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For more information, see Google Scholar Citations or ORCID (0000-0002-3976-9365).
Peer-Reviewed Articles and Book Chapters
2023. “Predicting the Issuance of COVID-19 Stay-at-Home Orders in Africa: Using Machine Learning to Develop Insight for Health Policy Research.” Jordan Mansell, Carter Lee Rhea, & Gregg R. Murray. International Journal of Disaster Risk Reduction 88: 103598.
During the COVID-19 pandemic, many countries have issued stay-at-home orders (SAHOs) to reduce viral transmission. Because of their social and economic consequences, SAHOs are a politically risky decision for governments. Researchers typically attribute public health policymaking to five theoretically significant factors: political, scientific, social, economic, and external. However, a narrow focus on extant theory runs the risk of biasing findings and missing novel insights. This research employs machine learning to shift the focus from theory to data to generate hypotheses and insights “born from the data” and unconstrained by current knowledge. Beneficially, this approach can also confirm the extant theory. We apply machine learning in the form of a random forest classifier to a novel and multiple-domain data set of 88 variables to identify the most significant predictors of the issuance of a COVID-19-related SAHO in African countries (n = 54). Our data set includes a wide range of variables from sources such as the World Health Organization that cover the five principal theoretical factors and previously ignored domains. Generated using 1000 simulations, our model identifies a combination of theoretically significant and novel variables as the most important to the issuance of a SAHO and has a predictive accuracy using 10 variables of 78%, which represents a 56% increase in accuracy compared to simply predicting the modal outcome.
Free access: doi.org/10.1016/j.ijdrr.2023.103598.
2023. “Following the Science? Examining the Issuance of Stay-At-Home Orders Related to COVID-19 by U.S. Governors.” Gregg R. Murray & Susan M. Murray. American Politics Research 51(2): 147-160.
Informed by the public health policymaking literature, this study’s objective is to identify scientific, political, social, economic, and external factors related to U.S. governors’ decisions to issue stay-at-home orders (SAHOs) in response to the first wave of the COVID-19 pandemic. Public health experts advocate for social distancing to slow the spread of infectious diseases, but government mandates to social distance can impose substantial social and economic costs. This study uses event history analysis to investigate the issuance of COVID-19-related gubernatorial SAHOs during a 41-day period in the 50 U.S. states. The findings indicate that scientific, political, and economic factors were associated with the issuance of SAHOs, but that external considerations played the largest role, particularly those related to the timing of other governors’ decisions. This study offers evidence about how some U.S. political leaders balance public health concerns against other considerations and, more broadly, how state governments address crisis-level issues.
Open access: doi.org/10.1177/1532673X221106933.
2022. “Assessing the Effects of COVID-19-related Stay-at-Home Orders on Homicide Rates in Selected U.S. Cities.” Gregg R. Murray & Kim Davies. Homicide Studies 26(4): 419-444.
Most U.S. states issued stay-at-home orders (SAHOs) to limit the spread of COVID-19 in 2020. These orders required people to remain in their residences except when undertaking essential activities. While SAHOs are a powerful public health tool against infectious diseases, they can have significant social and economic consequences. Grounded in general strain and routine activities theories and using interrupted time series analyses, this study assesses the effects of SAHOs on homicide rates in 10 U.S. cities. Substantive results suggest SAHOs were associated with changes in homicide rates in theoretically identifiable ways.
Free access: https://doi.org/10.1177/10887679221108875.
2022. “Identifying Factors Related to School Closures Due to COVID-19 in the Middle East and North Africa Region.” Olivia G. Carr, Nadia Jilani-Hyler, & Gregg R. Murray. International Journal of Educational Development.
The COVID-19 pandemic has had devastating effects on the Middle East and North Africa (MENA) region, and MENA states have taken dramatic steps in response. This study focuses on school closures, an intervention that all MENA states adopted, some much earlier than others. It seeks to identify policy factors related to MENA governments’ decisions to close schools during the first wave of the pandemic. Results suggest external issues regarding temporal and geographic diffusion played the largest role. They also indicate that factors related to disease risk, the economy, political institutions, and women’s position in society mattered as well, all of which suggest the decisions were complex.
