README.Rmd

---
title: "The 2019 EES Stacked Data Matrix"
output: github_document
date: "2022-10-10"
---

This repository contains the code for building the stacked data matrix ([SDM](https://search.gesis.org/research_data/ZA7890)) of the 2019 European 
Election Studies ([EES](https://www.gesis.org/en/services/finding-and-accessing-data/international-survey-programs/european-election-studies)) voter study. The creation of this SDM is 
part of the research activities of the [ProConEU](https://www.mzes.uni-mannheim.de/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](https://cran.r-project.org/) 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](http://europeanelectionstudies.net/european-election-studies/ees-2019-study/voter-study-2019), 
and the related [codebook](https://access.gesis.org/dbk/67448) (rename the latter 'ZA7581_codebook.csv'); 
3. Create a new folder in [`Data`](https://github.com/giucarny/EESstacked/tree/main/Data), 
rename it 'EES2019' and transfer/paste the EES data here;  
4. Run the [`EES2019_stack`](https://github.com/giucarny/EESstacked/blob/NewREADME/Scripts/EES2019_stack.R) script.




```{r libraries, echo=TRUE}



```