Political Trust Analysis

Project Overview

This case study investigates political trust using the World Values Survey, an international research program that studies social, political, economic, religious, and cultural values across many countries. The project focuses on how cultural and societal values are related to trust in political institutions.

The analysis was developed for the Case Studies course at TU Dortmund and combines survey-data preprocessing, cross-country comparison, ordinal modelling, time-series exploration, and a bonus clustering component.

Research Question

The broad research question of the project was:

Specific Research Questions

Dataset

The project used the World Values Survey Wave 7 data, covering the most recent completed WVS wave from 2017 to 2022, together with the WVS time-series dataset covering the longer period from 1981 to 2022. The WVS data provides variables on political trust, social values, corruption perceptions, political participation, religion, demographics, and country-level identifiers.

The analysis focused on trust-related institutional variables and selected cultural, political, and demographic predictors. Missing-value codes such as “do not know”, “no answer”, and “not asked” were handled during preprocessing before modelling.

Key Variables

Methodology

The main modelling approach was ordinal regression with a cumulative link function, because the institutional trust variables are ordinal response variables with ordered categories. This allows the analysis to respect the ordered structure of the survey answers instead of treating them as purely continuous variables.

Analysis Pipeline

1. Data preprocessing: selected relevant WVS variables, filtered countries and waves, converted missing-value codes into missing values, and prepared ordinal variables.
2. Exploratory analysis: inspected missingness, age distribution, and distributions of institutional confidence variables.
3. Ordinal regression: modelled trust outcomes using political, cultural, and demographic predictors.
4. Cross-country comparison: compared institutional trust patterns between selected countries.
5. Time-series analysis: examined how confidence in political institutions changed across WVS waves.
6. Bonus clustering: explored grouping patterns among respondents or countries based on selected variables.

Key Findings

The project found that political trust is not explained by one single factor. Instead, it is associated with a combination of institutional confidence, political-system preferences, demographic characteristics, and country-level differences.

• Trust levels differed substantially between countries and across different institutions.
• Ordinal regression was used to model ordered confidence outcomes without collapsing them into a binary variable.
• Time-series analysis allowed the project to compare recent Wave 7 results with longer historical developments.
• Clustering was explored as a bonus analysis to identify broader similarity patterns, although methodological limitations were considered carefully.

Lessons Learned and Improvements

Based on feedback, an important improvement was to present ordinal survey variables more carefully. For example, bar plots are often more appropriate than box plots for ordinal variables, and ordinal categories should not be merged or averaged without a clear methodological justification.

Another important lesson was to describe the model specification clearly: dependent variable, predictors, coding, country subset, and interpretation. This makes the analysis easier to understand and more rigorous.

Outcome

This project strengthened my ability to work with large-scale survey data, handle variable codebooks, prepare data across different waves, and apply ordinal regression to real social-science research questions. It also improved my understanding of cross-country comparison, time-series survey analysis, and the importance of matching visualization methods to the measurement level of the data.