2022. "Prioritizing Public Health? Factors Affecting the Issuance of Stay-at-home Orders in Response to COVID-19 in Africa." Gregg R. Murray & Joshua Rutland. PLOS Global Public Health e0000112.
COVID-19 has sickened and killed millions of people globally. Conventional non-pharmaceutical interventions, particularly stay-at-home orders (SAHOs), though effective for limiting the spread of disease have significantly disrupted social and economic systems. The effects also have been dramatic in Africa, where many states are already vulnerable due to their developmental status. This study is designed to test hypotheses derived from the public health policymaking literature regarding the roles played by medical and political factors as well as social, economic, and external factors in African countries’ issuance of SAHOs in response to the early stages of the COVID-19 pandemic. Using event history analysis, this study analyzed these five common factors related to public health policy to determine their impact on African states’ varying decisions regarding the issuance of SAHOs. The results of this analysis suggest that medical factors significantly influenced decisions as did factors external to the states, while the role of political factors was limited. Social and economic factors played no discernible role. Overall, this study suggests how African leaders prioritized competing factors in the early stages of a public health crisis.
Open access: https://doi.org/10.1371/journal.pgph.0000112.
2021. "Identifying Factors Associated with the Issuance of Coronavirus-related Stay-at-home Orders in the Middle East and North Africa Region." Gregg R. Murray & Nadia Jilani-Hyler. World Medical & Health Policy 13(3): 477-502.
The COVID-19 pandemic has not spared the Middle East and North Africa (MENA) Region. MENA is one of the most politically, socially, and economically heterogeneous regions in the world, a characteristic reflected in its governments' responses to COVID-19. About two-thirds of these governments issued coronavirus-related stay-at-home orders (SAHOs), one of the most effective tools public health officials have for slowing the spread of infectious diseases. While SAHOs are very effective in terms of countering infectious diseases, they are extremely disruptive in nonhealth domains. The objective of this study is to identify reliable factors related to health care policy making that shaped the decisions of MENA governments to issue a SAHO or not in response to COVID-19. The results identify specific political, social, and medical factors that played important roles and provide a look at early government responses to a global health crisis in a heterogeneous region of the world.
Free, full-text ePrint from Wiley Article Share: https://tinyurl.com/2zjff3vs and https://doi.org/10.1002/whm3.444.
2020. "An Experimental Examination of Demand-Side Preferences for Female and Male National Leaders." Gregg R. Murray & Bruce A. Carroll. Frontiers in Psychology 11: 2364.
.Females constitute a far smaller proportion of political leaders than their proportion in the general population. Leading demand- and supply side explanations for this phenomenon account for some of the variance but leave a great deal unexplained. In an effort to account for additional variance, this research evaluates the issue informed by the biological theory of evolution by natural selection, a foundational explanation for the diversity and function of living organisms. It experimentally assesses how varying types of inter- and intragroup threat–a recurring ancestral problem–affect demand for female and male national leaders. This work analyzes data collected from individuals (N = 826) in the U.S. during the 2012 Cooperative Congressional Election Study. The results suggest the predominant preference for male over female leaders in some contexts may be the non-adaptive and non-functional but lingering outcome of an adaptive preference for physically formidable allies that was shaped by natural selection in ancestral environments.
Open access: https://doi.org/10.3389/fpsyg.2020.576278.
2019. "Watching Eyes and Partisan Mobilization: A Rejoinder to Panagopoulos and van der Linden." Social Influence 14(3-4): 147-151.
