% 23 9.9% 1 5.6% 5 7.2% 25 16.9% 120 14.4% Total 369 100% 232 100% 18 100% 69 100% 148 100% 836 100% 2= 22.949, df= 12 , p < 0.05 When analyzed by sample group, statistically significant differences were observed for APB members 55 (2 = 17.513, df = 9, p < 0.05) with Cramer’s V (.228) indicating a moderate association (Table 3.15). 55 The UBC Faculty member sample was not analyzed, as level of education is a constant for that sample group. 72 Table 3.15. Support for advocacy vs. education (APB sample, n=112). Advocacy Model some post secondary completed post secondary some graduate school completed masters degree completed doctoral degree total Traditional 0 3 7.5% 0 1 2.0% 0 4 3.6% Science Communication 0 10 25.0% 3 33.3% 17 34.7% 2 33.3% 34 30.4% Expert Advocacy 0 26 65.0% 6 66.7% 29 59.2% 4 66.7% 67 59.8% Political Advocacy 0 1 2.5% 0 2 4.1% 0 7 6.2% Total 0 40 100% 9 100% 49 100% 14 100% 112 100% 2 = 17.513 df = 9, p < 0.05 The bivariate results for the complete sample and the APB sample group, however there is no visible increase in support for advocacy from lower to higher categories of education. 3.2.2.6 Political Affiliation A cross-tabulation of advocacy models and political affiliation (right, center, left) indicates that generally, higher frequencies of “right” respondents support lower models of advocacy, and higher frequencies of “left” respondents support higher models (Table 3.16). Chi-square test of independence results (2 = 17.881, df= 6, p < .05) indicated the presence of statistically significant differences, with Cramer’s V (.135) indicating a weak association. Table 3.16 Support for advocacy vs. political affiliation (n= 492). Advocacy Model right center left total Traditional 17 18.5% 13 8.0% 16 6.8% 46 9.3% Science Communication 30 32.6% 40 11.1% 55 23.2% 125 25.4% Expert Advocacy 38 41.3% 91 55.8% 133 56.1% 262 53.3% Political Advocacy 7 7.6% 19 11.7% 33 13.9% 59 12.0% Total 92 100% 163 100% 237 100% 492 100% 2 = 17.881, df= 6, p < .05 When analyzed by sample source, statistically significant differences were found only within the faculty group. Within the faculty sample, no ‘left’ affiliated respondents support the 73 traditional model, with more than 70% of respondents from the ‘center’ and ‘left’ categories supporting the expert or political advocacy models (Table 3.17). Chi-square test of independence results (2 = 19.219, df= 6, p < .05) indicated the presence of statistically significant differences with Cramer’s V (.363) indicating a strong association. Within the student sample, lower frequencies of ‘center’ and ‘left’ – affiliated respondents supported the traditional model. In the ABCFP and APB groups, it was observed that no respondents with a ‘right’ political affiliation supported political advocacy. No statistically significant differences were found in these sample groups. Table 3.17. Support for advocacy vs. political affiliation (faculty sample, n=73). Advocacy Model Right Center Left Total Traditional 3 60.0% 4 8.9% 0 7 9.6% Science Communication 1 20.0% 8 17.8% 6 26.1% 15 20.5% Expert Advocacy 0 27 60.0% 13 56.5% 40 54.8% Political Advocacy 1 20.0% 6 13.3% 4 17.4% 11 15.1% Total 5 100% 45 100% 23 100% 73 100% 2 = 19.219, df= 6, p < .05 Overall, the bivariate results for the complete sample and the faculty sample support the prediction that respondents affiliated with the political left will be more likely to support higher models of advocacy, and those on the political right less likely. 3.2.2.7 Source of Employment It was hypothesized that respondents employed by industry would be less likely to support higher levels of advocacy. Bivariate results did not support this hypothesis. Chi-square test of independence results did not indicate significant differences between source of employment (industry, consulting, government, academic) and support for advocacy (2 = 10.002, df= 9, p > .05). By sample source, no significant differences were found in the ABCFP (2 = 5.094, df= 9, p > .05) or APB groups (2 = 11.724, df = 9, p > .05). 74 3.2.2.8 Faculty Significant differences were expected between faculties of study for the faculty and student samples. No statistically significant differences were found for support for advocacy vs. faculty in the Student sample (2 = 11.011 df= 12, p > .05), and Cramer’s V (.084) indicated a weak association. A cross-tabulation of support for advocacy vs. faculty for the Faculty sample indicates that a much higher frequency of respondents from the Faculty of Science support political advocacy than respondents from the Faculty of Forestry, where higher frequencies of respondents support lower models (Table 3.18). Chi-square test of independence results (2 = 17.065 df = 6, p < .05) indicate the presence of statistically significant differences and Cramer’s V (.261) indicated a moderately strong association. Table 3.18. Support for advocacy vs. faculty (faculty sample, n=125). Advocacy Models Forestry LFS/IRES Science Total Traditional 4 16.0% 3 15.0% 5 6.2% 12 9.6% Science Communication 12 48.0% 2 10.0% 15 18.8% 29 23.2% Expert Advocacy 8 32.0% 12 60.0% 43 53.8% 63 50.4% Political Advocacy 1 4.0% 3 15.0% 17 21.2% 21 16.8% Total 25 100% 20 100% 80 100% 125 100% 2 = 17.065 df = 6, p < .05, Cramer’s V= .261 A large proportion of Faculty of Science members supported the political advocacy model compared to respondents from the Faculty of Forestry, and the results above suggest significant differences between the faculties. Therefore, support for advocacy was further examined by department (Tables 3.19- 3.21). The majority of respondents from the Faculty of Forestry supported the science communication model. Expert advocacy was the second most supported model. The one respondent from this faculty that supported ‘political advocacy’ was from the Department of Forest Sciences. No respondents from the Department of Wood Science supported expert advocacy or political advocacy (Table 3.19). There were no significant differences in support for advocacy between departments in this faculty (2 = 12.689, df= 9, p > .05). 75 Table 3.19 Support for advocacy: Faculty of Forestry member frequencies by department (n=24). Forest Resources Management Forest Sciences Wood Science Traditional 2 (18%) 0 1 (25%) Science Communication 6 (55%) 3 (33%) 3 (75%) Expert Advocacy 3 (27%) 5 (56%) 0 Political Advocacy 0 1 (11%) 0 Total 11 9 4 2 = 12.689, df= 9, p > .05, Cramer’s V= .411 Expert advocacy was the most supported model by the Faculty of Science. However, more than one-fifth of the Faculty of Science (21.2%) supported the ‘political advocacy’ model. A cross-tabulation of support for advocacy by Faculty of Science departments (Table 3.20) indicates that proportions of faculty members from the Departments of Zoology, Microbiology and Immunology, Physics and Astronomy, Chemistry, Math, and Computer Science supported the highest model of advocacy (political advocacy). Chi-squared results indicate that there were significant differences in support for advocacy between departments in the Faculty of Science (2 = 53.426, df= 27, p < .05, Cramer’s V = .472). Expert advocacy was also the most supported model for the ‘other’ faculty category (LFS, IRES, Fisheries) (Table 3.21). No significant differences between departments were identified (2 = 10.456, df= 6, p > .05). 76 Table 3.20 Support for advocacy: Faculty of Science member frequencies by department (n= 78). Botany Zoology Microbiology and Immunology Earth and Ocean Science Physics and Astronomy Chemistry Math Statistics Computer Science Traditional 0 0 0 1 (8%) 1 (6%) 0 0 3 (75%) 0 Science Communication 2 (40%) 2 (18%) 0 4 (33%) 4 (24%) 1 (14%) 1 (10%) 0 0 Expert Advocacy 3 (60%) 7 (64%) 3 (60%) 7 (58%) 8 (47%) 4 (47%) 5 (50%) 1 (25%) 5 (63%) Political Advocacy 0 2 (18%) 2 (40%) 0 4 (24%) 2 (29%) 4 (40%) 0 3 (38%) Total 5 11 5 12 17 6 10 4 8 2 = 53.426, df= 27, p < .05, Cramer’s V = .472 Table 3.21 Support for Advocacy: “other” faculty members by department (n=24). Land and Food Systems Institute for Resources, Environment and Sustainability Fisheries Traditional 2 (17%) 1 (14%) 0 Science Communication 0 2 (18%) 0 Expert Advocacy 9 (75%) 3 (43%) 0 Political Advocacy 1 (8%) 1 (14%) 1 (100%) Total 12 7 1 2 = 10.456, df= 6 , p > .05 Cramer’s V= .511 77 The results for ‘faculty’ were not consistent with expectations that academic scientists studying more applied/ theoretical sciences may be less likely to support higher models of advocacy than those studying more natural or multidisciplinary sciences. 3.2.2.9 Bivariate Summary Summaries of the bivariate analyses of support for advocacy with the various dependent variables of interest are provided for the complete sample (Table 3.22), and by sample source (Table 3.23, Table 3.24, Table 3.25). 56 Table 3.22 Bivariate summary: support for advocacy (statistically significant relationships in bold). Variables Analysis Description Sample Source 2 = 19.736, df = 9, p < .05, Cramer’s V= .089 Significant differences, weak association - ABCFP members and students most supportive of traditional model. - ABCFP and APB members least supportive of political advocacy. Science Values F= 7.350 p < .05 (Welch F) Significant differences - Respondents that supported the traditional model have significantly lower mean science values, than respondents supporting expert and political advocacy. - Respondents that supported the science communication model had significantly lower science values than those who supported the expert advocacy model. Environmental Values (NEP) F(3, 569) = 5.624 p < .05 Significant differences - Respondents with higher mean NEP values significantly more supportive of expert advocacy than traditional and science communication models Age F= 2.507 p > .05 (Welch F) No significant differences ----- Gender 2= 5.386, df= 3, p > .05, No significant differences ----- Education 2 = 22.949, df = 12 p < .05, Cramer’s V = .096 Significant differences, weak association ----- Political Affiliation 2 = 17.881, df = 6 , p < .05, Cramer’s V= 0.135 Significant differences, weak association - “Right” more supportive of lower models of advocacy, “left” more supportive of higher 56 No statistically significant bivariate relationships were found for the ABCFP sample. No ABCFP summary table is provided. 