<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">RUDN Journal of Sociology</journal-id><journal-title-group><journal-title xml:lang="en">RUDN Journal of Sociology</journal-title><trans-title-group xml:lang="ru"><trans-title>Вестник Российского университета дружбы народов. Серия: Социология</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2313-2272</issn><issn publication-format="electronic">2408-8897</issn><publisher><publisher-name xml:lang="en">Peoples’ Friendship University of Russia named after Patrice Lumamba</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">33933</article-id><article-id pub-id-type="doi">10.22363/2313-2272-2023-23-1-122-141</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Sociological lectures</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Социологический лекторий</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Graphic associative test of attitudes as a convenient implicit measurement tool for mass polls</article-title><trans-title-group xml:lang="ru"><trans-title>Графический ассоциативный тест отношения как удобный инструмент имплицитного измерения в массовых опросах</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Chernozub</surname><given-names>O. L.</given-names></name><name xml:lang="ru"><surname>Чернозуб</surname><given-names>Олег Леонидович</given-names></name></name-alternatives><bio xml:lang="ru">кандидат социологических наук, ведущий научный сотрудник Центра комплексных социальных исследований Института социологии</bio><email>9166908616@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Institute of Sociology of FCTAS RAS</institution></aff><aff><institution xml:lang="ru">Институт социологии ФНИСЦ РАН</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2023-03-16" publication-format="electronic"><day>16</day><month>03</month><year>2023</year></pub-date><volume>23</volume><issue>1</issue><issue-title xml:lang="en">VOL 23, NO1 (2023)</issue-title><issue-title xml:lang="ru">ТОМ 23, №1 (2023)</issue-title><fpage>122</fpage><lpage>141</lpage><history><date date-type="received" iso-8601-date="2023-03-16"><day>16</day><month>03</month><year>2023</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2023, Chernozub O.L.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2023, Чернозуб О.Л.</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="en">Chernozub O.L.</copyright-holder><copyright-holder xml:lang="ru">Чернозуб О.Л.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/sociology/article/view/33933">https://journals.rudn.ru/sociology/article/view/33933</self-uri><abstract xml:lang="en"><p style="text-align: justify;">Several latest elections and referendums were marked by the dramatic failure of electoral forecasts based on mass polls. To respond to the dissatisfaction of the public and politicians, alternative approaches like prediction markets, Implicit Attitude Test (IAT), expectationbased forecasts and so on were developed. IAT proves to be one of the most efficient ways to enrich the forecasting models and improve their accuracy. The problem is that the original form of IAT implies too rigid rules to be applied in the traditional mass poll. As a thorough laboratory-style measurement of nervous reactions to stimuli, IAT requires a special environment, for instance, nothing should disturb or distract respondents from performing experimental tasks. Such an environment is difficult to provide during the mass poll’s fieldwork; thereby, researchers usually implement IAT on small samples. This article presents the Graphic Associative Test of Attitude (GATA) as a tool for mass polls. It is the IAT’s functional analog developed by the author and tested in a wide range of preelectoral mass polls in Russia. GATA is easy to use even with inexperienced interviewers, and its simple and intuitive-clear tasks do not create additional barriers for respondents and do not decrease the response rate. At the same time, in a reliable way, GATA identifies implicit factors of behavior and helps to improve the accuracy of forecast. As a theoretical research, this study proves the ‘dual attitude’ concept of the structural theory of attitude.</p></abstract><trans-abstract xml:lang="ru"><p style="text-align: justify;">Несколько последних выборов и референдумов ознаменовались очевидными провалами предвыборных прогнозов, основанных на массовых опросах избирателей. В ответ на недовольство общественности и политиков стали появляться альтернативные подходы, такие как «рынки прогнозирования», тест неявного отношения (IAT), прогнозы, основанные на ожиданиях, и т.д. IAT представляется одним из наиболее эффективных способов обогащения моделей прогнозирования и повышения их точности. Однако проблема в том, что первоначальная форма IAT устанавливает жесткие методические ограничения, вписаться в которые традиционные массовые опросы просто не могут. Являясь тщательным лабораторным измерением нервных реакций на раздражители, IAT требует создания особой среды, например, ничто не должно беспокоить или отвлекать респондента во время выполнения экспериментальных заданий. Такую среду трудно обеспечить во время массового опроса, и обычно исследователи используют IAT в лабораторных условиях на малых выборках. В статье графический ассоциативный тест отношения (GATA) представлен в качестве способа измерения имплицитной компоненты социальной установки, пригодного для использования в массовых опросах. Насколько можно судить по накопленным данным, это функциональный аналог IAT, протестированный в широком спектре предвыборных массовых опросов в России. Его легко реализовать даже с неопытной сетью интервьюеров, поскольку простые и интуитивно понятные задачи не создают дополнительных барьеров для респондентов и не влияют на уровень отказов. В практическом плане GATA достаточно надежно выявляет имплицитные факторы поведения и помогает повысить точность его прогноза. В теоретическом плане представленные в статье данные подтверждают концепцию «двойственных моделей» структурной теории установки.