This rejoinder addresses concerns raised by Panagopoulos and van der Linden about replication studies of their work conducted by Matland and Murray and published in this journal. Specifically, it offers counterarguments grounded in a broader view of the evidence to the assertion and findings that watching eyes stimuli more effectively mobilize Republican/conservative identifiers to vote than Democratic/liberal identifiers. It concludes that the overall evidence generated by the original and replication research is inconclusive at best.
Free, full-text ePrint access from Taylor & Francis: https://tinyurl.com/vjqxhoy.
2019. "A Second Look at Partisanship’s Effect on Receptivity to Social Pressure to Vote" Richard E. Matland & Gregg R. Murray. Social Influence 14(1): 1-13.
Social pressure can exert a powerful, but sometimes counterproductive, influence on compliance with the social norm of voting. Scholars have tested several implicit social pressure techniques to reduce negative reactions to these methods. Among the most innovative is the use of ‘watching eyes’ in voter mobilization messages. Using three large randomized field experiments, this study attempts to reproduce Panagopoulos and van der Linden’s finding that political partisanship moderates the effect of watching eyes messages on voter turnout. Our findings diverge from previous findings statistically and substantively and indicate partisanship may have limited influence on the effectiveness of watching eyes in mobilizing voters.
Free, full-text ePrint access from Taylor & Francis: https://goo.gl/Qr8VFf.
2017. "Demonstrating the Effect of Evolved Psychological Mechanisms on Partisan Identification Using Perceptions of Political Leaders." J. David Schmitz & Gregg R. Murray. Politics and the Life Sciences 36(2): 60-79.
Partisan identification is a fundamental force in individual and mass political behavior around the world. Informed by scholarship on human sociality, coalitional psychology, and group behavior, this research argues that partisan identification, like many other group-based behaviors, is influenced by forces of evolution. If correct, then party identifiers should exhibit adaptive behaviors when making group-related political decisions. The authors test this assertion with citizen assessments of the relative physical formidability of competing leaders, an important adaptive factor in leader evaluations. Using original and novel data collected during the contextually different 2008 and 2012 U.S. presidential elections as well as two distinct measures obtained during both elections, this study presents evidence that partisans overestimate the physical stature of the presidential candidate of their own party compared to the stature of the candidate of the opposition party. These findings suggest the power of party identification on political behavior may be because modern political parties address problems similar to the problems groups faced in human ancestral times.
Free-to-read, full-text article from Cambridge Core Share: https://goo.gl/A7f5n1.
2017. "Mass Political Behavior and Biology." Gregg R. Murray. In Handbook of Biology and Politics, Steven A. Peterson and Albert Somit (eds.) pp. 247-61, Edward Elgar Publishers.
The objective of this chapter is to introduce readers to research that addresses the effects of biological forces on behaviors central to democracy: voting, forming political opinions, and cognitively engaging public issues. It provides an overview of the literature on mass political behavior and behavioral genetics. This includes the substantial body of heritability studies (e.g., twin studies) and the small but growing number of genomic studies (e.g., genome-wide association studies). Then it introduces political neuroscience, which has been driven by the emergence of fMRI scans and is poised to explore a vast and mostly untouched behavioral territory. Next it reviews biological perspectives on mass political behavior that have not received as much attention but appear to be well-positioned to grow: evolution, biological signals and cues, behavioral endocrinology, and health status. Finally, it speculates about how biologically informed research can help scholars more thoroughly understand mass political behavior.
2016. "These Eyes: A Rejoinder to Panagopoulos on Eyespots and Voter Mobilization." Richard E. Matland & Gregg R. Murray. Political Psychology 37(4): 559-563.
Key words: Field Experiment, Voter Mobilization, Eyespots, Implicit Social Pressure, Voter Turnout, Multiple Hypothesis Testing
2016. "I Only Have Eyes for You: Does Implicit Social Pressure Increase Voter Turnout?" Richard E. Matland & Gregg R. Murray. Political Psychology 37(4): 533-550.