78 Table 3.23. Bivariate summary: support for advocacy (APB sample, statistically significant relationships in bold). Variables Analysis Description Science Values F= 7.880 p < .05 (Welch F) Significant differences - Respondents in support of traditional model have significantly lower mean science values than supporters of expert and political advocacy. - Respondents that supportive of science communication model had significantly lower science values than those in support of expert advocacy model. Environmental Values (NEP) F= 3.405 p > .05 (Welch F) No significant differences ----- Age F (3,105) = 1.423 p > .05 No significant differences ----- Gender 2= 4.617, df= 3, p > .05, Phi = .203 No significant differences, ----- Education 2 = 17.513 df = 9, p < 0.05, Cramer’s V =.228 Significant differences, moderate association ----- Political Affiliation 2 = 5.531, df = 6 , p > .05, Cramer’s V= 0.171 No significant differences, weak association ----- Source of Employment 2 = 11.724, df = 9, p > .05 Cramer’s V= .229 No significant differences, moderate association ----- 79 Table 3.24. Bivariate summary: support for advocacy (faculty sample, statistically significant relationships in bold). Variables Analysis Description Science Values F (3,103)= p > .05 (Welch F) No significant differences ----- Environmental Values (NEP) F= 1.583, p < .05 Significant differences - post hoc tests could not identify location of differences Age F = 1.867 p > .05 (Welch F) No significant differences ----- Gender 2= 2.203, df= 3, p > .05, Phi = .133 No significant differences, weak association ----- Political Affiliation 2 = 19.291, df = 6 , p < .05, Cramer’s V= .363 Significant differences, very strong association - no “left”- affiliated respondents support traditional model - more than 70% of respondents from the “center” and “left” categories supporting the expert or political advocacy models Faculty 2 = 17.065 df = 6 p < .05 Cramer’s V= .261 Significant differences, moderately strong association - a higher frequency of Faculty of Science respondents support political advocacy - a higher frequency of Faculty of Forestry respondents support lower models (traditional, science communication) Table 3.25. Bivariate summary: support for advocacy (Student sample, statistically significant relationships in bold). Variables Analysis Description Science Values F(3,387)= 4.216, p < .05 Significant differences - Respondents that supported the traditional model had significantly lower science values than supports of expert advocacy Environmental Values (NEP) F(3,347)= 2.014, p > .05 No significant differences ----- Age F (3,522) = .848 p > .05 No significant differences ----- Gender 2= 5.893, df= 3, p > .05, Phi = .106 No significant differences, weak association ----- Education 2 = 7.432, df = 12, p > .05, Cramer’s V= .068 No significant differences, very weak association ----- Political Affiliation 2 = 11.229 , df = 6 , p > .05, Cramer’s V= .144 No significant differences, weak association ----- Faculty 2 = 11.011 df = 12, p > .05, Cramer’s V= .146 No significant differences, weak association ----- 80 3.2.3 Ordinal Regression (Dependent Variable: Support for Advocacy) 3.2.3.1 Ordinal Regression Overview Ordinal regression was chosen as the method of multivariate analysis as the outcome variable ‘support for advocacy’ is ordinal. While multinomial logistic regression is another possible method for multivariate data analysis, it ignores any value the dependant variable might have, treating the values as nominal (Norusis 2012). Unlike logistic regression, where the probability of an event is measured, ordinal regression is concerned with cumulative probabilities (the probability of an event and all others above it in an ordinal rank), and considers the effects of a set of explanatory variables across cumulative splits in the outcome. The primary assumption of ordinal regression is that the effects of explanatory variables are the same across different thresholds (the shift between one level in the outcome variable and the next), the assumption of proportional odds. Similar to logistic regression, ordinal regression coefficients are expressed as logits (log-odds), the natural logarithm of the odds. The exponents of these values are taken to calculate the odds. Additionally, like logistic regression, pseudo R 2 values are used as the outcome variable is not continuous, and it is not possible to calculate the same R 2 statistic as in linear regression (SRME 2012). When assessing an ordinal regression, model-fitting information, goodness of fit results, and a test of parallel lines must be taken into account to evaluate the validity of the model. A model-fit test compares the baseline model with the current model including explanatory variables, to test whether or not the additional of explanatory variables results in a statistically significant improvement in the model’s ability to predict the outcome variable (Chi-square statistic). A significant test (p < .05) indicates an improvement in the model. The goodness of fit test (Chi- square statistic) determines whether the observed data is consistent with the model it has been fitted to. The null hypothesis is that the model is a good fit, therefore if the test is significant (p < .05), the model is not a good fit (SRME 2012). The test of parallel lines tests the null hypothesis that the odds are proportional. If the null hypothesis is rejected (p < .05), the relationships between the logits and independent variables are not proportional for all logits (Norusis 2012). However, the test of parallel lines is known to be very conservative and sensitive to continuous 81 variables. The use of individual binary logistic regressions are recommended to explore the odds in the event the assumption is not met (SRME 2012). 3.2.3.2 Support for Advocacy Ordinal Regression Models Tables 3.26-3.30 present the results of a series of ordinal regression models that were fitted to each individual group, and the sample as a whole. The upper section of each table denotes the “thresholds” for support for advocacy (dependent/outcome variable), representing the intercept equivalent terms (j). Three thresholds are derived from the cumulative transitions between the four categories (traditional, science communication, expert advocacy, political advocacy), as SPSS models the probability of achieving each level or below. These threshold parameters are not interpreted directly, but can be used for the prediction of individual cases (SRME 2012). The middle section of each table presents the logits and statistical significance of the explanatory variables. The covariates (continuous variables) are followed by the factors (categorical variables). The last category of each categorical explanatory variable is the reference category; 57 therefore, the logit is 0. 58 The final section of the tables includes two pseudo R 2 measures (Nagelkerke and Snell-Cox) to estimate the % variance explained by the independent variables in the model, and the results of a series of diagnostic tests: model fit, goodness of fit, and parallel lines tests. A check mark indicates an improved model (model fit), a good fit (goodness of fit test), or confirms the assumption of proportional odds. 3.2.3.3 ABCFP Members No statistically significant relationships were observed in the bivariate analyses of support for advocacy and potential explanatory variables for the ABCFP member sample (n= 68). For this sample group, a number of explanatory variables that demonstrated noticeable patterns in the preceding analyses were added into ordinal regression models (Table 3.26). 57 SPSS predicts the change in the odds ratio from the reference category to the other categories for a shift of one level (threshold) in the outcome variable. 58 As recommended by SRME (2012), categorical variables were recoded so the categories with the largest ‘n’ serve as reference categories. 82 Science values were added to the baseline for Model 1, 59 and shown to be statistically significant with the logit suggesting a positive relationship with support for advocacy, consistent with expectations. The Negelkerke pseudo R 2 estimate for this model was .081. For Model 2, NEP scores were added. 60 While the addition of NEP scores increased the Negelkerke Pseudo R 2 value to .121, the model fit became insignificant (p= 0.80) indicating that Model 2 does not give an improvement over the baseline model. For Model 3, NEP scores were removed and political affiliation was added as an explanatory variable, 61 however the model fit remained poor, and science values became insignificant. Table 3.26. Support for advocacy ordinal regression models (ABCFP sample). Explanatory Variables Model 1 Model 2 Model 3 Support for Advocacy 1 3.971 5.936 5.884 2 5.857* 7.881* 7.551 3 8.429** 10.145** 9.720* Science Value Scores 1.716* 1.724* 1.556 NEP Scores - .591 .742 Political Affiliation Right - - -.334 Center - - .014 Left - - 0 Pseudo R 2 S-C .081 .111 .108 Negelkerke .089 .121 .117 Model fit 4.995* 5.056* 3.889 Goodness of Fit 40.093 108.154 92.955 Proportional Odds .136 1.198 6.632 Valid n 59 43 34 *p < .05, **p < .001 Model 1 was found to be the best ordinal regression model for the ABCFP member sample. The model fit test confirmed that the final model gives a significant improvement over the baseline, an insignificant goodness of fit test implied a good fit, and a non-significant parallel lines test confirmed the proportional odds assumption was satisfied. Negelkerke's pseudo R 2 estimated that the model explains 8.9% of the variance in preferred levels of advocacy. Science value scores, 59 The Science value score vs. Support for Advocacy ANOVA was not significant. However, mean science value scores were observed to increase with support for advocacy. 60 The NEP score vs. Support for Advocacy ANOVA was not significant. However, mean NEP scores were observed to increase for models 2-4. 61 A political affiliation vs. support for advocacy Chi-square test of independence did not reveal significant differences. However, a cross-tabulation indicated higher frequencies of “right” ABCFP members supported lower models of advocacy than “left”, and vice versa. 83 the only explanatory variable in this model, were found to be a statistically significant predictor of the outcome variable, with the odds of supporting a higher level of advocacy increasing by 5.56 for each unit increase in science value score. 62 These findings supported the prediction that respondents with higher science values are more likely to support higher levels of advocacy (positive relationship). 3.2.3.4 APB Members Science value scores and education were added into the APB sample (n=112) Model 1 as explanatory variables (Table 3.27). 63 Model 1 yielded positive model fit, goodness of fit, and parallel lines tests. Negelkerke's pseudo R 2 was estimated at .210 and science values were extremely significant (p = .000). Political affiliation was added as an explanatory variable in Model 2. Tests remained positive, and science values statistically significant; however the pseudo R 2 value dropped by .001, suggesting the effect of political affiliation is negligible for this sample group. NEP scores were added in the third model. Science values became less significant in Model 3, and Negelkerke’s pseudo R 2 rose to .252, however the goodness of fit test indicated the model was a poor fit, and the significant parallel lines test indicated the odds were not proportional, diminishing the quality of the model. For Model 4, education was removed and the R 2 estimate decreased slightly. The odds became proportional (as indicated by a positive parallel lines test); however the goodness of fit results remained poor. Model 5 consisted solely of science value scores to explore the influence of education as an explanatory variable (in comparison with Model 1). The pseudo R 2 estimates were lower suggesting that while education is not statistically significant, it improves the model. 