A New Approach to the Study of Ticket Splitting

1998 ◽  
Vol 92 (3) ◽  
pp. 533-544 ◽  
Author(s):  
Barry C. Burden ◽  
David C. Kimball
Keyword(s):  
Survey Data ◽  
Aggregate Data ◽  
Divided Government ◽  
Ecological Fallacy ◽  
Ecological Inference ◽  
High Quality ◽  
Inference Problem ◽  
New Approach ◽  
Congressional Districts ◽  
Individual Level

A new solution to the ecological inference problem is used to examine split-ticket voting patterns across states and congressional districts in the 1988 elections. Earlier studies of ticket splitting used either aggregate data, which suffer from the “ecological fallacy” and threaten individual-level inferences, or survey data from small, unrepresentative samples. We produce more accurate estimates of the proportions of voters splitting their ballots in each state and district, which enables us to examine variations across geographical units. We also clarify the connection between ticket splitting and divided government and test several competing theories about the causes of both. We find, contrary to balancing arguments, that voters are not intentionally splitting tickets to produce divided government and moderate policies. In most cases split outcomes are a by-product of lopsided congressional campaigns that feature well-funded, high-quality candidates versus unknown competitors.

Iff the Assumption Fits…: A Comment on the King Ecological Inference Solution

1998 ◽  
Vol 7 ◽  
pp. 143-163 ◽  
Author(s):  
Wendy K. Tam Cho
Keyword(s):  
Aggregate Data ◽  
Extended Model ◽  
Extended Version ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Basic Model ◽  
Proposed Model

I examine a recently proposed solution to the ecological inference problem (King 1997). It is asserted that the proposed model is able to reconstruct individual-level behavior from aggregate data. I discuss in detail both the benefits and limitations of this model. The assumptions of the basic model are often inappropriate for instances of aggregate data. The extended version of the model is able to correct for some of these limitations. However, it is difficult in most cases to apply the extended model properly.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

scholarly journals The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level

2021 ◽  
Author(s):  
Shiro Kuriwaki ◽  
Stephen Ansolabehere ◽  
Angelo Dagonel ◽  
Soichiro Yamauchi
Keyword(s):  
Survey Data ◽  
Total Variation ◽  
The United States ◽  
District Level ◽  
Congressional District ◽  
Congressional Districts ◽  
Individual Level ◽  
National Differences ◽  
Racial Polarization ◽  
Hispanic Voters

Voting in the United States has long been known to divide sharply along racial lines, and the degree of racially polarized voting evidently varies across regions, and even within a state. Researchers have further studied variation in racially polarized voting using aggregate data techniques, but these methods assume that variation in individual preferences is not related to geography. This paper presents estimates based on individual level data of the extent and variation in racially polarized voting across US Congressional Districts. Leveraging large, geocoded sample surveys, we develop an improved method for measuring racial voting patterns at the Congressional District-level. The method overcomes challenges in previous attempts of survey modeling by allowing survey data to inform the synthetic population model. This method has sufficient power to provide precise estimates of racial polarization even when survey data are sparse. We find that variation across districts but within states explains roughly 20 percent of the total variation; states explain a further 20 percent of the total variation, and 55 percent of the variation is simply national differences between races. The Deep South still has the highest racial polarization between White and Black voters, but some Midwestern congressional districts exhibit comparably high polarization. The polarization between White and Hispanic voters is far more variable than between Black and White voters.

“Ecological” Inference: The Use of Aggregate Data to Study Individuals

1969 ◽  
Vol 63 (4) ◽  
pp. 1183-1196 ◽  
Author(s):  
W. Phillips Shively
Keyword(s):  
Research Data ◽  
Aggregate Data ◽  
Ecological Inference ◽  
Nineteen Fifties ◽  
Unit Of Analysis ◽  
Census Tracts ◽  
Individual Level ◽  
Ecological Regression ◽  
Ecological Correlation ◽  
Standing Problem

Because they are inexpensive and easy to obtain, because they may be available under circumstances in which survey data are unavailable, and because they eliminate many of the measurement problems of survey research, data on geographic units such as counties or census tracts are often used by political scientists to measure individual behavior. This has involved us in the long-standing problem of inferring individual-level relationships from aggregate data, which was first raised by W. S. Robinson in the early nineteen fifties.In this paper, I shall first discuss the problem raised by Robinson. I shall then review three partial solutions to the problem—the Duncan-Davis method of setting limits, Blalock's version of ecological regression, and Goodman's version of ecological regression. Finally, I shall propose some ways in which Goodman's method may be used so as to reduce the problem of bias in its estimates, and make it a more reasonable tool for reserch.Our difficulty, as Robinson showed, is that we cannot necessarily infer the correlation between variables, taking people as the unit of analysis, on the basis of correlations between the same variables based on groups of people as units. For example, the “ecological” correlation between per cent black and per cent illiterate is +0.946, whereas the correlation between color and illiteracy among individuals is only+0.203.

Individuals Writ Large: An Epilogue on the “Ecological Fallacy”

1989 ◽  
Vol 1 ◽  
pp. 235-269 ◽  
Author(s):  
Lutz Erbring
Keyword(s):  
Aggregate Data ◽  
Ecological Fallacy ◽  
Individual Data ◽  
Group Level ◽  
Social Scientists ◽  
Ecological Analysis ◽  
Individual Level ◽  
Data Group

For more than three decades, social scientists have struggled with the statistical consequences of aggregation. Ever since Robinson (1950) first shocked a whole generation of social scientists with his demonstration of the “ecological fallacy,” much has been written about alleged fallacies, biases, pitfalls, and hazards of one kind or another lurking behind aggregate data and about strategies for circumventing them (Goodman, 1953, 1959; Blalock 1964; Scheuch 1966; Alker 1969; Shively 1969, 1974; Hannan 1971; Hammond 1973; Meckstroth 1974; Hanushek, Jackson, and Kain 1974; Hannan and Burstein 1974; Irwin and Lichtman 1976; Smith 1977; Langbein and Lichtman 1978). Intrigued—or alarmed—by the recurrent observation that correlations and regressions based on aggregate data (group means) often differ dramatically from those based on individual data, researchers have sought to answer the traditional question of ecological analysis: under what conditions can inferences to individual-level (micro) relationships be made from group-level (macro) data?

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