Extending the Use and Prediction Precision of Subnational Public Opinion Estimation
Bibliographic Data
| ID | 6232067 |
|---|---|
| Authors | Lucas Leemann (0000-0001-5201-869X, University of Essex), Fabio Wasserfallen (0000-0002-5157-7163, University of Salzburg University of Zurich) |
| Year | 2017 |
| Volume | 61 |
| Issue | 4 |
| Pages | 1003-1022 |
| Publication date | 2017-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Political Science (JOURNAL) |
| Journal identifiers | ISSN: 0092-5853 • E-ISSN: 1540-5907 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/ajps.12319 |
| OpenAlex | W2736885773 |
| Language | EN |
| Citations received | 29 |
| References cited | 26 |
The comparative study of subnational units is on the rise. Multilevel regression and poststratification (MrP) has become the standard method for estimating subnational public opinion. Unfortunately, MrP comes with stringent data demands. As a consequence, scholars cannot apply MrP in countries without detailed census data, and when such data are available, the modeling is restricted to a few variables. This article introduces multilevel regression withsyntheticpoststratification (MrsP), which relaxes the data requirement of MrP to marginal distributions, substantially increases the prediction precision of the method, and extends its use to countries without census data. The findings of Monte Carlo, U.S., and Swiss analyses show that, using the same predictors, MrsP usually performs in standard applications as well as the currently used standard approach, and it is superior when additional predictors are modeled. The better performance and the more straightforward implementation promise that MrsP will further stimulate subnational research
Census · Econometrics · Economics · Estimation · Machine learning · Monte Carlo method · Multilevel model · Political science · Politics · Population · Public opinion · Regression · Regression analysis · Sociology · Statistics · Survey data collection · Computer Science · Electoral Systems and Political Participation · Mathematics · Social Capital and Networks · Social Media and Politics
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| Unique citing works | 29 |
|---|---|
| Citations per year | 2,9 |
| Citation span | 2016 - 2026 (11) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 29 |