62 Exp (1.716) = 5.56 63 Science value scores and education were shown to have significant relationships with ‘support for advocacy’ in the preceding bivariate analyses. 84 Table 3.27. Support for advocacy ordinal regression model (APB Sample). Explanatory Variables Model 1 Model 2 Model 3 Model 4 Model 5 Support for Advocacy 1 9.019* 8.164* 11.471* 9.745* 8.393* 2 12.013** 11.338** 14.455** 12.651* 11.367** 3 16.003** 15.384** 18.858** 16.985** 15.297** Science Value Scores 3.458** 3.296** 3.554** 3.226** 3.356** NEP Scores - - .542 .515 - Political Affiliation Right - -.445 -.259 -.383 - Center - .486 .243 .306 - Left - 0 0 0 - Education Phd .621 -.046 .066 - - Masters .327 .288 .664 - - Some Grad .773 .410 1.032 - - Post-2 nd 0 0 0 - - Pseudo R 2 S-C Negelkerke .178 .176 .211 .189 .167 .210 .209 .252 .225 .196 Model fit 19.840* 17.077* 17.563* 15.523* 18.423 Goodness of Fit 81.104 138.331 258.000* 275.028 27.628 Proportional Odds 10.530 16.181 33.200* 3.909 1.893 Valid n 101 88 74 74 101 *p < .05, **p < .001 Science values were significant across all models suggesting that, as expected, science values have a positive relationship with support for advocacy. The values of the logits remained relatively similar across the models with the exception of Model 3, which exhibited poor goodness of fit and test of parallel lines results. Model 1 was determined to be the best ordinal regression model for the APB sample group. The model fit test confirmed that the final model gives a significant improvement over the baseline, an insignificant goodness of fit test implied a good fit, and the parallel lines test confirmed the proportional odds assumption was satisfied. Negelkerke's pseudo R 2 estimated that the model explains 21.0% of the variance in preferred levels of advocacy. Science value scores were significant, with the odds of supporting a higher level of advocacy calculated to increase by 31.5 for each unit increase in science value score, supporting the expectation that respondents with higher science values are more likely to support higher levels of advocacy. 64 While not significant in multivariate models, the addition of education as an explanatory variable improved the model. 64 Exp(3.450)= 31.50 85 3.2.3.5 Faculty Members Science value scores, NEP scores, political affiliation, and faculty were added into the Faculty Member sample (n= 127) Model 1 as explanatory variables (Table 3.28). 65 In this model, no explanatory variables were significant, the model fit indicated there was no significant improvement over the baseline (intercept) model, and Negelkerke's Pseudo R 2 estimate was low (.074). NEP scores were removed for Model 2. This did not improve the model as it resulted in a decreased Negelkerke's pseudo R 2 estimate (.062) and a violation in the assumption of proportional odds. No explanatory variables were significant. Table 3.28. Support for advocacy ordinal regression models (faculty sample). Explanatory Variables Model 1 Model 2 Model 3 Model 4 Model 5 Support for Advocacy 1 3.195 1.977 -3.120** -1.620 1.356 2 5.202 4.090 -1.303* .078 3.035 3 7.502 6.738 1.420* 2.572 5.609** Science Value Scores 1.050 1.425 - - - NEP Scores .664 - - - 1.238* Political Affiliation Right .146 -.707 -2.976* - - Center .090 -.225 -.242 - - Left 0 0 0 - - Faculty Forestry -.604 -.600 -.628 -1.408* -.891 LFS/IRES -.042 .066 -.174 -.072 .194 Science 0 0 0 0 0 Pseudo R 2 S-C .067 .055 .115 .093 .134 Negelkerke .074 .062 .128 .102 .148 Model fit 3.383 3.403 8.831 10.331* 12.553* Goodness of Fit 124.914 78.922 87.357** 62.579 121.958 Proportional Odds 19.030 21.542* 48.241** 26.805** 3.990 Valid n 49 60 72 106 87 *p < .05, **p < .001 In Model 3, science value scores were also removed. With both continuous variables removed from the mode, right political affiliation became significant and pseudo R 2 estimates increased. The magnitude of the significant negative logit for right political affiliation in this model suggested faculty members with right political affiliation were 20 times more likely to support lower levels of advocacy than those with left political affiliation. 66 However, all three test results 65 These variables were shown to have significant relationships with ‘support for advocacy’ in the preceding bivariate analyses. 66 Exp(-2.976)= .050, 1/.050= 20 86 (model fit, goodness of fit, and test of parallel lines) had negative results suggesting the model did not provide a reliable prediction of the outcome variables. For Model 4, political affiliation was removed, leaving faculty as the only remaining explanatory variable. The Negelkerke pseudo R 2 estimate suggested faculty alone contributes to approximately 10% of the variance observed in the model. The Faculty of Forestry became significant at the .005 level, with the statistically significant negative logit suggesting that professors from the Faculty of Forestry were four times more likely than professors from the Faculty of Science to support lower models of advocacy. 67 However, this model was not viable as the assumption of proportional odds was violated. Two of the three models that involved political affiliation suggested that, as expected, there is a negative relationship between support for advocacy and right political affiliation. The observed logits for ‘faculty’ suggest members of the Faculty of Forestry are less likely to support higher levels of advocacy than members of the Faculty of Science (reference group), building on the prior bivariate analysis (Table 3.17). Model 5, with NEP scores and faculty as explanatory variables, was determined to be the best ordinal regression model for the Faculty member sample group. The model fit test confirmed that the final model gives a significant improvement over the baseline, an insignificant goodness of fit test implied a good fit, and a significant parallel lines test confirmed the proportional odds assumption was satisfied. Negelkerke's pseudo R 2 estimated that the model explained 14.8% of the variance in preferred levels of advocacy. NEP scores were determined to be a significant predictor of the outcome variable, consistent with bivariate results, and the prediction that NEP scores will have a positive relationship with support for advocacy. Model 5 predicts the odds of supporting a higher level of advocacy will increase by 3.45 for each unit increase in NEP score. 68 67 Exp(-1.408)= .245, 1/.245 = 4.08 68 Exp(1.238)= 3.45 87 3.2.3.6 Students For the Student sample (n= 529), Model 1 (Table 3.29) was composed solely of science value scores, the only variable shown to be statistically significant in the prior bivariate analyses (Table 3.9). While science value scores were significant, the pseudo R 2 estimate was low (.026). In an attempt to improve the pseudo R 2 estimate, political affiliation was added for Model 2. In this model, science value scores remained significant, right political affiliation became significant, and the Negelkerke pseudo R 2 estimate increased (.065). For Model 3, education was added as an explanatory variable and was found to improve the model as the Negelkerke pseudo R 2 estimate increased (.073). In Model 4, faculty was added as an explanatory variable, decreasing the pseudo R 2 estimate very slightly (.072), but an insignificant model fit test compromised the viability of the model. Table 3.29. Support for advocacy ordinal regression model (student sample). Model 1 Model 2 Model 3 Model 4 Support for Advocacy 1 .333 .383 .202 .095 2 1.738* 1.862 1.690 1.618 3 4.146** 4.683* 4.529*** 4.427** Science Value Scores .706* .851* .840** .861* NEP Scores - - - - Political Affiliation Right - -.802* -.864* -.804* Center - -.527 -.571 -.598 Left - 0 0 0 Education PhD - - .170 - Masters - - -.834 -.844 Some Grad - - .445 .370 Post-2nd - - -.289 -.309 Some Post- 2 nd - - 0 0 Faculty Forestry - - - -.252 Science - - - -.105 Arts - - - -.132 Ap. Sci - - - 0 Pseudo R 2 S-C .023 .058 .065 .065 Neg. .026 .065 .073 .072 Model fit 9.293* 12.834* 14.427* 14.075 Goodness of Fit 56.141 113.683 194.274 351.224 Proportional Odds 2.935 3.262 8.409 10.070 Valid n 391 213 213 209 *p < .05, **p < .001 88 The effects of science values scores and right political affiliation are observed across all models, confirming expectations that support for advocacy will have a positive relationship with science value scores, and a negative relationship with right political affiliation. Model 3 was determined to be the best ordinal regression model for the student sample. The model fit test confirmed that the final model gives a significant improvement over the baseline, an insignificant goodness of fit test implied a good fit, and a significant parallel lines test confirmed the proportional odds assumption was satisfied. Negelkerke's pseudo R 2 estimated that the model explains 7.3% of the variance in preferred levels of advocacy. Science value scores were a statistically significant predictor of the outcome variable with the odds of supporting a higher level of advocacy expected to increase by 2.31 for each unit increase in science value score. 69 Political affiliation was also statistically significant. The model estimates that respondents from the student sample with ‘right’ political affiliation are almost 2.5 times as likely to support lower levels of advocacy. 70 3.2.3.7 Complete Sample All variables of interest shown to have significant relationships with ‘support for advocacy’ in the previous series of bivariate analyses (section 3.2.2) were added into Model 1 for the complete sample (n= 836) (Table 3.30). The explanatory variables were: science value scores, NEP scores, political affiliation, sample source, and education. While the model fit and goodness of fit results were favorable, and the model indicated a statistically significant positive relationship between science values and support for advocacy, the Negelkerke pseudo R 2 value was low (.073), and the test of parallel lines was significant, indicating the assumption of proportional odds has been violated. 