Measuring attitudes as a complex system: Structured thinking and support for the Canadian carbon tax

Area of Science:

• Political Science
• Cognitive Psychology
• Network Analysis

Background:

• Political attitudes are complex and challenging to measure.
• Network-based approaches offer novel ways to represent and analyze cognitive and affective structures.
• Cognitive-affective mapping (CAM) visually represents individual attitudes as networks of associated concepts.

Purpose of the Study:

• To test the efficacy of CAM as a method for studying political attitudes.
• To investigate whether emotional and structural properties of CAMs predict support for the carbon tax.
• To explore associations between CAM structural properties and political interest.

Main Methods:

• A sample of 111 Canadians created cognitive-affective maps (CAMs) of their views on the carbon tax using the Valence software.
• CAMs were analyzed as undirected graphs, focusing on emotional and structural properties (density, centrality).
• Statistical methods were used to predict carbon tax support and political interest from CAM properties.

Main Results:

• Emotional properties of CAMs significantly predicted individuals' attitudes toward the carbon tax.
• Structural properties (density, centrality) of CAMs did not significantly predict carbon tax support.
• CAM structural properties showed associations with measures of political interest.

Conclusions:

• CAMs provide a promising tool for visualizing and analyzing political attitudes.
• The emotional content of concept networks is a key predictor of policy support.
• Further research is needed to fully understand the utility of CAMs in political science.