Political campaigning has become a multi-million dollar business. A substantial proportion of a campaign's budget is spent on voter mobilization, i.e., on identifying and
influencing as many people as possible to vote. Based on data, campaigns use statistical
tools to provide a basis for deciding who to target. While the data available is usually rich,
campaigns have traditionally relied on a rather limited selection of information, often including only previous voting behavior and one or two demographical variables. Statistical
procedures that are currently in use include logistic regression or standard classification
tree methods like CHAID, but there is a growing interest in employing modern data mining approaches. Along the lines of this development, we propose a modern framework
for voter targeting called LORET (for logistic regression trees) that employs trees (with
possibly just a single root node) containing logistic regressions (with possibly just an intercept) in every leaf. Thus, they contain logistic regression and classification trees as special
cases and allow for a synthesis of both techniques under one umbrella. We explore various
flavors of LORET models that (a) compare the effect of using the full set of available
variables against using only limited information and (b) investigate their varying effects
either as regressors in the logistic model components or as partitioning variables in the
tree components. To assess model performance and illustrate targeting, we apply LORET
to a data set of 19,634 eligible voters from the 2004 US presidential election. We find that
augmenting the standard set of variables (such as age and voting history) together with
additional predictor variables (such as the household composition in terms of party affiliation and each individual's rank in the household) clearly improves predictive accuracy.
We also find that LORET models based on tree induction outbeat the unpartitioned competitors. Additionally, LORET models using both partitioning variables and regressors
in the resulting nodes can improve the efficiency of allocating campaign resources while
still providing intelligible models. / Series: Research Report Series / Department of Statistics and Mathematics
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3458 |
| Date | 03 1900 |
| Creators | Rusch, Thomas, Lee, Ilro, Hornik, Kurt, Jank, Wolfgang, Zeileis, Achim |
| Publisher | WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/3458/ |
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