69 Exp(.840)= 2.31 70 Exp(-.864)= .042, 1/.42 = 2.38 89 Table 3.30. Support for advocacy ordinal regression models (complete sample). Explanatory Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Support for Advocacy 1 1.065 1.204 1.341 1.012 1.250 1.070 2 2.801 2.964** 3.045** 2.633** 2.995** 2.826* 3 5.618** 5.857** 5.984** 5.184** 5.861** 5.712** Science Value Scores .914** 1.168** 1.178** .974** 1.096** 1.127** NEP Scores .231 - - - - - Political Affiliation Right -.618 -.734** -.737* - -.740* -.764* Center -.157 -.297 -.268 - -.185 -.278 Left 0 0 0 - 0 0 Sample ABCFP -.641 -.395 -.476 -.607* - - APB -.503 -.371 -.390 -.353 - - Faculty -.349 -.277 -.075 -.178 - - Student 0 0 0 0 - - Education PhD .102 .078 - - - -.174 Masters -.017 -.060 - - - -.411 Some Grad .260 .017 - - - -.277 Post-2 nd .302 -.255 - - - -.431 Some Post-2 nd 0 0 - - - 0 Pseudo R 2 S-C .066 .075 .072 .042 .063 .071 Negekerke .073 .084 .080 .046 .070 .080 Model fit 21.849* 31.781** 30.436 28.263 26.565* 30.222* Goodness of Fit 897.268 471.747 318.723 164.646 126.939 337.258 Paralell lines 36.675* 28.183 15.072 21.549* 2.344 21.179 Valid n 322 408 408 658 408 408 *p < .05, **p < .001 For model 2, NEP scores were removed, and the model was improved, as indicated by the non- significant parallel lines test and the slight increase in the Negelkerke pseudo R 2 value (.084). Science values remained statistically significant, and ‘right’ political affiliation became significant, suggesting a negative relationship between political affiliation and support for advocacy. Subsequent models (3-6), generated by different combinations of explanatory variables, did not improve pseudo R 2 values. However, science values were statistically significant across all models (p = .000) with the positive coefficients supporting the expectation that science values have a positive relationship with support for advocacy. Additionally, “right” political affiliation was statistically significant in four of the five models in which it was entered (p < .005). The negative logits varied only slightly between models supporting the expectation that political affiliation is negatively associated with support for advocacy. In model 3, the ABCFP sample 90 source category became significant (p < .05), suggesting forest professionals are less likely than students (the reference group) to support higher levels of advocacy, consistent with the prediction that ABCFP members would have lower support for advocacy. However, the model itself was not viable as the pseudo R 2 values were substantially diminished and the assumption of proportional odds was not met. The diagnostic tests suggest that Model 2 was the best model for the complete sample. The explanatory variables were: science value scores, political affiliation, sample source, and education. Science values and right political affiliation were found to be statistically significant predictors of the outcome variable. The model fit test confirmed that the final model gives a significant improvement over the baseline, an insignificant goodness of fit test indicated the fit was good, and a significant parallel lines test confirmed the proportional odds assumption was satisfied. Negelkerke's Pseudo R 2 estimated that the model explains 8.4% of the variance in preferred levels of advocacy. The coefficients for the statistically significant variables indicated that the odds of supporting a higher level of advocacy increase by 3.21 for each unit increase in science value score 71, and respondents from the complete sample with ‘right’ political affiliation are more than twice as likely to support lower levels of advocacy. 72 These findings are consistent with the expectation that respondents with higher science values are more likely to support higher levels of advocacy (positive relationship), and that respondents with ‘right’ political affiliation will be less likely to support higher models of advocacy than respondents with a ‘left’ political affiliation. 3.2.3.8 Summary Table 3.31 presents a summary of the most viable ordinal regression models determined for the complete sample, and each group individually. Science value scores and right political affiliation were the most common statistically significant predictors of the outcome variable. Science value scores were predicted to increase with support for higher levels of advocacy for the complete 71 Exp(1.168)= 3.21 72 Exp(-.734)= 0.48, 1/0.48 = 2.08 91 sample, and three of the four sample groups, supporting the prediction that science values will be positively associated with support for advocacy. Science values scores were not a statistically significant predictor of the outcome variable for the faculty member sample, however, NEP scores were determined to be to be a predictor for this group, consistent with the expectation that NEP scores will be positively associated with support for advocacy. Right political affiliation was, as expected, observed to have a negative relationship with support for advocacy, and was statistically significant predictor of the dependent variable in the complete sample, and the student group. Table 3.31 Support for advocacy ordinal regression summary. Sample Groups n R 2** Explanatory Variables Description ABCFP Members 59 .089 - Science value scores* - Odds of supporting a higher level of advocacy increase by 5.56 for each unit increase in science value score APB Members 101 .210 - Science value scores* - Education - Odds of supporting a higher level of advocacy increase by 31.5 for each unit increase in science value score Faculty Members 87 .148 - NEP scores* - Faculty - Odds of supporting a higher level of advocacy predicted to increase by 3.45 for each unit increase in NEP score Students 213 .073 - Science value scores* - Political affiliation* - Education - Odds of supporting a higher level of advocacy expected to increase by 2.31 for each unit increase in science value score. - Respondents with “right” political affiliation are almost 2.5 times as likely to support lower levels of advocacy. Complete Sample 408 .084 - Science value scores* - Political affiliation* - Sample source - Education - Odds of supporting a higher level of advocacy increase by 3.21 for each unit increase in science value score - Respondents with “right” political affiliation more than twice as likely to support lower levels of advocacy * statistically significant predictor of the outcome variable ** Negelkerke Pseudo R2 estimates Significant effects were observed for the two continuous variables (science value scores, NEP scores) in the multivariate analyses in one or more sample groups. Figure 3.1 graphically depicts the influence of science values across advocacy models, comparing the complete sample, and the four sample groups. Groups with a significant positive 92 relationship (ABCFP members, APB members, students, and the complete sample) are denoted by a dotted outline. The large odds observed for the APB member sample are evident here, highlighting the strong positive relationship for this sample group, consistent with the expectation that individuals with higher science values would support higher levels of advocacy. Although faculty members have the highest overall science value scores, it is apparent in this figure that the expected relationship between science values and support for advocacy does not hold for this sample group. Figure 3.1 Support for advocacy vs. science values Figure 3.2 graphically displays the influence of NEP scores across advocacy models, comparing the complete sample, and the four sample groups. While APB members and students had the highest NEP scores, no significant positive relationships were observed between NEP scores and support for advocacy. Looking at the complete sample, a positive trend is observed, but was not found to be statistically significant in the multivariate ordinal regression models. The expectation that NEP values will be positively associated with support for advocacy holds only for the faculty member sample, as illustrated below. 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 Traditional Science Communication Expert Advocacy Political Advocacy S ci en ce V a lu e S co re s Support for Advocacy ABCFP APB Faculty Students Complete Sample 93 Figure 3.2. Support for advocacy vs. NEP scores. 3.3 Credibility 3.3.1 Perceived Credibility Respondents were presented with hypothetical situations of scientist advocacy in survey questions 28 -35 (scenarios 1-8). The situations corresponded with models of advocacy/policy advocacy traits (Table 2.3). Respondents were and asked to indicate their perceived credibility of the scientist in each situation as not credible (1), somewhat not credible (2), somewhat credible (3), or credible (4). Table 3.32 presents the frequencies (%) and descriptive statistics for the eight scenarios. 2.6 2.8 3 3.2 3.4 3.6 Traditional Science Communication Expert Advocacy Political Advocacy N E P S co re s Support for Advocacy ABCFP ABP Faculty Students Complete Sample 94 Table 3.32 Perceived credibility frequencies (%) and descriptive statistics (n= 836, mode responses in bold). Credibility Scenerio Not Credible (1) Somewhat Not Credible (2) Somewhat Credible (3) Credible (4) Don’t know Mean 95% CI SD 1. A scientist measures heavy metal accumulation in a wetland area receiving acid mine drainage from an abandoned copper mine. The results are published in the Journal of Ecological Engineering. 0.2 1.7 17.8 76.7 3.6 3.77 * .033 .475 2. The results of a study done by a team of wildlife biologists evaluating habitat fragmentation as a result of above-ground pipeline construction are brought to the attention of relevant managers, such as Environment Canada. - 3.8 31.7 60.8 3.7 3.59 .040 .567 3. A scientist specializing in watershed biogeochemistry is appointed to an advisory group monitoring water quality downstream from the Athabasca oil sands. He/she announces the results at a press conference. 3.6 14.6 42.8 34.2 4.8 3.13 .057 .804 4. A scientist from the BC Ministry of Forests, Lands and Natural Resource Operations discusses recent research initiatives in BC’s forests pertaining to the Mountain Pine Beetle Stewardship Research Strategy on CBC Radio. 1.2 11.2 49.3 35.6 2.6 3.23 † .047 .693 5. On the local news, a meteorologist reports climate change as the cause for the lack of snow on Cypress Mountain during the 2010 Vancouver Olympic Winter Games. 41.5 32.5 18.3 4.2 3.5 1.85 § .060 .878 6. A university scientist argues more stringent regulations be applied to logging road construction under BC’s Forest and Range Practices Act based on his/her research results with respect to soil erosion following logging activities. 1.8 5.9 38.5 51.2 2.6 3.43 ¶ .062 .690 7. A fish biologist from a local university publicly denounces BC electricity policy and BC Hydro’s supply calculations that support the construction of run of the river power projects to maintain secure electricity supplies. 9.1 20.7 40.9 22.8 6.5 2.83 .063 .909 8. A Federal Government scientist criticizes Canadian climate policy claiming the issue is about “intergenerational injustice,” and lobbies in favour of an alternative class of policies for CO2 emission reduction. 