About the research
Elizabeth undertook her research as part of her BA in Social Science (Sociology & Philosophy) at the University of Manchester.
The aim of Elizabeth’s research was to identify whether there was a relationship between class and the perception of the environmental crisis, as well as the uptake of different environmental behaviours.
Her research questions were:
- How does perception of the environmental crisis differ between NS-SEC classes?
- How does the uptake of environmental behaviours differ between classes?
- Are the distinctions between classes greater in environmental buying choices than in the performance of everyday-level environmental tasks?
Data
To investigate her research questions, Elizabeth used data from the Understanding Society, United Kingdom Longitudinal Household Survey, Wave 4, 2012-14 . She chose this data as it enabled her to look at climate related attitudes and behaviours using a nationally representative sample.
“The Understanding Society dataset uses really interesting and deep-delving questions, not only on attitudes towards climate change, but objective actions people taken to mitigate it. Using this data set allowed a wide-scale project that truly reflected the environmental attitudes and actions of different socio-economic sectors of the UK population.”
Methodology
Elizabeth used the National Socio-Economic Classification (NS-SEC) as her measure of class and used logistic regression analysis to explore its relationship with environmental perceptions and behaviours. She first used a logistic regression model to explore the influence of class on the likelihood of agreeing that the environmental crisis has been exaggerated. A further three logistic regression models were then used to test the influence of class on the likelihood of never having undertaken three different sets of environmental activities (everyday behaviours, buying choices and transport choices).
She chose to use logistic regression models as they allow comparisons to be made between different environmental activities, while also controlling for theoretically relevant explanatory variables such as education, age, gender and political affiliation.
Findings
Elizabeth found a statistically significant difference in environmental perspectives between classes. She found that individuals from a professional class are 1.5 times as likely, and intermediate class 1.3 times as likely as those in a working class to believe that the environmental crisis has not been exaggerated. This remained the case when education, political affiliation and influential factors were controlled for.
She also found that there was a class difference in environmentalist buying activities, with those from a professional class significantly less likely than those from a routine class to report never doing such activities.
Based on her findings and the literature she concluded that the problem was not as simple as ‘lower socio-economic groups are excluded from environmentalism’ and that unequal distribution of knowledge and resources impacted who could undertake environmental activities.
“In some cases, such as environmental buying choices, it seems working classes may be somewhat excluded, by both economics and domination of salariat classes.
Contrarily, in everyday activities or transport choices, working classes are similarly as willing as salariat classes to engage environmentally.”
Top Tips
Elizabeth’s top tips for students about to start a project or dissertation with secondary data are:
Remember it is okay to spend a lot of time on finding a topic – Elizabeth downloaded over 50 datasets before she settled on hers!
“This was not time wasted. You need to check through all the datasets to see if there is suitable data which answers your research questions.”
Read through previous research to check that your research is something different
Check that the dataset you select is representative enough to answer your main question
Do your data analysis early on. It is useful to start the analysis with a lot of time before you want to start writing up. This will give you time to adapt your research questions, to read and review relevant readings and develop your ideas as you write up.
Skills learnt from doing a secondary data analysis
The skills Elizabeth feels she has gained from completing a quantitative dissertation using secondary data:
- allowed her to really grasp how to analyse large datasets and refine these in line with individual characteristics and attitudes within the dataset.
- made her confident with statistical software like SPSS and Excel
“I am now working at my job and enjoying it so much because of my experience with data.”
Future plans
Elizabeth is now using her data skills working within the public sector on programmes assisting offenders.