</p></trans-abstract><kwd-group xml:lang="en"><kwd>prediction of behavior</kwd><kwd>factors of behavior</kwd><kwd>precursors of action</kwd><kwd>dual process</kwd><kwd>two-component model of behavioral factors</kwd><kwd>attitude</kwd><kwd>structural theory of attitude</kwd><kwd>explicit attitude</kwd><kwd>implicit attitude</kwd><kwd>IAT</kwd><kwd>GATA</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>прогнозирование поведения</kwd><kwd>факторы поведения</kwd><kwd>двойственный процесс</kwd><kwd>двухкомпонентная модель факторов поведения</kwd><kwd>установка</kwd><kwd>структурная теория установки</kwd><kwd>эксплицитная компонента установки</kwd><kwd>имплицитная компонента установки</kwd><kwd>тест неявного отношения (IAT)</kwd><kwd>графический ассоциативный тест отношения (GATA)</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Alwin D.F. Feeling thermometers versus 7-point scales: Which are better? Sociological Methods and Research. 1997; 25 (3).</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Anson I.G., Hellwig T. Economic models of voting. Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. Wiley; 2015.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Arcuri L., Castelli L., Galdi S. et al. Predicting the vote: Implicit attitudes as predictors of the future behavior of decided and undecided voters. Political Psychology. 2008; 29.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Arrow K., Forsythe R., Gorham M. et al. The promise of prediction markets. Science. 2008; 320.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Atanasov P. et al. Distilling the wisdom of crowds: Prediction markets versus prediction polls. Academy of Management Proceedings. 2015. https://doi.org/10.5465/AMBPP.2015.15192abstract.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Baskakova Yu. Techniques and methods of political forecasting in the 2016-2018 elections. Elections after the Crimea. Fedorov V. (Ed). Moscow; 2018. (In Russ.).</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Celli F., Stepanov E.A., Poesio M., Riccardi G. Predicting Brexit: Classifying agreement is better than sentiment and pollsters. Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media. Osaka; 2016.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Choma B.L., Hafer C.L. Understanding the relation between explicitly and implicitly measured political orientation: The moderating role of political sophistication. Personality and Individual Differences. 2009; 47.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Chernozub O.L. The two-component model of behavior factors: Evidences of orthogonality of explicit and implicit factors. RUDN Journal of Sociology. 2022; 22 (1).</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Chernozub O.L. Implicit factors and inconsistency of electoral behavior: from a theoretical concept to an empirical phenomenon. Monitoring of Public Opinion: Economic and Social Changes. 2020; No4.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Chernozub O.L. Implicit factors and inconsistency of electoral behavior: From attitude to behavior. Monitoring of Public Opinion: Economic and Social Changes. 2020; 5.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Etkind A.M. The color test of attitude. General Psychodiagnostics. Moscow; 1987. (In Russ.).</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Himmelfarb S., Eagly A.H. Orientations to the study of attitudes and their change. S. Himmelfarb, A.H. Eagly (Eds.). Readings in Attitude Change. New York; 1974.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Fishbein M., Ajzen I. Predicting and Changing Behavior: The Reasoned Action Approach. New York-Hove; 2011.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Ganser C., Riordan P. Vote expectations at the next level. Trying to predict vote shares in the 2013 German Federal Election by polling expectations. Electoral Studies. 2015; 40.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Gayo-Avello D. A meta-analysis of state-of-the-art electoral prediction from Twitter data. Social Science Computer Review. 2013; 31.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Graefe A. Accuracy of vote expectation surveys in forecasting elections. Public Opinion Quarterly. 2014; 78.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Graefe A. Political Markets. Sage Handbook of Electoral Behavior; 2016.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Green D.Ph. On the dimensionality of public sentiment toward partisan and ideological groups. American Journal of Political Science. 1988; 32 (3).</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Greenwald A.G., Poehlman T.A., Uhlmann E.L., Banaji M.R. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology. 2009; 97 (1).