12.3 24.0 34.7 17.0 12.0 2.64 .069 .947 * Student mean perceived credibility significantly lower than APB and faculty, faculty mean response significantly higher than ABCFP † Student mean perceived credibility significantly lower than APB and ABCFP § Faculty mean perceived credibility significantly higher than students. ¶ APB mean perceived credibility significantly higher than ABCFP, faculty and students. 95 The majority of respondents from all sample groups perceived Scenario 1 (traditional model) as credible. However, the mean perceived credibility of the student sample (3.71) was significantly lower than that of the APB (3.89) and faculty ( samples. The mean response of professors was found to be significantly higher (higher perceived credibility) than ABCFP members (3.77) (F= 19.260, df =3, p < .05). 73 The majority of respondents from the four sample groups perceived the situation detailed in Scenario 2 (traditional) as credible or somewhat credible. No significant differences were found between sample groups. Lower proportions of the four sample groups perceived Scenario 3 (science communication model) as credible or somewhat credible. Almost one-fifth of ABCFP members, APB members, faculty members, and students perceived the situation as somewhat not credible or not credible. More than half of the faculty and student samples perceived Scenario 4, also under the science communication model, as somewhat credible while nearly half of both resource professional samples perceived the scenario as credible. The mean perceived credibility of students (3.17) was found to be significantly lower than the ABCFP (3.44) and APB samples (3.38) (F (3, 810) = 4.974). 74 A much lower frequency of respondents perceived Scenario 5, associated with the expert advocacy model, as credible. The majority of all sample groups perceived this situation as somewhat not credible or not credible. The mean perceived credibility of students (1.92) was significantly higher than professors (.56) (F (3, 803) = 5.915).75 The responses to this situation (On the local news, a meteorologist reports climate change as the cause for the lack of snow on Cypress Mountain during the 2010 Vancouver Olympic Winter Games) were observed to be inconsistent with the rest of the section. The situation was, in hindsight, poorly constructed and was omitted from further analyses. More than three-quarters of respondents from all sample groups perceived the other situation corresponding with the expert advocacy model (6) as 73 The Welch F test was used as the Levene statistic (52.986, p < .05) indicated that the variances between mean responses were not equal. Differences were located by the Games-Howell post hoc test. 74 Differences located by the Scheffe post hoc test. 75 Differences located by the Scheffe post hoc test. 96 credible or somewhat credible. The mean perceived credibility of the of the APB sample (was significantly higher than ABCFP ( faculty (), and student (40) samples (F= 6.235, df= 3, p < .05). 76 Scenarios 7 and 8 corresponded with the final advocacy model, political advocacy. Slightly more than half of ABCFP and APB respondents, and more than sixty percent of faculty members and students perceived Scenario 7 as credible or somewhat credible, resulting in a lower mean perceived credibility than the previous situations (excluding scenario 5). Respondents from each sample group were divided over the final situation. Between forty and fifty-five percent of respondents from each sample group perceived the situation as credible or somewhat credible. A higher proportion of ABCFP members (44.1%) perceived this situation as somewhat not credible or not credible. Slightly lower proportions of APB members (43.7%), professors (36.2%), and students (33.9%) perceived the scenario as somewhat not credible or not credible. No statistically significant differences were found. Figure 3.3 Perceived credibility vs. support for advocacy by sample source. 76 The Welch F test was used as the Levene statistic (4.174, p < .05) indicated that the variances between mean responses were not equal. Differences were located by the Games-Howell post hoc test. 2 2.5 3 3.5 4 1 2 3 4 6 7 8 P er ce iv ed C re d ib il it y Traditional Science Communication Expert Advocacy Political Advocacy ABCFP APB Faculty Student 97 As illustrated by the chart depicting perceived credibility vs. models of advocacy (Figure 3.3) Perceived credibility generally followed the expected pattern, and decreased with increasing levels of advocacy. 77 By sample group, perceived credibility varied for each scenario, there was not one sample group mean response that was consistently higher or lower than the others as observed with some of the science and NEP items. Scenario 6 (expert advocacy) is an exception to the pattern. 3.3.2 Credibility of Source Respondents were asked to rank the credibility of four sources of scientific information: industry, university, government, and environmental non-governmental organization (ENGO) from 1 (most credible) to 4 (least credible). The frequencies and descriptive statistics of the credibility results are presented in Table 3.33. Table 3.33 Credibility of Source frequencies (%) and descriptive statistics (ABCFP n= 68, APB n= 112, faculty n= 127, student n= 529, highest frequencies in bold). Source Sample 1 most credible 2 more credible 3 less credible 4 least credible Mean 95% CI SD Industry ABCFP 1.5% 13.2% 42.6% 42.6% 3.26 . 18 .745 APB 2.7% 4.5% 24.1% 68.8% 3.59  .13 .705 Faculty 0.8% 5.5% 18.1% 75.6% 3.69  .11 .613 Student 7.4% 8.7% 17.4% 66.5% 3.43  2.9 2.73 University ABCFP 82.4% 11.8% 4.4% 1.5% 1.25  .15 .608 APB 81.3% 13.4% 4.5% 0.9% 1.25  .11 .577 Faculty 96.9% 3.1% - - 1.03  .03 .052 Student 70.5% 18.0% 7.0% 4.5% 1.46  .07 .813 Government ABCFP 10.3% 61.8% 26.5% 1.5% 2.19  .15 .629 APB 8.9% 54.5% 32.1% 4.5% 2.32  .13 .700 Faculty 1.6% 53.5% 41.7% 3.1% 2.46  .10 .588 Student 5.9% 30.2% 49.3% 14.6% 2.73  .07 .780 ENGOs ABCFP 5.9% 13.2% 26.5% 54.4% 3.29  .22 .915 APB 7.1% 27.7% 39.3% 25.9% 2.84  .17 .896 Faculty 0.8% 37.8% 40.2% 21.3% 2.82  .13 .771 Student 16.3% 43.1% 26.3% 14.4% 2.39  .08 .923 77 Situation 5 was omitted (see previous page). 98 A large proportion of respondents from all sample groups ranked university scientists as the most credible source of information (82.4% ABCFP members, 81.3% APB members, 96.9% faculty members, 70.5% students). The majority of respondents from each group ranked government scientists as the second most credible source (2) with the exception of students (49.3% ranked government scientists as a 3) less credible). Scientists associated with ENGOs were ranked as less credible (3) by 39.3% of APB members and 40.2% of professors. A higher proportion of students (43.1%) ranked ENGOs as more credible (2). The majority of APB members (75.6%), faculty members (75.6%) and student respondents (66.5%) ranked industry as the least credible source of scientific information. Although a high frequency of ABCFP members (42.6%) ranked industry as the least credible source, a higher percentage of the sample (54.4%) ranked ENGOs as the least credible source of scientific information. 99 Chapter 4: Discussion and Conclusions The results of the support for advocacy section of this study build on past research concerning preferred roles for scientists in natural resource management policy by further examining the influence of value orientations and socio-demographic characteristics on support for advocacy. The credibility component of this research provides further insight into perceptions of scientific credibility and its relationship with support for advocacy. The perceptions of resource professionals, academic scientists, and students sampled in this study emphasize the significance of a number of guidelines for credibility maintenance and emergent themes in the relevant literature. Furthermore, these findings suggest the need for future, more comprehensive research into influences of perceived credibility. 4.1 Support for Advocacy Influences Assessing support for advocacy, and the underlying variables that influence an individual’s support, was a critical element of this research. A series of research hypotheses were developed to address this objective. The research hypotheses for demographic variables such as age, gender, education, source of employment, and faculty of study were not supported. However, the results identify science values and political affiliation as significant influences on a respondent’s support for advocacy in the complete sample (Table 4.1), and environmental values in the faculty member sample (Table 4.2). A detailed discussion of the results for this section of the study includes the influence of value scales, field of study, and the left-right political divide on support for advocacy. 100 Table 4.1 Support for advocacy: summary of main findings (complete sample). Hypothesis Independent Variable Bivariate Ordinal Regression Conclusion H1: Support for Advocacy will be significantly different between sample groups. Sample source Significant (+) - H1 is not supported in multivariate analysis. Significant differences were observed between ABCFP, APB, faculty, and student samples w/ chi-square, justifying independent analysis of sample groups. H2: Respondents that place a higher value on science/ the scientific method will support higher models of advocacy Science values Significant (+) Significant (+) H2 is supported; respondents with stronger science values were more supportive of higher models of advocacy. H3: Environmental values will be positively associated with support for advocacy. Environmental Values (NEP) Significant (+) - H3 is not supported; NEP showed no significant effect on support for advocacy in the complete sample multivariate analysis H4: Age will be negatively associated with support for advocacy. Age - - H4 is not supported; age showed no effect on support for advocacy. H5: Women will demonstrate support for higher levels of advocacy than men. Gender - - H5 is not supported; gender showed no effect on support for advocacy. H6: Education will be positively associated with support for advocacy. Education Significant (+) - H6 is not supported by the multivariate analysis, Significant differences in support for advocacy by level of education were observed w/ chi-square. H7: Respondents further left on the political spectrum will be more likely to support higher models of advocacy. Political Affiliation Significant (+) Significant (+) H7 is supported; respondents with right political affiliation were significantly less supportive of higher models of advocacy. H8: Respondents employed by primary resource industries will be less likely to support higher models of advocacy. Source of Employment - - H8 is not supported; source of employment showed no effect on support for advocacy. H9: Support for advocacy will be significantly different between faculties. Faculty/dept. - - H9 is not supported; Faculty/Dept of study showed no effect on support for advocacy in the complete sample. 