The use of Q-methodology to understand public perspectives on forests’ contribution
to climate change mitigation
M. Nijnik
Socio-Economic Research Programme, Macaulay Institute, Aberdeen, AB15 8QH,
Scotland, UK, email: m.nijnik@macaulay.ac.uk
__________________________________________________________________________________________
Extended abstract
The research develops a Q-methodology decision-support tool to assist in establishing a
common view toward climate change mitigation forest policies and to inform end-users on
the most appropriate forest management decisions, particularly with regard to woodlands
expansion. Q-method is a rigorous and systematic quantitative means for examining human
subjectivity. By using sequential application of correlation and factor analysis it allows us to
identify and assess subjective structures, attitudes and perspectives from the standpoint of
the persons being observed (Stephenson, 1963; Brown, 1996). The method provides
insights into respondents’ preferences, identifies important criteria of respondents’
perspectives and explains factors influencing attitudinal diversity through analyzing the
respondents’ socio-economic background. The main concern of the Q-method is not with
how many people believe such-and-such, but why and how people believe in what they do.
The method therefore allows for a rather simple data set, and its factor analytical tool makes
it possible to analyse the interviews, even when the respondents have not explicitly revealed
their opinions.
Though the body of literature on Q-method is growing, the subset of forestry related research
employing it is rather small. The methodology has been used e.g. to define attitudes toward
agrarian reform (Peritore, 1990), to understand participant perspectives in national forest
management (Steelman and Maguire, 1999), and to measure attitudinal diversity of forest
policy actors (Nijnik and Oskam, 2004).
The following basic features distinguish Q-method from standard survey analysis (Ranalysis) (McKeown and Thomas, 1988; Barry and Proops, 1999; Nijnik and Oskam, 2004):
• Whilst R-analysis is concerned with patterns across objective variables (gender, age,
etc) and yields statistically generalizable results, Q-analysis deals with patterns of
subjective perspectives across individuals and it results in typologies of perspectives
that prevail in a given situation;
• With R-method, correlation summarizes the relationships among the traits and then
factor analysis denotes the clusters of traits. Q-method allows individual responses to
be collated and correlated; correlation summarises the views among people, and
resulting factors represent points of view.
• Q-method employs small number of respondents, because most of the data derives
from how much information is implicit in each participant’s Q sort. As few as twelve
respondents can generate statistically meaningful results, in terms of the range of
implicit discourses uncovered, and a single person surveys are used in certain cases.
The research involves the following steps. Firstly, the existing attitudes are analyzed through
interviews. After pre-testing of the questionnaire, and then improving both the statements
and the procedure, more of the respondents are asked to rank order the responses by
placing the statements in the normal distribution chart. Each respondent is requested to
distribute the statements (their numbers) across boxes in the chart, according to his or her
agreement/disagreement with each of the statements. The diversity of the opinions across
the respondents is important. The respondents rank each Q-statement on the scale ranging,
for instance, +5 through -5, where "plus five" indicates a complete agreement and "minus
five" indicates a complete disagreement with the statement, with zero indicating a neutral
attitude to it. The statements include various economic, environmental and social dimensions
of forest’s contribution to climate change mitigation. After the respondents had ranked the
statements across the normal distribution, which encouraged them to consider the
relationships among the statements more systematically, the output data are assessed,
using the sequential application of multiple regression and factor analysis.
Factor analysis reduced the dimensionality of the data by creating a few new uncorrelated
choice variables (a set of factors or typical Q-sorts) which have captured the common
essence of the several individual Q-sorts. The research therefore identifies the groups of
people with their views (dominant attitudes towards forests’ contribution to climate change
alleviation), according to the orientation of the blocks of statements. Then, the distinguishing
statements across the factors are analysed, and social discourses uncovered by the
statistical analysis are given. Finally, factors influencing attitudinal diversity are defined and
explained. Statistically analysed socio-economic background of the respondents (gender;
age; education; occupation etc. revealed in course of the survey) is considered to elucidate
its influences on the perceptions.
The research is in progress, and seen as a contribution to a broad discussion on climate
change which is going on in the UK, and on opportunities of climate change mitigation
through forestry (including by using wood as a substitute for fossil fuels). It is anticipated that
the use of Q-method in this particular setting will enable us: -to clarify important internal and
external constituencies of climate change mitigation forestry policy decisions; -provide
sharper insights into the preferred management directions (e.g. concerning renewable
energy); -identify criteria of climate change mitigation solutions that are important to people; outline areas of consensus and conflict between the participants’ opinions, as well as
between political and public perceptions. This consultation with the public would provide a
better understanding of people’s perspectives on the role and place of forestry projects in
climate change alleviation. The research would lead to a number of conclusions, with the
view of offering some insights into the connection between the policy of woodlands
development promoted in the UK and public attitudes towards forests’ contribution to climate
change mitigation.
Reference
Barry, J. and Proops, J. (1999) Seeking sustainability discourses with Q methodology.
Ecological Economics 28: 337-345
Brown, S. (1996) Q methodology and qualitative research. Qualitative Health Research 6:
561-567.
McKeown, B. and Thomas, D. (1988) Q Methodology, Sage Publ., California.
Nijnik M. and Oskam, A. (2004) Governance in Ukrainian forestry: trends, impacts and
remedies. International Journal of Agricultural Resources, Governance and Ecology 3(1/2):
116-133.
Peritore, N. and Peritore, A. (1990) Brazilian attitudes toward agrarian reform: A Qmethodology opinion study of a conflictual issue. Journal of Developing Areas 24(3): 377405.
Steelman, T. and Maguire, L. (1999) Understanding participant perspectives: Qmethodology in national forest management. Journal of Policy Analysis and Management
18(3): 361-388.
Stephenson, W. (1963) Independency and operationism in Q-sorting. The Psychological
Record 13: 269-272.