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Methodological Optimization of the Median Research Agency's Likely Voter Model Based on Findings from Czech Household Panel Study
Kunc, Michal ; Buchtík, Martin (advisor) ; Soukup, Petr (referee)
The aim of this graduate thesis is proposing an optimization of the likely voter model parameter values utilized by Median (research agency) based on secondary analysis of data from the third wave and post-election follow-up of the Czech Household Panel Study 2017 and the Median omnibus survey. The theoretical chapter presents selected aspects of the analyzed likely voter model parameters. Secondary data analysis confirms hypotheses regarding the relationships of: 1) voter turnout, prior voting behavior and the intent to vote, 2) pre-election voting preferences and actual voting behavior, 3) reported prior voting behavior and time elapsed since the prior election. Hypotheses are confirmed, and analysis results are utilized in construction of an optimized likely voter model. This model's results are then compared to the results of four currently or formerly published likely voter models (MEDIAN, CVVM2017, CVVM2018, KANTAR), all computed using an identical dataset (September/October 2017 Median omnibus survey). Based on prior-set comparison criteria, the proposed model has the highest ranking out of all the compared models. Areas of future research proposed, namely exploring the relationship between prior voting behavior misreporting and voting preference trends, in accordance with cognitive...
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Methodological Optimization of the Median Research Agency's Likely Voter Model Based on Findings from Czech Household Panel Study
Kunc, Michal ; Buchtík, Martin (advisor) ; Soukup, Petr (referee)
The aim of this graduate thesis is proposing an optimization of the likely voter model parameter values utilized by Median (research agency) based on secondary analysis of data from the third wave and post-election follow-up of the Czech Household Panel Study 2017 and the Median omnibus survey. The theoretical chapter presents selected aspects of the analyzed likely voter model parameters. Secondary data analysis confirms hypotheses regarding the relationships of: 1) voter turnout, prior voting behavior and the intent to vote, 2) pre-election voting preferences and actual voting behavior, 3) reported prior voting behavior and time elapsed since the prior election. Hypotheses are confirmed, and analysis results are utilized in construction of an optimized likely voter model. This model's results are then compared to the results of four currently or formerly published likely voter models (MEDIAN, CVVM2017, CVVM2018, KANTAR), all computed using an identical dataset (September/October 2017 Median omnibus survey). Based on prior-set comparison criteria, the proposed model has the highest ranking out of all the compared models. Areas of future research proposed, namely exploring the relationship between prior voting behavior misreporting and voting preference trends, in accordance with cognitive...
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