IDEAS home Printed from https://ideas.repec.org/p/zbw/kitwps/100.html
   My bibliography  Save this paper

Selection of questions for VAAs and the VAA-based elections

Author

Listed:
  • Tangian, Andranik S.

Abstract

During the 2016 election to the Student Parliament of the Karlsruhe Institute of Technology (KIT), an experiment on 'The Third Vote' was conducted. The goal was to test an alternative election method based on the idea of internet voting advice applications (VAAs). Under the election method tested, the voters cast no direct votes for candidate parties; rather, they are asked about their preferences on the policy issues as declared in the party manifestos. These embedded referenda measure the degree to which the parties' positions match the policy preferences of the electorate. The parliament seats are then distributed among the parties in proportion to their indices of representativeness: popularity (the average percentage of the population represented on all the issues) and universality (frequency in representing a majority). The Third Vote Experiment reveals that the critical point is the selection of questions: unless they draw sufficient distinctions between the parties, it can cause a malfunction of both the VAA and the VAA-based election method. To solve this problem, this paper develops a model for contrasting as much as possible between the parties by maximizing the total distance between the party policy profiles while simultaneously reducing the number of questions. The guaranteed best solution is obtained by means of an exhaustive search on all the possible combinations of m out of n initial questions. However, since this search is cumbersome, a stepwise removal of questions is proposed. This alternative is shown to offer a good compromise between formal rigor and computational efficiency.

Suggested Citation

  • Tangian, Andranik S., 2017. "Selection of questions for VAAs and the VAA-based elections," Working Paper Series in Economics 100, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    • Handle: RePEc:zbw:kitwps:100
    DOI: 10.5445/IR/1000068508
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/149398/1/876435150.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.5445/IR/1000068508?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. W. J. Krzanowski, 1987. "Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(1), pages 22-33, March.
    2. Kristine Hogarty & Jeffrey Kromrey & John Ferron & Constance Hines, 2004. "Selection of variables in exploratory factor analysis: An empirical comparison of a stepwise and traditional approach," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 593-611, December.
    3. de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
    4. Tangian, Andranik S., 2016. "The third vote experiment: VAA-based election to enhance policy representation of the KIT student parliament," Working Paper Series in Economics 93, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    5. Yutaka Kano & Akira Harada, 2000. "Stepwise variable selection in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 7-22, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tanguiane, Andranick S., 2019. "Combining the third vote with traditional elections," Working Paper Series in Economics 132, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    2. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    3. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    4. Michael Brusco & Renu Singh & Douglas Steinley, 2009. "Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 705-726, December.
    5. Brusco, Michael J. & Steinley, Douglas, 2011. "Exact and approximate algorithms for variable selection in linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 123-131, January.
    6. Wang, Fa, 2017. "Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor-augmented regressions," MPRA Paper 93484, University Library of Munich, Germany, revised 19 May 2019.
    7. Kaoru Fueda & Masaya Iizuka & Yuichi Mori, 2009. "Variable selection in multivariate methods using global score estimation," Computational Statistics, Springer, vol. 24(1), pages 127-144, February.
    8. Seokho Lee & Hyejin Shin & Sang Han Lee, 2016. "Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study," Biometrics, The International Biometric Society, vol. 72(4), pages 1325-1335, December.
    9. Tasos Kalandrakis, 2006. "Roll Call Data and Ideal Points," Wallis Working Papers WP42, University of Rochester - Wallis Institute of Political Economy.
    10. Yang Liu, 2020. "A Riemannian Optimization Algorithm for Joint Maximum Likelihood Estimation of High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 439-468, June.
    11. Wang, Fa, 2022. "Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions," Journal of Econometrics, Elsevier, vol. 229(1), pages 180-200.
    12. Groenen, Patrick J. F. & van de Velden, Michel, 2016. "Multidimensional Scaling by Majorization: A Review," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i08).
    13. Jérome SARACCO & Marie CHAVENT & Vanessa KUENTZ, 2010. "Clustering of categorical variables around latent variables," Cahiers du GREThA (2007-2019) 2010-02, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    14. Michael Greenacre, 2023. "The chi-square standardization, combined with Box-Cox transformation, is a valid alternative to transforming to logratios in compositional data analysis," Economics Working Papers 1857, Department of Economics and Business, Universitat Pompeu Fabra.
    15. van Dijk, A. & van Rosmalen, J.M. & Paap, R., 2009. "A Bayesian approach to two-mode clustering," Econometric Institute Research Papers EI 2009-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. Lash, Michael T. & Sajeesh, S. & Araz, Ozgur M., 2023. "Predicting mobility using limited data during early stages of a pandemic," Journal of Business Research, Elsevier, vol. 157(C).
    17. Sayan Chakraborty & Arnab Bhattacharjee & Taps Maiti, 2021. "Structural Factorization of Latent Adjacency Matrix, with an application to Auto Industry Networks," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 185-206, November.
    18. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    19. Robin, Geneviève & Josse, Julie & Moulines, Éric & Sardy, Sylvain, 2019. "Low-rank model with covariates for count data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 416-434.
    20. Landgraf, Andrew J. & Lee, Yoonkyung, 2020. "Dimensionality reduction for binary data through the projection of natural parameters," Journal of Multivariate Analysis, Elsevier, vol. 180(C).

    More about this item

    Keywords

    policy representation; elections; theory of voting; feature selection; variable selection;
    All these keywords.

    JEL classification:

    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:kitwps:100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/fwkitde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.