101 Table 4.2 Support for advocacy: summary of main findings (by sample). Hypothesis Independent Variable Bivariate Ordinal Regression Conclusion H2: Respondents that place a higher value on science/ the scientific method will support higher models of advocacy Science values Significant (+) - APB - Student Significant (+) - ABCFP - APB - Student H2 is supported for three of the four sample groups; respondents with stronger science values were more supportive of higher models of advocacy. H3: Environmental values will be positively associated with support for advocacy. Environmental Values (NEP) Significant (+) - Faculty Significant (+) - Faculty H3 is supported for the faculty sample group; respondents with higher NEP scores were more supportive of higher models of advocacy H4: Age will be negatively associated with support for advocacy. Age - - H4 is not supported; age showed no effect on support for advocacy. H5: Women will demonstrate support for higher levels of advocacy than men. Gender - - H5 is not supported; gender showed no effect on support for advocacy. H6: Education will be positively associated with support for advocacy. Education Significant (+) - APB - H6 is not supported in multivariate analysis. Education was observed to be a non- significant explanatory variable in the APB and student sample regression model. H7: Respondents further left on the political spectrum will be more likely to support higher models of advocacy. Political Affiliation Significant (-) - Faculty Significant (+) - Students H7 is supported in the faculty sample; respondents with right political affiliation were significantly less supportive of higher models of advocacy. H8: Respondents employed by primary resource industries will be less likely to support higher models of advocacy. Source of Employment - - H8 is not supported; source of employment showed no effect on support for advocacy. H9: Support for advocacy will be significantly different between faculties. Faculty/dept. Significant (+) - Faculty - H9 is not supported by multivariate analysis. Faculty/dept. was found to be an explanatory variable in the faculty sample regression model. 102 4.1.1 Value Scales as Advocacy Influences The results involving science and environmental value (NEP) scores highlight notable differences between faculty members, and the other groups. Science values were a statistically significant predictor of support for advocacy for the complete sample, ABCFP, APB, and student groups. The results are consistent with the findings of Steel et al. (2001, 2004), and support the hypothesis that science values are positively correlated with support for advocacy. Although the faculty member sample had the highest science values across each model of advocacy, there was no positive correlation observed (Figure 3.2). On the contrary, the hypothesis that environmental values are positively correlated with support for advocacy held only for the faculty member sample (Figure 3.3). Because scientific research is the profession of this sample, high science values across the advocacy continuum are not surprising. It seems logical that environmental values replace science values as an explanatory variable for support for advocacy in this particular group. 4.1.2 Field of Study (Advocacy by Faculty and Department) Although not statistically significant in regression analysis, the results of support for advocacy by faculty and department within the faculty member sample group warrant further discussion. Because the bivariate results suggested members of the Faculty of Science were more likely to support advocacy than members of the Faculty of Forestry (Table 3.18). These groups were further analyzed by department. In the Faculty of Science, a majority of respondents in each department supported the higher expert advocacy model, and more than one-fifth of respondents from the Departments of Microbiology and Immunology, Physics and Astronomy, Math, and Computer Science supported the highest model, political advocacy. In the Faculty of Forestry, there was minimal support for political advocacy within the three departments and more support for the lower models than observed in the Faculty of Science. These results conflict with expectations that faculty members from natural, life sciences would be more likely to support higher models of advocacy than faculty members from more theoretical, applied areas of study. However, the reluctance of more life-science oriented faculty members to support political advocacy is consistent with the findings of Lach et al. (2003) that ecological scientists abstain 103 from higher levels of involvement in policy issues out of concern their credibility will be challenged by colleagues, politicians, and representatives of interest groups. 4.1.3 Political Affiliation and Advocacy: The Left-Right Divide The hypothesis that respondents who voted for federal parties on the left of the political spectrum is supported for the complete sample, as ordinal regression results suggest respondents with ‘left’ political affiliation are twice as likely to support higher models of advocacy. This hypothesis was also supported for the student sample. The results for political affiliation were generally consistent with the literature, paralleling Steel et al.’s (2001) findings that respondents with ‘right’ political affiliation were more likely to support a traditional role for scientists, and respondents with ‘left’ political affiliation less likely. Blake et al. (1996) discuss a similar left- right political divide over environmental policy in both the United States and British Columbia, noting that: “the regulatory regime erected in the name of environmental protection has attracted the ire of ideological conservatives” (55). The polarizing effect of political affiliation on natural resource science and environmental advocacy is evident not only in the results of this research and the supporting literature, but also in the current Canadian political climate. Editorials criticizing the Conservative-led Canadian Government’s dismissal of scientific research and science-based policy have been published in the international scientific journal Nature twice in 2012. The first alleged that the government has been gradually tightening the constraints on government scientists with respect to communicating peer-reviewed, published research results since the conservative party came into power in 2006 (e.g. limiting scientists to lower models of involvement at the science-policy interface) (Nature Editorial Board 2012a). The second detailed the government’s purported suppression of scientific data as evidenced by cuts to granting programs, the closure of research facilities, and the weakening of environmental legislation (Nature Editorial Board 2012b). These themes further explain the significant effects of political affiliation on support for advocacy as observed in this study. 104 4.2 Perceptions of Credibility The results of the credibility component of the study suggest that the relationship between advocacy and perceived credibility generally supports the prediction that risk to credibility increases with higher models of advocacy. However, perceived credibility can be subject to other influences apart from the advocacy continuum. The ‘credibility of source’ results distinguish ABCFP members from other sample groups and are supported by previous research findings. The overall results of both components of the study suggest that if a line were to be drawn on the advocacy continuum with regard to credibility risk it would fall between the expert and political advocacy models. Additionally, these results highlight the importance of a number of credibility guidelines and prevalent themes in the literature for the protection of scientific credibility when engaging in advocacy at the science policy interface. 4.2.1 Perceived Credibility and the Advocacy Continuum The relationship between perceived credibility and support for advocacy generally followed the expected pattern, suggesting that perceived credibility is negatively associated with increasing levels of advocacy. However, there were notable exceptions that suggest factors separate from the level of advocacy on the continuum such as credibility of source and mode of communication can influence perceptions. The respondent’s ratings of perceived credibility decreased as the scenarios moved from the traditional model to higher levels of advocacy (Figure 3.3). Scenario 6 is an exception to the negative relationship observed between models of advocacy and credibility. The perceived credibility in Scenario 6 (expert advocacy) is higher than Scenarios 3 and 4 (science communication). One possible explanation is that the higher perceived credibility observed for Scenario 6 (expert advocacy) could be attributed to the fact that the source of the information was a university scientist, the source ranked most credible by the majority of respondents from each sample group (see section 4.2.2). 105 Another possible explanation is that Scenarios 3 and 4 (science communication) involve speaking to the media, which can be perceived in a different way as then when a scientist speaks to the public or to policy-makers, resulting in a detrimental effect on perceived credibility. As a respondent from the APB sample explained: The source of the research is important but so are the means by which the information is relayed to the general public. Too often (almost always) news reporters in all fields misrepresent the facts leading to further confusion and debate as to the credibility of the source. Scientists need to learn how to convey scientific discovery in layman’s terms to avoid relying on the interpretations of others to relay facts and findings. Lets face it, science is useless unless shared and applied. 4.2.2 Credibility of Source The responses of ABCFP members stood out from the other groups when asked to rank sources of scientific information based on credibility. It was not surprising that the majority of all groups ranked university scientists to be the most credible source of scientific information (faculty members almost unanimously). University science can be perceived as more objective and unbiased, with industry and ENGOs as more polarized sources of information. A respondent from the student sample commented: The only truly credible source would be University research. Governments, ENGOs, industries all have a certain belief and agenda that they promote and wanting to accomplish that obscures the credibility of the figures and the analysis. Industries are the worst source in my opinion, since they are ‘maximum profit seeking organizations’ and their greed for money obscures their objectivity. APB members, faculty members, and students ranked industry as the least credible source of scientific information, while the majority of ABCFP members ranked ENGOs as the least credible. The differences observed with regard to the ABCFP sample can be explained by a number of factors consistent with other findings from this study and the supporting literature. Higher proportions of forest professionals in the sample are employed by, or work closely with industry in comparison to the professional biologist sample, which could cause them to perceive industry as a more credible source. Additionally, the fact that the ABCFP sample had significantly lower NEP scores than the other groups, combined with Tindall et al.’s (2010) results that individuals employed by, or with social ties to the forest industry have lower NEP 106 scores, may explain why the majority of respondents in this sample group perceive ENGOs as the least credible source of information. 