Pre-Election Polling: Identifying Likely Voters Using Iterative Expert Data Mining

Murray, Gregg R.; Riley, Chris; Scime, Anthony

doi:10.1093/poq/nfp004

Cited by 28 publications

(9 citation statements)

References 12 publications

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“…Three factors remain, that is, in decreasing order of importance: Voting Attitude, Previous Voting, and Political Interest. This confirms M urray , R iley and S cime (2009), who propose two survey items to identify likely voters, that is, vote intent and previous voting 6…”

Section: Modelssupporting

confidence: 59%

Correcting for survey effects in pre‐election polls

Heij¹,

Franses²

2011

Statistica Neerlandica

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Pre-election polls can suffer from survey effects, causing biases in forecasted election outcomes. We advocate a simple methodology to estimate the magnitude of survey effects, by collecting data both before and after the election.This method is illustrated by means of a field study with data concerning the 2009 European Parliament elections in the Netherlands. Our study provides empirical evidence of significant positive survey effects with respect to voter participation, especially for individuals with low intention to vote. For our data, the overall survey effect on party shares is small. This effect can be more substantial, for example, if political orientation and voting intention are correlated in the sample.

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Section: Modelssupporting

confidence: 59%

Correcting for survey effects in pre‐election polls

Heij¹,

Franses²

2011

Statistica Neerlandica

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“…Perry developed several versions of a multiple-question battery primarily intended to identify who would actually vote versus those who were merely saying they would vote when, in fact, they would not. Several research teams have developed other multiple-question batteries using the most recent American National Election Studies (ANES) validated vote data from 1980, 1984, and 1988(Freedman & Goldstein, 1996Murray, Riley, & Scime, 2009). The most recent election for which we were able to find an analysis of the inaccuracies of self-predicted vote was the 1999 Philadelphia Municipal election.…”

Section: Contribution and Hypothesesmentioning

confidence: 99%

Vote Self-Prediction Hardly Predicts Who Will Vote, and Is (Misleadingly) Unbiased

Rogers¹,

Aida²

2013

American Politics Research

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Public opinion researchers, campaigns, and political scientists often rely on self-predicted vote to forecast turnout, allocate resources, and measure political engagement. Despite its importance, little research has examined the accuracy of self-predicted vote responses. Seven pre-election surveys with postelection vote validation from three elections (N = 29,403) reveal several patterns. First, many self-predicted voters do not actually vote (flakeout). Second, many self-predicted nonvoters do actually vote (flake-in). This is the first robust observation of flake-in. Third, actual voting is more accurately predicted by past voting (from voter file or recalled) than by self-predicted voting. Finally, self-predicted voters differ from actual voters demographically. Actual voters are more likely to be White (and not Black), older, and partisan than actual nonvoters (i.e., there is participatory bias), but self-predicted voters and self-predicted nonvoters do not differ much. Vote self-prediction is "biased" in that it misleadingly suggests that there is no participatory bias.

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“…While regression analysis starts with a statistical model, and tries to adjust its parameters to fit the 1 The most recent articles using data driven RDTs are [14,15]. [16] is one of the first texts of using data driven methods in social sciences, and takes a very critical stance.…”

Section: Regression and Decision Treesmentioning

confidence: 99%

Shaking the trees: Abilities and Capabilities of Regression and Decision Trees for Political Science

Waldhauser

Hochreiter

2017

ITM Web Conf.

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Abstract. When committing to quantitative political science, a researcher has a wealth of methods to choose from. In this paper we compare the established method of analyzing roll call data using W-NOMINATE scores to a data-driven supervised machine learning method: Regression and Decision Trees (RDTs). To do this, we defined two scenarios, one pertaining to an analytical goal, the other being aimed at predicting unknown voting behavior. The suitability of both methods is measured in the dimensions of consistency, tolerance towards misspecification, prediction quality and overall variability. We find that RDTs are at least as suitable as the established method, at lower computational expense and are more forgiving with respect to misspecification.

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Pre-Election Polling: Identifying Likely Voters Using Iterative Expert Data Mining

Cited by 28 publications

References 12 publications

Correcting for survey effects in pre‐election polls

Correcting for survey effects in pre‐election polls

Vote Self-Prediction Hardly Predicts Who Will Vote, and Is (Misleadingly) Unbiased

Shaking the trees: Abilities and Capabilities of Regression and Decision Trees for Political Science

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