</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Greenwald A.G., Smith C.T., Sriram N., Bar-Anan Y., Nosek B.A. Implicit race attitudes predicted vote in the 2008 U.S. Presidential Election. Analyses of Social Issues and Public Policy. 2009; 9.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Jacoby W.G. Feeling thermometers. Candidate Evaluation Conference Proceedings. 1994. URL: http://www.electionstudies.org/conferences/1994Candidate/1994Candidate_Jacoby.pdf.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Kennedy C. et al. An Evaluation of 2016 Election Polls in the United States. URL: https://www.aapor.org/getattachment/Education-Resources/Reports/AAPOR-2016-ElectionPolling-Report.pdf.aspx.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Kiesler Ch.A., Collins B.E., Miller N. Attitude Change. A Critical Analysis of Theoretical Approaches. New York; 1969.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Kou S.G., Sobel M.E. Forecasting the vote: A theoretical comparison of election markets and public opinion polls. Political Analysis. 2004; 12.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Leigh A., Wolfers J. Competing Approaches to Forecasting Elections: Economic Models, Opinion Polling and Prediction Markets. IZA Discussion Papers. No. 1972. Bonn; 2006.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Lewis-Beck M.S., Stegmaier M. Economic models of voting. The Oxford Handbook of Political Behavior. Ed. by J. Dalton, H.-D. Klingemann. Oxford University Press; 2007.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Lupton R.N., Jacoby W.G. The Reliability of the Anes Feeling Thermometers: An optimistic assessment. Presentation at the 2016 Annual Meetings of the Southern Political Science Association. San Juan-Puerto Rico; 2016.</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Lüscher M. The Luscher Color Test. New York; 1990.</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Mamonov M.V., Gavrilov I.V., Vyadro M.A. Imitational features of the 2018 presidential elections and their impact on the next electoral cycle: Results of public opinion polls. Monitoring of Public Opinion: Economic and Social Changes. 2018; 4. (In Russ.).</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Markert С. Test Your Emotions. Wellingborough; 1980.</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Mercer A., Deane C., McGeeney K. Why 2016 election polls missed their mark? URL: http:// www.pewresearch.org/fact-tank/2016/11/09/why-2016-election-polls-missed-their-mark.</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Metaxas P.T., Mustafaraj E., Gayo-Avello D. How (not) to predict elections: Privacy, security, risk and trust. 2011 IEEE Third International Conference on Social Computing. Boston; 2011.</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Murr A.E. The wisdom of crowd: Applying Condorcet’s jury theorem to forecasting US presidential elections. International Journal of Forecasting. 2015; 31.</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>O’Keefe D.J. Persuasion: Theory and Research. Sage; 1990.</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Perugini M. Predictive models of implicit and explicit attitudes. British Journal of Social Psychology. 2005; 44.</mixed-citation></ref><ref id="B37"><label>37.</label><mixed-citation>Roccato M., Zogmaister C. Predicting the vote through implicit and explicit attitudes: A field research. Political Psychology. 2010; 31.</mixed-citation></ref><ref id="B38"><label>38.</label><mixed-citation>Rogers T., Aida M. Why Bother Asking? The Limited Value of Self-Reported Vote Intention. Harvard Kennedy School of Government. Faculty Research Working Paper Series. 2012. URL: http://EconPapers.repec.org/RePEc:hrv:hksfac:7779639.</mixed-citation></ref><ref id="B39"><label>39.</label><mixed-citation>Rothschild D., Wolfers J. Forecasting Elections: Voter Intentions versus Expectations. 2012. URL: https://ssrn.com/abstract=1884644.</mixed-citation></ref><ref id="B40"><label>40.</label><mixed-citation>Sturgis P., Baker N., Callegaro M. at al. Report of the Inquiry into the 2015 British General Election Opinion Polls. London; 2016.</mixed-citation></ref><ref id="B41"><label>41.</label><mixed-citation>Tumasjan A., Sprenger T.O., Sandner P.G., Welpe I.M. Predicting elections with Twitter: What 140 characters reveal about political sentiment. Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. AAAI Press; 2010.</mixed-citation></ref><ref id="B42"><label>42.</label><mixed-citation>Vandenberghe F. On the coming end of sociology. Canadian Review of Sociology = Revue Canadienne de Sociologie. 2019; February. https://doi.org/10.1111/cars.12238.</mixed-citation></ref><ref id="B43"><label>43.</label><mixed-citation>Whiteley P. Four reasons why the polls got the U.S. election result so wrong. URL: http:// www.newsweek.com/polls-2016-us-elections-trump-potus-hillary-clinton-520291.</mixed-citation></ref><ref id="B44"><label>44.</label><mixed-citation>Wilcox C., Sigelman L., Cook E. Some like it hot: Individual differences in responses to group feeling thermometers. Public Opinion Quarterly. 1989; 53 (2).</mixed-citation></ref><ref id="B45"><label>45.</label><mixed-citation>Yarygin G., Yarygin O. Modeling of electoral process: From conceptual model to computer simulation. Azimuth of Science and Research. 2016; (1). (In Russ.).</mixed-citation></ref></ref-list></back></article>