4.2.3 Expert Advocacy: Drawing the Line As stated in the literature review, many scientists agree that when engaging in advocacy, there is a line that should not be crossed, yet no consensus on where to place it (Blockstein 2002). The results of both the support for advocacy and perceived credibility aspects of this study provide evidence to suggest that a line should be drawn between the expert and political advocacy models. Drawing on the support for advocacy results from the first component of this study, the expert advocacy model was the most supported, for all sample groups. Results indicated that the majority of each group was in support of expert advocacy (Table 3.7). Approximately 60% of APB members, and half of the respondents in the other groups agreed with the premise of expert advocacy. These results represent a much greater support for more involved roles of scientists at the science policy interface than previous results would suggest (Gray and Campbell 2009; Lach et al. 2003; Steel et al. 2004). In contrast to this support for expert advocacy, a considerably smaller proportion of respondents supported political advocacy (less than 10% of resource professionals, less than 20% of faculty members and students). Furthermore, observed differences in science and environmental scale values across models of advocacy reinforce this idea. Looking across the continuum on the graph of support for advocacy vs. science values (Figure 3.1), science value scores for students and the complete sample that otherwise increased across the advocacy continuum were found to level off or decrease slightly for political advocacy. The same was observed for NEP scores with the APB member, student, and complete samples (Figure 3.2) suggesting that many individuals with the highest science and NEP values choose to abstain from supporting the highest model, perhaps due to perceived credibility risks. Examining the relationship between support for advocacy and perceived credibility, there was a substantial drop in the perceived credibility levels observed at the transition between expert 107 advocacy and political advocacy (Figure 3.3). This further supports the proposition to ‘draw the line at expert’ advocacy. 4.2.4 Guidelines to Protect Scientific Credibility at the Science Policy Interface While advocacy and the science-policy interface can be extremely advantageous to effective natural resource management policy development, the risk of credibility loss remains a legitimate threat. The results of this study emphasize a number of guidelines and common themes throughout the literature such as: ‘follow the rules of science’, ‘avoid hyperbole’, and the provision of a clear distinction between scientific results and policy advocacy. The first guideline ‘follow the rules of science’ is one of a set of guidelines for credibility maintenance proposed by Blockstein (2002). The ‘rules of science’ include: “explain how conclusions are reached, present margins of error, and base interpretation upon data and conclusions that are peer-reviewed” (93). The importance of peer review is observed as a prevalent theme in both the literature, and the questionnaire responses. Lach et al. (2003) found that the science community emphasized their perceptions of scientific credibility on the quality of peer-reviewed publications and research contributions more so then the public. However, both groups ranked ‘reputation in specialization’ as a measure of scientific credibility, and the quality of peer-reviewed publications and research contributions can make up a sizable portion of a scientist’s reputation in their specialization. Questionnaire responses such as these were common in the faculty member sample: “peer review underpins scientific credibility”, “peer review of findings is a key measure of credibility”, and “only when data is peer-reviewed can it be truly credible”. The second credibility guideline, also introduced by Blockstein (2002) is ‘avoid hyperbole’. The use of value-laden language is, from a credibility standpoint, arguably one of the most dangerous traits of political advocacy. The importance of this guideline is evidenced in the credibility 108 results of the study as the perceived credibility scenario that involved hyperbole (Scenario 8, political advocacy) received the lowest perceived credibility rating. The idea that, when advocacy occurs, there should be a clear distinction between scientific results and policy preferences, is also a recurrent theme within the literature (Scott et al. 2007, Lackey 2007). In a survey of conservation biologists, Scott et al. (2007) found that a majority of respondents supported advocacy in scientific publics with the stipulation: “only when the preferences are supported by the science, and only if the policy preference is clearly separated from the science” (Scott et al. 2007, 33). This stipulation was also present among survey respondents. 4.3 Future Research As noted in the literature review, there is a body of previous research surrounding preferred roles for research scientists in natural resource policy, but few experimental studies focusing on perceptions of scientific credibility. The results of the exploratory credibility component of this research suggest future research possibilities to promote a more in-depth understanding of influences on perceptions of scientific credibility. One significant flaw in the perceived credibility component of the study that can be improved upon was the fact that many of the hypothetical credibility situations are heavily loaded with information (e.g. source, action, topic) allowing for misinterpretation. Many respondents noted that in some circumstances not enough information was given to form a sufficient opinion of the involved scientist’s credibility. It is recommended that a future study on this topic include a more detailed, extensive set of scenarios in which only one detail (education, experience, publications, source of employment, research topic, etc) is varied for each to provide a better understanding of which details account for differences in perceived credibility between the various situations, apart from position on the advocacy continuum, and to determine the extent of influence by operationalizing the details as credibility components. 109 4.4 Conclusions In summary, this study builds on previous research into support for, and influences of, advocacy. It also identifies interesting differences in the values and credibility perceptions of professional foresters, biologists, and academic scientists. Risks to credibility were generally found to increase across the advocacy continuum, yet perceived credibility levels can be influenced by other factors, with further research recommended. Taking into account these findings, and the support for advocacy results, expert advocacy is encouraged, while political advocacy is not recommended. Finally, the results highlight a number of guidelines and themes from the relevant literature. Following the rules of science, avoiding hyperbole, and the separation of scientific results from policy preferences are recommended to protect scientific credibility when engaging in advocacy at the science policy interface. Consistent with previous research, the results identify science values and political affiliation as significant influences on a respondent’s support for advocacy in the complete sample. The left-right political divide over support for advocacy found in this study supports previous research and is representative of the current political climate in Canada. These findings suggest there is a greater likelihood of controversial policy issues becoming politicized, and underscores the risks of credibility loss as a consequence of political advocacy. No significant differences in support for advocacy were found between sample groups in the multivariate analysis. However, members of professional associations and academic scientists were distinguished from the complete sample by differing values and demographic characterizations. The two professional associations sampled: professional foresters and professional biologists exhibited very different characteristics. Forestry professionals differed from the rest of the groups in the sense that they had significantly lower NEP scores, and ranked ENGOs as the least credible source of scientific information while other groups ranked industry in that position. They were also the most likely to support the ‘traditional’ model. Consistent with the literature, professional biologists were distinguished from the complete sample with the highest proportion of support for expert advocacy, and significantly higher environmental values. The faculty sample was distinguished from other 110 groups as environmental values were a shown to have a significant positive influence on support for advocacy in place of science values as observed for other groups. The results of the credibility component of this research confirms that perceived credibility risk increases across the advocacy continuum, yet can be subject to other influences separate from the continuum such as credibility of source and mode of communication. Consequently, further research into perceived credibility to study the effects of such influences is recommended. Overall, the results of both components of the study suggest that if a line were to be drawn on the advocacy continuum with regard to credibility risk it would fall between the expert and political advocacy models. This proposition is based on the high proportion of support for expert advocacy, lower support for political advocacy, and low credibility perceptions of scenarios on the political advocacy end of the continuum observed in this study. Although arguments for and against advocacy persist in the science community, many scientists do support advocacy and are engaging in at the science-policy interface, where threats to scientific credibility remain a serious concern. As evidenced by the literature and questionnaire results, following the rules of science, avoiding hyperbole and value statements, and clearly separating scientific results from policy preferences are important guidelines to protect scientific credibility when engaging at this interface. 