Abstract
The scholarly literature provides mixed guidance on the question of whether DREs or optical scan systems inspire greater confidence. We bring new evidence to bear on the debate using a unique exit poll and a nationally representative survey, both of which examine a wide range of voting experiences. Having detailed information about voting experiences enables us to investigate both the direct effects of DRE/optical scan voting and the indirect effects through voting experiences. Doing so reveals new information about the relationships between voting technology, voting experiences, and voter confidence. Indeed, the type of machine one uses has very different direct and indirect effects on voter confidence—a finding that may help explain scholarly disagreement over voters’ reactions to different types of voting machines.
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Notes
Evidence regarding the relationship between general measures of “trust in the government” and general measures of political participation is decidedly mixed (see Levi and Stoker 2000). However, when it comes to a particular form of trust—trust in the electoral process—Alvarez, Hall, and Llewellyn report, “preliminary evidence that suggests confidence in the electoral process affects turnout” (2008, 754).
George W. Bush. 2002. “Remarks by the President at Signing of H.R. 3295, Help America Vote Act of 2002” see: http://www.whitehouse.gov/news/releases/2002/10/20021029-1.html.
See the February 2008 electionline.org briefing “Back to Paper: A case study” at http://www.pewcenteronthestates.org/uploadedFiles/EB21Brief.pdf. See also, Ian Urbina, “Influx of Voters Expected to Test New Technology” New York Times 21 July 2008, pA1.
See Directive 2008–01 at: http://www.sos.state.oh.us/SOS/Upload/elections/directives/2008/Dir2008-01.pdf.
The demographic variables are included primarily as statistical controls to ensure that optical scan voters and DRE voters are comparable on other dimensions.
Summit County uses an ES&S Model 100 precinct count optical scan system and Franklin County uses an ES&S iVotronic DRE touchscreen voting machine.
Due to poll workers refusing (contrary to Ohio law) to allow students to conduct the exit poll in one polling place in Summit County, we only have 49 locations there.
The sampling design is modeled after the sampling done from 1982 through 2006 for the Utah Colleges Exit Poll (Grimshaw et al. 2004).
Appendix A in the online appendix contains question wording for the election experience items and Appendix B contains descriptive statistics for each exit poll variable. In order to use as many surveys with valid responses as possible, we use STATA’s “impute” command to fill-in missing cases—provided they were not missing on the confidence measure and had a valid comparison of the new and old voting systems. STATA’s impute command uses a linear combination of the variables to impute missing values. The number of missing observations on the independent variables is relatively small, most well under 5% of the sample. We also note that the pattern of results is the same even if imputation is not used.
Once the actual turnout for all precincts was available a summary was made so that the actual turnout for all strata was calculated and then this number becomes the numerator of the weight variable. The denominator is the actual number of polling places sampled in the strata by the number of completes at the polling place. This weight by turnout is then normalized by multiplying the ratio of the number of completes to the actual turnout. This accounts for the auxiliary and factual information of actual turnout on election day.
Our data have a Democratic bias, so we used an iterative rim weighting or raking process to calculate a weight based on the U.S. Senate results, multiplied it by the sampling weight described above to create a temporary weight used to adjust the data, then computed a weight based on the gubernatorial results. The process is repeated iteratively until the exit poll results for both races closely approximate the actual election results. But we also note that the results are the same if no survey weight is used.
LISREL uses a linear, maximum likelihood estimator to obtain SEM parameters. Therefore, these parameters retain the usual linear model interpretation (see Diamantopoulos and Siguaw 2000).
Our model is recursive. In part this reflects the cross-sectional nature of our data and limitations of the research design. However, we did use two-stage least squares to assess the possibility that voters’ preconceived ideas about the fairness of the election colored their election day voting experiences. Even when the predetermined variables in the model are used as instruments for voting experiences (combined into a single additive index), the pattern of results is identical.
Technically, LISREL estimates all of the Structural Equation parameters simultaneously. Our point is that demographic controls were included in the models, but they are not reported in Table 3. We omit the demographic variables for two reasons. First, our theoretical interest is not in the indirect effects of the demographic variables, they are included as statistical controls. Second, the overall effects of the demographic variables are generally the same as their direct effects—meaning the indirect effects are of little consequence in contrast to the pattern for optical scan voting. See Appendix E in the online appendix for the full models.
The sample size of the two CCES modules was 1,384 after removing non-voters and accounting for panel attrition.
More information about CCES sampling and fielding techniques is available at: http://web.mit.edu/polisci/portl/cces/.html.
Appendix C in the online appendix contains question wording for the election experience items and Appendix D contains descriptive statistics for each CCES variable.
In order to use as many surveys with valid responses as possible, we again use STATA’s “impute” command to estimate values for independent variables with missing data for all cases not missing on the dependent variable. And again we note that the pattern of results is the same even if imputation is not used.
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Acknowledgments
This material is based upon work supported by Carnegie Corporation of New York, the JEHT Foundation, and by the National Science Foundation under Grant No. 0627880. Exit polls require an immense amount of support from individuals and institutions. Thad Hall collaborated on the development of the survey instruments and the research design. Baxter Oliphant, Nisha Riggs, Dustin Slade, Steven Snell, and research assistants at the Center for the Study of Elections and Democracy at BYU provided valuable research assistance. Howard B. Christensen, Paul Fields, and Dan Williams of the Department of Statistics at Brigham Young University collaborated with us on the sampling design and Dan Williams constructed the sampling weights. John Green, Karl Kaltenthaler, Daniel Coffey, David Cohen, and Steven Brooks at the University of Akron and Rick Robyn at Kent State University collaborated with us on the Summit County data collection. Stephen Mockabee at the University of Cincinnati and Anand Sokhey at Ohio State University collaborated with us on the Franklin County data collection. We also thank Paul Herrnson at the University of Maryland and Richard Niemi at the University of Rochester for sharing data modules from the CCES and for providing helpful feedback on this project. We also thank the anonymous reviewers of this manuscript for their helpful comments. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Carnegie Corporation of New York, the JEHT Foundation, the National Science Foundation, or others who assisted us.
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Claassen, R.L., Magleby, D.B., Monson, J.Q. et al. Voter Confidence and the Election-Day Voting Experience. Polit Behav 35, 215–235 (2013). https://doi.org/10.1007/s11109-012-9202-4
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DOI: https://doi.org/10.1007/s11109-012-9202-4