2.3.5. Multivariate Analyses

We used bivariate analysis methods, such as bar charts and Spearman correlations, and multiple regression models to analyze the relationships between explanatory variables, such as household type, gender, education, income, PEA factor scores, urban zone of the residential location, and the outcome variables: PEB factor scores related to clothing (factor 1), heating (factor 2), and produce (factor 3), and travel emissions from local, national, and international travel.

The statistical analyses were run in IBM SPSS Statistics 24. Three models for each of the PEB factor scores were prepared and the first model included the four sociodemographic variables as independent variables. In the second model, PEA factor scores were added as independent variables, and in the third model, the three residential urban zones were added too. Ordinary least squares (OLS) regression was used due to the quantitative character of the dependent variables.

Two models were calculated for each type of travel (local, domestic, and international). Binary logistic regression was used to analyze participation in emissions from travel, due to the dichotomous character of the dependent variable. OLS regression was used to analyze the amount of emissions of those who participated. By also running a binary logistic regression on participation in travel emissions, it was possible to capture which variables impacted whether a respondent had traveled in the past year and see if those same variables affected the amount of emissions. The independent variables in all models were gender, income, education level, household type, PEA factor scores, and urban zones.