Get-out-the-vote mailers using explicit social pressure consistently increase electoral turnout; however, they often generate a negative reaction or backlash. One approach to increase turnout, yet alleviate backlash, may be to use implicit social pressure. An implicit social pressure technique that has shown promise is to display a set of eyes. Researchers contend eyes generate a feeling of being watched, which cues subjects to act in more pro-social ways to demonstrate compliance with social norms. Several studies support this argument, including two voter mobilization studies. The technique has not been widely tested, however, in the political context. In five randomized field experiments, we test the impact on turnout of mobilization mailers using eye displays. We extend previous research by testing for differences in effects between male and female eyes and across political cultures. The effects are substantively and statistically weak at best and inconsistent with previous findings.
Key words: Field Experiment, Voter Mobilization, Eyespots, Implicit Social Pressure, Voter Turnout
2015. “‘You’ve Gone Too Far’: Social Pressure Mobilization, Reactance, and Individual Differences.” Gregg R. Murray & Richard E. Matland. Journal of Political Marketing 14(4): 333-351.
Important theoretical strides have been made in understanding how to mobilize voters. One especially promising technique encourages voting by suggesting to people their compliance with social norms to vote is being monitored. While several studies register increases in turnout with social pressure techniques, campaigns have failed to adopt them. Our previous research suggests this may be because of voter backlash against these techniques. In this article, we delve more deeply into partisan, sex, and age differences in voter backlash effects in an effort to identify subgroups that may not react to campaigns mobilizing their supporters by using these powerful techniques.
2015. "Classification Trees as Proxies." Anthony Scime, Nilay Saiya, Gregg R. Murray, & Steven J. Jurek. International Journal of Business Analytics 2(2): 31-44.
In data analysis, when data are unattainable, it is common to select a closely related attribute as a proxy. But sometimes substitution of one attribute for another is not sufficient to satisfy the needs of the analysis. In these cases, a classification model based on one dataset can be investigated as a possible proxy for another closely related domain’s dataset. If the model’s structure is sufficient to classify data from the related domain, the model can be used as a proxy tree. Such a proxy tree also provides an alternative characterization of the related domain. Just as important, if the original model does not successfully classify the related domain data the domains are not as closely related as believed. This paper presents a methodology for evaluating datasets as proxies along with three cases that demonstrate the methodology and the three types of results.
2014. "Evolutionary Preferences for Physical Formidability in Leaders." Gregg R. Murray. Politics and the Life Sciences 33(1): 33-53.
This research uses evolutionary theory to evaluate followers’ preferences for physically formidable leaders and to identify conditions that stimulate those preferences. It employs a population-based survey experiment (N ≥ 760), which offers the advantages to internal validity of experiments and external validity of a highly heterogeneous sample drawn from a nationally representative subject pool. The theoretical argument proffered here is followers tend to prefer leaders with greater physical formidability because of evolutionary adaptations derived from humans’ violent ancestral environment. In this environment, individuals who allied with and ultimately followed physically powerful partners were more likely to acquire and retain important resources necessary for survival and reproduction because the presence of the physically powerful partner cued opponents to avoid a challenge for the resources or risk a costly confrontation. This argument suggests and the results indicate that threatening (war) and non-threatening (peace, cooperation, and control) stimuli differentially motivate preferences for physically formidable leaders. In particular, the findings suggest threatening conditions lead to preferences for leaders with more powerful physical attributes, both anthropometric (i.e., weight, height, and body mass index) and non-anthropometric (i.e., attributes of being “physically imposing or intimidating” and “physically strong”). Overall, this research offers a theoretical framework from which to understand this otherwise seemingly irrational phenomenon. Further, it advances the emerging but long-neglected investigation of biological effects on political behavior and has implications for a fundamental process in democratic society, leader selection.
2014. “Mobilization Effects Using Mail: Social Pressure, Descriptive Norms, and Timing.” Gregg R. Murray & Richard E. Matland. Political Research Quarterly 67(2): 304-319.