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B., H. W. Harshaw, and S. R. J. Sheppard. 2010. “Understanding the Social Bases of Satisfaction with Public Participation in Forest Management Decision-making in British Columbia.” The Forestry Chronicle 86 (6): 709–722. University of Alberta. 2012. “Dr. David Schindler: Biography.” University of Alberta. http://www.biology.ualberta.ca/faculty/david_schindler/?Page=1023. 116 Appendix A - Questionnaire Perceptions of Science, Advocacy, and Credibility Welcome Thank you for your participation! The objective of this study is to address the question of whether or not scientists should advocate policy preferences, and how such advocacy impacts perceptions of credibility. Your responses to this questionnaire will further understanding of public perception on this subject, and contribute to a Master’s thesis. This questionnaire should take 10-15 minutes to complete. Please provide a response to every question. After completing each section click "Next" at the bottom of the screen to continue. Your identity will remain confidential, and your answers anonymous. Submission of this questionnaire implies your consent to participate. Please click "NEXT" to begin. Section 1: Science Values Select one response based on your own opinion/ interpretation of the statement. strongly agree mildly agree mildly disagree strongly disagree don't know 1. Science and technology are making our lives healthier, easier, and more comfortable. 2. Bias in scientific inquiry is expected and therefore accepted, it is human nature. 3. Scientific research that advances the frontiers of knowledge is necessary and should be supported by the Federal Government. 4. I do not find knowledge of science to be important in my daily life. 5. Decision-making in the field of natural resource management should reflect the best available science. 6. It is often thought that scientific research has produced both beneficial and harmful results in society. Overall, harmful results have outweighed the benefits. 7. Adherence to the scientific method (propose a hypothesis, develop experimental studies to test hypotheses) is essential in the development of scientific knowledge. 8. Our society allocates too much time and resources toward scientific inquiry. 9. I take interest in new scientific discoveries and innovations. 10. Science should never take precedence over other factors in decision-making (economic, social, etc.) 117 Section 2: Environmental Values Select one response based on your own opinion/ interpretation of the statement. strongly agree mildly agree mildly disagree strongly disagree don't know 11. We are approaching the limit of the number of people the earth can support. 12. Humans have the right to modify the natural environment to suit their needs. 13. When humans interfere with nature it often produces disastrous consequences. 14. Human ingenuity will ensure that we do NOT make the earth unlivable. 15. Humans are severely abusing the environment. 16. The earth has plenty of natural resources if we just learn how to develop them. 17. Plants and animals have as much right as humans to exist. 18. The balance of nature is strong enough to cope with the impacts of modern industrial nations. 19. Despite our special abilities humans are still subject to the laws of nature. 20. The so- called “ecological crisis” facing humankind has been greatly exaggerated. 21. The earth is a closed system with very limited room and resources. 22. Humans were meant to rule over the rest of nature. 23. The balance of nature is very delicate and easily upset. 24. Humans will eventually learn enough about how nature works to be able to control it. 25. If things continue on their present course, we will soon experience an ecological catastrophe 118 Section 3: Support for Advocacy Advocacy is defined as providing active support for a cause, idea or policy. 26. Please select the statement below that you most agree with: Scientists should provide objective scientific findings when relevant to their core area of expertise with no formal position or further involvement. Scientists should interpret and explain their findings, but they should not advocate a preference for management. Scientists should act as policy advocates in their area of expertise. Scientists should advocate their positions in any area of scientific research they feel is appropriate. Section 4: Credibility of Source Credibility denotes the believability of the source of a message with trustworthiness as a major component. 27. What type of scientist would you perceive to be the most credible source of information, based on their source of employment? Rank each source below from 1 (most credible) to 4 (least credible). Do not repeat ranks. 1 2 3 4 Industry University Government Environmental Non-Governmental Organization (ENGO) Section 5: Credibility of Scientists who Advocate Positions on Environmental Issues Select one response to indicate how you would perceive the scientist’s credibility in each hypothetical situation provided: credible somewhat credible somewhat not credible not credible don't know 28. A scientist measures heavy metal accumulation in a wetland area receiving acid mine drainage from an abandoned copper mine. The results are published in the Journal of Ecological Engineering. 29. The results of a study done by a team of wildlife biologists evaluating habitat fragmentation as a result of above-ground natural gas pipeline construction are brought to the attention of relevant managers, such as Environment Canada 119 30. A scientist specializing in watershed biogeochemistry is appointed to an advisory group monitoring water quality downstream from the Athabasca oil sands. He/she announces the results at a press conference. 31. A scientist from the BC Ministry of Forests, Lands and Natural Resource Operations discusses recent research initiatives in BC’s forests pertaining to the Mountain Pine Beetle Stewardship Research Strategy on CBC Radio. 32. On the local news, a meteorologist reports climate change as the cause for the lack of snow on Cypress Mountain during the 2010 Vancouver Olympic Winter Games. 33. A university scientist argues that more stringent regulations be applied to logging road construction under British Columbia’s Forest Range and Practices Act based on his/her research results with respect to soil erosion on steep slopes following logging activities. 34. A fish biologist from a local university publicly denounces BC electricity policy and BC Hydro’s supply calculations that support the construction of run of the river power projects to maintain secure electricity supplies. 35. A Federal Government scientist criticizes Canadian climate policy claiming the issue is about “intergenerational injustice,” and lobbies in favor of an alternative class of policies for CO2 emission reduction. Comments: Section 6: Demographic Information 36. Which UBC Faculty and Department are you a member of? [Student/Faculty Samples only] If joint-appointed please name the faculty/department you most identify with. Faculty: Department: 120 36. What is your source of employment? [APB/ABCFP Samples only] Please specify. Industry Consulting Academic Government Other: ______________________ 37. What is your title as a faculty member? [Faculty Sample only] Professor Associate Professor Assistant Professor Professor Emeritus Other: ______________________ 37. What is your title as a member of the ABCFP? [RPF Sample only] RPF RFT Retired RPF Retired RFT Other: ______________________ 37. What is your gender? [38. For Faculty/RPF Sample] Male Female Other 38. What is your age? [39. For Faculty/RPF Sample] 121 39. What is the highest level of education you have completed? [APB/ABCFP/Student Samples Only, 40. For ABCFP] Select one. Some post secondary Completed post secondary (bachelors degree, technical school diploma) Some graduate school Completed masters degree Completed doctoral degree 40. Did you vote in the last Canadian federal election? [41. For ABCFP] Yes No If yes, which federal political party did you vote for? Conservative Liberal NDP Green Other: ______________________ Thank you for completing this questionnaire! Comments: 122 Appendix B – Supplementary Tables Table A-1 “Which UBC Faculty are you a member of?” (n= 127). Faculty Frequency % Science 81 63.8% Forestry 25 19.7% Land and Food Systems 13 10.2% College for Inter-disciplinary Studies 7 5.5% Medicine 1 0.8% Table A-2 “What is your title as a UBC Faculty Member?” (n=12, SD= 0.929) Title Frequency % Professor 72 56.7 Associate Professor 29 22.8 Assistant Professor 13 10.2 Professor Emeritus 10 7.9 Other 3 2.4 Table A-3 Which UBC Faculty are you a student in? [students] (n=529) Faculty Frequency % Arts 200 37.8% Science 178 33.7% Forestry 101 19.1% Applied Science 39 7.4% Business 4 0.8% CFIS 3 0.6% Undecided/Unknown 3 0.6% Land and Food Systems 1 0.2% Table A-4 “What is your source of employment?” [RPF/RPBio] Sample n Industry Consulting Academic Government Other RPF 68 22.1% 23.5% 2.9% 41.2% 10.3% RPBIO 112 8.0% 53.6% 2.7% 25.9% 9.8% Table A-5. “What is your title as a member of the Association of BC Forest Professionals?” (n= 68, SD= 0.929) Title Frequency % Registered Professional Forester (RPF) 55 80.9% Registered Forest Technologist (RFT) 7 10.3% Retired RPF 3 4.4% Forester in-training (FIT) 3 4.4% 123 Table A-6. “What is your gender?” (n=836, SD=0.514). Sample n Male Female Other ABCFP 68 73.5% 26.5% - RPBIO 112 61.6% 38.4% - Professor 127 80.3% 17.3% 2.4% Student 529 38.2% 61.2% 0.6% 2 = 100.733, df= 3, p < .05; Cramer’s V = 0.348 Table A-7. “What is your age?” Sample n Mean age Range SD ABCFP 68 45 27-79 12.46 APB 112 47 25-72 11.42 Faculty 127 52 31-78  10.80 Student 529 23 18-77  4.70 Total 836 32 18-79 14.80 F= 471.208, df = 3 p < .05  Significantly higher than ABCFP, APB, students  Significantly lower than ABCFP, APB, faculty Table A-8. “What is the highest level of education you have completed?” (n=709) [RPF, RPBio, students] Sample n Some post secondary Completed post secondary Some graduate school Completed Masters Degree Completed Doctoral Degree ABCFP 68 2.9% 67.6% 2.9% 17.6% 8.8% APB 127 - 35.7% 8.0% 43.8% 12.5% Students 529 69.4% 27.6% 1.3% 0.5% 0.2% 2 = 396.975, df=8, p < .05, Cramer’s V = 0.529 Table A-9. “Which federal political party did you vote for (last federal election)?” Sample n Conservative Liberal NDP Green Other Private/prefer not to disclose ABCFP 68 22.1% 13.2% 33.8% 8.8% 1.5% 4.4% APB 112 15.2% 17.9% 35.7% 16.1% 1.8% 1.8% Faculty 127 3.9% 35.4% 13.4% 4.7% 6.3% 3.9% Student 529 10.4% 16.8% 14.2% 9.8% 4.3% 0.8% 2 = 48.732 , df= 9, p < .05; Cramer’s V = 0.182
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Perceptions of advocacy and credibility in policy-relevant natural resource science
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