Abstract
In this paper, Gallup results and a questionnaire in the context of a voting advice application related to the Finnish presidential election are combined. The main emphasis is on preprocessing phases where raw data is reformed to temporal data sets. We also pay attention to find optimized parameters for a merged recursive model. Aggregated data from a questionnaire was stored frequently and modified by a differential equation. The method presented in this paper allows us to visualize more accurately the daily support of each candidate before the election. The results can be used for further research such as forecasting the results and the success of presidential campaigns.
Highlights
In this paper, we propose methods for combining two different data sources to get a more accurate estimate of election candidates’ support in time
We introduced novel methods in this paper for combining temporal political support data sets and improved the candidates’ support estimates with the proposed preprocessing methods
We paid attention to find the best parameters for a merged recursive model
Summary
We propose methods for combining two different data sources to get a more accurate estimate of election candidates’ support in time. Repeated surveys are drawn from a population at irregular time intervals during the campaign. The surveys are reformed into time series with regular time intervals. The preprocessed data are combined with a second data source: questionnaire answers in the context of a voting advice application (VAA). VAAs are increasingly popular in democracies worldwide, especially among a group that is often considered ‘apathetic’ about electoral politics: youth [1]. Wrong Gallup designs, as in webpage VAAs, are producing sample proportions that differ systematically from the population [2]. With preprocessing and parameter optimization VAA data can be used to make the support estimates more accurate
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