This research contributes to the voter mobilization and voter turnout literatures. We use field experiments in Texas and Wisconsin to evaluate the effectiveness of non-partisan get-out-the-vote (GOTV) messages delivered via mail during the 2010 gubernatorial campaigns. We manipulate three factors associated with our GOTV messages: social pressure, the consistency of descriptive and injunctive voting norms, and the timing of message reception. We find GOTV mobilization efforts increase turnout, but the effects vary across states, across citizens (based on voting propensity), and across message texts. We present an initial field-based confirmation of the hypothesis that norm-consistent messages are effective at increasing turnout. We demonstrate significant timing effects, which are mediated by state election rules. Finally, we find social pressure’s effectiveness varies significantly more than indicated by previously published tests. Overall, these results indicate there is considerable variation in the impact of voter mobilization techniques and suggest researchers place a greater emphasis on context when running experiments and evaluating the effects of mobilization messages.
2013. “Voters versus Terrorists: Analyzing the Effect of Terrorist Events on Voter Turnout.” Joseph Robbins, Lance Y. Hunter, & Gregg R. Murray. Journal of Peace Research 50(4): 495-508.
Scholars and policy makers commonly assume terrorism is intended to affect a broader audience beyond the physically targeted victims. Informed by scholarship regarding the effects of heuristics and emotion on political cognition and behavior, we evaluate the impact of terrorism on the broader audience of the electorate as manifested by voter turnout. We hypothesize increased terrorism is associated with increased voter turnout. In particular, we invoke the Affective Intelligence model and related findings that emotion plays a key role in individuals’ political cognition and behavior. Following this perspective, we argue that terrorist attacks are threatening and novel political events that induce anxiety in the electorate, which, in turn, leads individuals to scrutinize the political environment more closely and to ascribe greater salience to proximate political events. As a result of this increased concern with the political environment and increased salience of upcoming elections, we expect voter turnout to increase. While conventional explanations of turnout are important, they do not capture the effect of emotions despite other well-known relationships, such as attitudinal responses to international political crises (e.g., the rally-around-the-flag effect). Our cross-national analyses, which include 51 democracies and use two geographically and definitionally distinct data sets, indicate that the positive relationship between terrorism and turnout is non-trivial and robust.
2013. “An Experimental Test for ‘Backlash’ Against Social Pressure Techniques Used to Mobilize Voters.” Richard E. Matland & Gregg R. Murray. American Politics Research 41(3): 358-385.
This research explores the possibility of psychological reactance, or “backlash,” against political candidates who use social pressure to mobilize voters. There is a compelling theoretical argument and solid empirical evidence suggesting social pressure substantially increases voter turnout. There is, however, equally noteworthy evidence suggesting social pressure frequently stimulates a negative reaction in targets. This research uses a lab-in-the-field experimental design that employs a hypothetical social pressure message to evaluate whether a candidate’s use of social pressure to turnout voters may increase anger and hostility toward that candidate, possibly to the point it increases the likelihood a citizen will actually vote against that candidate. Our findings indicate social pressure mobilization techniques evoke consequential psychological reactance against their sponsor. Until future research can further assess these effects, we suggest social pressure mobilization techniques should be used by campaigns only after careful consideration.
2013. “Convenient Yet Not a Convenience Sample: Jury Pools as Experimental Subject Pools.” Gregg R. Murray, Cynthia R. Rugeley, Dona-Gene Mitchell, & Jeffery J. Mondak. Social Science Research 42(1): 246-253.
Scholars greatly benefit from access to convenient, inexpensive data sources. Many researchers rely on student subject pools, a practice that raises concern about the “college sophomore problem,” or the possibility that findings from student subjects do not generalize beyond the campus. As an accessible, low cost, and heterogeneous data source, some researchers have used subjects recruited from jury pools, which are drawn from randomly-selected citizens required by law to appear for jury duty. In this paper, we discuss the strengths and weaknesses of this approach. First, we review pragmatic considerations involving access to jury pools, substantive content, the administration of survey-experiments, and the financial costs and benefits of this approach. Next, we present evidence regarding the quality of jury pool samples in terms of response rates, diversity, and representativeness. We conclude that jury pools, given proper attention to their limitations, offer an attractive addition to the viable sources of experimental data.
