The 2019 EES Stacked Data Matrix

2022-10-10

This repository contains the code for building the stacked data matrix (SDM) of the 2019 European Election Studies (EES) voter study. The creation of this SDM is part of the research activities of the ProConEU project, an academic research effort analysing the enlarging gaps between proponents and opponents of the European Union (EU) in terms of party politics, citizen politics, and social media communication. The data pipeline and the general workflow were completed mostly between July 2021 and January 2022 making use of R version 4.1-4.2.

What is an SDM

A SDM consists of a long format data matrix in which each row represents the (dyadic) relationship between two sets of relevant elements.

Among its applications, this data matrix has been extensively used for the study of voting behaviour. In this setting, the SDM observations are usually voter-party dyads, namely dyadic relationships between individual voters and the relevant vote choices available to each individual voter in a given election.

The reason behind the development of the SDM for voting behaviour studies is that it allows to go beyond problems related to the comparability of vote choice across different party systems, especially multi-party ones. By relying on party-voted dyads the SDM allows to address research questions concerning entire party systems, thus enhancing the possibility to develop longitudinal and/or cross-national comparative analyses without:

1. Arbitrarily reducing the number of relevant vote choices (parties) of the system;
2. Reducing the vote alternatives available in a given election to a single property of said alternatives (e.g., party positions on the Left-Right continuum).

Hence, the SDM allows to include in the analyses all the relevant individual-, party-, and context-level factors that might affect the vote choice.

How to build the 2019 EES SDM

1. Fork the repository;
2. Download the EES 2019 voter study dataset, and the related codebook (rename the latter ‘ZA7581_codebook.csv’);
3. Create a new folder in Data, rename it ‘EES2019’ and transfer/paste the EES data here;
4. Run the EES2019_stack script.