Suppose we were interested in understanding consumer
perceptions of GMO foods. People often make decisions and form
opinions about issues based on abstract constructs like fears, goals,
ambitions, values, political ideology etc. These are things that may not be so easy to quantify, despite being relevant to behavior and choice. When it comes to perceptions
of GMO food, perhaps they are shaped by some degree of skepticism of ‘big ag’
or chemical companies. We could call this ‘monsantophobia.’ Perceptions
of biotechnology could also be shaped by the extent of one’s knowledge of basic
biology, agricultural science, and genetics.
This second construct could be referred to as a ‘science’
factor. A third ‘factor’ that may shape one’s views could be based on their
ideals related to the role of government and political ideology. We’ll call
this the ‘political’ factor.
So how can we best quantify these ‘latent’ constructs or
‘factors’ that may be related to perceptions of biotechnology, and how do
we model these interactions? This
will require a combination of techniques involving factor analysis and
regression, known as structural equation modeling. We might administer a
survey, asking key questions that relate to one’s level of monsantophobia, science knowledge, and political views. To the extend that ‘monsantophobia’
exists and shapes views on biotechnology, it should flavor responses
to questions related to fears, skepticism, and mistrust of ‘big ag.’ Actual
knowledge of science should influence responses to questions related to science etc.
We also may want to quantify the actual flavor of perceptions of GMO food. This
could be some index quantifying levels of tolerance or preferences related
to policies concerning labeling, testing, and regulation or purchasing decisions and expenditures on related goods. To the extent that perceptions are ‘positive’ the index would reflect that on some scale related to answers
to survey questions about these issues. You could also include a set of questions related to policy preferences and try to model the interaction of the above factors and their impact on the support for some policy or the general policy environment.
Suppose we ask a range of questions related to skepticism of
big ag and agrochemical companies and record the responses to each question as
a value for a number of variables (Xm1…Xmn), and did the
same for science knowledge (Xs1…Xsn), political ideology
(Xp1…Xpn), and overall GMO perception (Yp1…Ypn) and policy environment (Ye1…Yen) .
Given the values of these variables will be influenced by the actual latent
constructs we are trying to measure, we refer to the X’s and Y’s above as
‘indicators’ of the given factors for monsantophobia, science, politics, gmo perception, and policy environment. They may also be referred to as the observable manifest variables.
Now, this is not a perfect system of measurement. Given the level of subjectivity among other things, there is likely to be a
non-negligible amount of measurement error involved. How can we deal with measurement error and quantify the
factors? Factor analysis
attempts to separate common variance (associated with the factors) from unique
variance in a data set. Theoretically, the unique variance in FA is correlated
with the measurement error we are concerned about, while the factors remain
‘uncontaminated’ (Dunteman,1989).
Structural equation modeling (SEM) consists of two models, a
measurement model which consists of deriving the latent constructs or factors
previously discussed, and a structural model, which relates the factors to one
another, and possibly some outcome. In this case, we are relating the factors
related to monsantophobia, science, and political preferences to the outcome,
which in this case would be the latent construct or index related to GMO
perceptions and policy environment. By using the measured ‘factors’ from FA, we can quantify the
latent constructs of monsantophobia, science, politics ,and GMO perceptions
with less measurement error than if we simply included the numeric responses
for the X’s and Y’s in a normal regression. And then SEM lets us identify the relative influence of each
of these factors on GMO perceptions and perhaps even their impact on the general policy environment for biotechnology. This is done in a way similar to regression, by
estimating path coeffceints for the paths connecting the latent constructs or factors as depicted below.
Equations:
References:
Principle Components Analysis- SAGE Series on
Quantitative Applcations in the
Social Sciences. Dunteman. 1989.
Awareness and Attitudes towards Biotechnology Innovations
among Farmers and Rural Population in the European Union
LUIZA TOMA1, LÍVIA MARIA COSTA MADUREIRA2, CLARE HALL1, ANDREW BARNES1, ALAN
RENWICK1
Paper prepared for presentation at the 131st EAAE Seminar ‘Innovation for
Agricultural Competitiveness and Sustainability of Rural Areas’, Prague, Czech
Republic, September 18-19, 2012
A Structural Equation Model of Farmers Operating within
Nitrate Vulnerable Zones (NVZ) in Scotland
Toma, L.1, Barnes, A.1, Willock, J.2, Hall, C.1
12th Congress of the European Association of Agricultural Economists – EAAE
2008
PLoS One. 2014; 9(1): e86174.
Published online Jan 29, 2014. doi: 10.1371/journal.pone.0086174
PMCID: PMC3906022
Determinants of Public Attitudes to
Genetically Modified Salmon
Latifah Amin,1,* Md. Abul Kalam Azad,1,2 Mohd Hanafy Gausmian,3 and Faizah
Zulkifli1
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