2013. “Social Science Data Analysis: The Ethical Imperative.” Anthony Scime & Gregg R. Murray. In Ethical Data Mining Applications for Socio-Economic Development, Hakikur Rahman & Isabel Ramos (eds.), pp. 131-147, IGI Global.
Social scientists address some of the most pressing issues of society such as health and wellness, government processes and citizen reactions, individual and collective knowledge, working conditions and socio-economic processes, and societal peace and violence. In an effort to understand these and many other consequential issues, social scientists invest substantial resources to collect large quantities of data, much of which are not fully explored. This chapter proffers the argument that the privacy protection and responsible use are not the only ethical considerations related to data mining social data. Given (1) the substantial resources allocated and (2) the leverage these “big data” give on such weighty issues, this chapter suggests social scientists are ethically obligated to conduct comprehensive analysis of their data. Data mining techniques provide pertinent tools that are valuable for identifying attributes in large data sets that may be useful for addressing important issues in the social sciences. By using these comprehensive analytical processes, a researcher may discover a set of attributes that is useful for making behavioral predictions, validating social science theories, and creating rules for understanding behavior in social domains. Taken together, these attributes and values often present previously unknown knowledge that may have important applied and theoretical consequences for a domain, social scientific or otherwise. This chapter concludes with examples of important social problems studied using various data mining methodologies including ethical concerns.
2012. “An Experimental Test of Mobilization Effects in a Latino Community.” Richard E. Matland & Gregg R. Murray. Political Research Quarterly 65(1): 192-205.
This article describes a field experiment designed to test the efficacy of get-out-the-vote (GOTV) techniques in a new context and for an understudied population. It evaluates the effectiveness of nonpartisan GOTV messages delivered via personal contact and mail in a heavily Latino community during the 2004 presidential campaign. It proposes and tests an alternative model of voter turnout based on Zaller’s receive accept–sample model of public opinion. The findings are consistent with the authors’ predictions; mobilization efforts increase turnout, but mobilization effects vary across citizens based on their propensity to vote. There is a large increase among episodic voters but little increase among habitual or registered nonvoters.
2012. “Parenting Styles, Socialization, and the Transmission of Political Ideology and Partisanship.” Gregg R. Murray & Matthew K. Mulvaney. Politics and Policy 40(6): 1106-1130.
While research has long shown that parents are first and foremost among the agents of political socialization, substantial evidence suggests there is a great deal of variation in the transmission of political values from parents to their children. This article attempts to explain some of this variation by examining how parenting style—as represented by the parent–child relational context in terms of dimensions of parental control and affect—affects the intergenerational transmission of political attributes. In particular, it evaluates how differences in parenting style influence the intergenerational transmission of political ideology and partisan identification. Findings based on original data collected from a sample of mother–offspring dyads show that differences in parenting styles play an important moderating role in the variable transmission of parental political values. Further, these results add a new dimension to the study of political socialization by demonstrating the role of parenting styles in the transmission of political values.
2011. “Caveman Politics: Evolutionary Leadership Preferences and Stature.” Gregg R. Murray & J. David Schmitz. Social Science Quarterly 92(5): 1215-1235.
Objective: Following evolutionary psychology, we argue that physical stature matters in preferences regarding political leadership. Particularly, a preference for physically formidable leaders evolved to promote survivability in the violent human ancestral history. Methods: We present two studies of original data to assess individual attitudes regarding the association between physical stature and political leadership. Analytical methods include ordered probit regression. Results: The findings are consistent with the evolutionary theory presented here. Study 1 indicates that individuals tend to prefer leaders with greater physical stature, while Study 2 indicates that males with greater physical stature are more likely to think of themselves as qualified to be a leader and, through this increased sense of efficacy, they are more likely to demonstrate interest in pursuing a leadership position. Conclusion: Consistent with emerging evidence from other research perspectives, political behavior, in this case preferences regarding political leadership, is shaped by both environmental and evolutionary forces.
Free-to-read, full-text article from Wiley Content Sharing: https://rdcu.be/bcmtS.
2011. “Caveman Executive Leadership: Evolved Leadership Preferences and Biological Sex.” Gregg R. Murray & Susan M. Murray. In Evolutionary Psychology in the Business Sciences, Gad Saad (ed.), pp. 135-163, Springer.
There is increasing recognition that human behavior in general, and business behavior in particular, is subject to social and biological effects. This research investigates the well-known but unsatisfactorily explained advantage that males have over females in obtaining executive leadership. We argue that environmental-cultural explanations are incomplete and propose an explanation that adds to the emerging evidence that behavior is subject to evolutionary effects. More specifically, we take the perspective of evolutionary psychology in this research. The explanation presented here is grounded in the evolutionary theory of natural selection such that a psychological adaptation for a preference for male leaders evolved to promote individual survivability in the violent ancestral history of humans. We present convergent interdisciplinary findings as well as supporting evidence from three studies with distinct research designs, domains, and perspectives of analysis to strengthen the validity of our argument. In all, this research offers a more complete theoretical explanation for male predominance in executive leadership and provides an additional theoretical approach to the investigation of modern biases that have been costly to the business community.
2011. “Finding Persistent Strong Rules: Using Classification to Improve Association Mining.” Anthony Scime, Karthik Rajasethupathy, Kulathur S. Rajasethupathy, & Gregg R. Murray. In Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains, A.V. Senthil Kumar (ed.), pp. 85-107, IGI Global.
Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.
2010. “Microtargeting and Electorate Segmentation: Data Mining the American National Election Study.” Gregg R. Murray & Anthony Scime. Journal of Political Marketing 9(3): 143-166.
Business marketers widely use data mining for segmenting and targeting markets. To assess data mining for use by political marketers, we mined the 1948 to 2004 American National Elections Studies data file to identify a small number of variables and rules that can be used to predict individual voting behavior, including abstention, with the intent of segmenting the electorate in useful and meaningful ways. The resulting decision tree correctly predicts vote choice with 66 percent accuracy, a success rate that compares favorably with other predictive methods. More importantly, the process provides rules that identify segments of voters based on their predicted vote choice, with the vote choice of some segments predictable with up to 87 percent success. These results suggest that the data mining methodology may increase efficiency for political campaigns, but they also suggest that, from a democratic theory perspective, overall participation may be improved by communicating more effective messages that better inform intended voters and that motivate individuals to vote who otherwise may abstain.
2010. “Testing Terrorism Theory with Data Mining.” Anthony Scime, Gregg R. Murray, & Lance Y. Hunter. International Journal of Data Analysis Techniques and Strategies 2(2): 122-139.
This research demonstrates the application of multiple data mining techniques to test theories of the macro-level causes of terrorism. The unique dataset is comprised of terrorist events and measures of social, political and economic contexts in 185 countries worldwide between the years 1970 and 2004. The theories are assessed using the iterative expert data mining (IEDM) methodology with classification mining and then association mining. The resulting 100 rules suggest that the level of democracy in a country is an integral part of the explanation for terrorism. This research shows that a multi-method data mining approach can be used to test competing theories in a discipline by analysing large, comprehensive datasets that capture multiple theories and include large numbers of records.
2009. “Pre-Election Polling: Identifying Likely Voters Using Iterative Expert Data Mining.” Gregg R. Murray, Christopher Riley, & Anthony Scime. Public Opinion Quarterly 73(1): 159-171.
One often-noted difficulty in pre-election polling is the identification of likely voters. Our objective is to build a likely voter model for presidential elections that efficiently balances accuracy and number of questions used. We employ the Iterative Expert Data Mining technique and data from the American National Election Studies to identify a small number of survey questions that can be used to classify likely voters while maintaining or surpassing the accuracy rates of other models. Specifically, we propose two survey items that together correctly classify 78 percent of respondents as voters or nonvoters over a multielection, multidecade period. We argue that our proposed model compares favorably to competing models by capturing the successful elements of those models while ignoring other elements that constrain identification. We end by suggesting that our model offers a new approach to identifying and evaluating likely voters that may maintain or increase accuracy without also increasing cost.
2009. “Finding ‘Persistent Rules’: Combining Association and Classification Results.” Karthik Rajasethupathy, Anthony Scime, Kulathur S. Rajasethupathy, & Gregg R. Murray. Expert Systems with Applications 36: 6019-6024.
Different data mining algorithms applied to the same data can result in similar findings, typically in the form of rules. These similarities can be exploited to identify especially powerful rules, in particular those that are common to the different algorithms. This research focuses on the independent application of association and classification mining algorithms to the same data to discover common or similar rules, which are deemed “persistent-rules." The persistent-rule discovery process is demonstrated and tested against two data sets drawn from the American National Election Studies: one data set used to predict voter turnout and the second used to predict vote choice.
2008. “Data Mining in the Social Sciences and Iterative Attribute Elimination.” Anthony Scime, Gregg R. Murray, Wan Huang, & Carol Brownstein-Evans. In Data Mining and Knowledge Discovery Technologies, David Taniar (ed.), pp. 308-332, IGI Global.
Immense public resources are expended to collect large stores of social data, but often these data are under-examined thereby missing potential opportunities to shed light on some of society’s pressing problems. This chapter proposes and demonstrates data mining in general and an iterative attribute-elimination process in particular as important analytical tools to exploit more fully these important data from the social sciences. We use an iterative domain-expert and data mining process to identify attributes that are useful for addressing distinct and nontrivial research issues in social science—presidential vote choice and living arrangement outcomes for maltreated children—using the American National Election Studies (ANES) from political science and the National Survey on Child and Adolescent Well-Being (NSCAW) from social work. We conclude that data mining is useful for more fully exploiting important but under-evaluated data collections for the purpose of addressing some important questions in the social sciences.
2007. “Do You See What I See? Perceptions of Party Differences and Voting Behavior.” Craig Goodman & Gregg R. Murray. American Politics Research 35(6): 905-931.
We approach the issues of partisanship and voting behavior by focusing specifically on a seldom-studied group—the substantial proportion of citizens who see little to no important differences between the major parties. Motivated by the heuristics and burgeoning behavioral economics literatures, we conclude that party cues help reduce uncertainty for voters. More specifically, for voters lacking these cues, we expect that there will be a bias toward the incumbent candidate or party, which is motivated by the desire to decrease the potential costs of postdecision regrets. Similarly, we expect that these individuals are likely to delay choosing between candidates and may abstain from voting altogether, which is driven by a shortage of justifications on which to base the decision. We develop measures of perceived party differences based on symbolic and operational differences using data from the American National Election Study and find significant support for our hypotheses in the context of presidential elections.
2007. “Vote Prediction by Iterative Domain Knowledge and Attribute Elimination.” Anthony Scime & Gregg R. Murray. International Journal of Business Intelligence and Data Mining 2(2): 160-176.
Data mining the American National Election Study (ANES), a rich but disparate source of information about Americans' vote choices, is the focus of this research. Specifically, we use data mining classification to construct a decision tree to select important predictors of the vote from the more than 900 items that compose the ANES. We use an iterative domain expert and data mining process to identify a limited number of survey questions intended to predict for which party an individual will vote in a presidential election or whether that individual will vote at all.
2003. “Raising Considerations: Public Opinion and the Fair Application of the Death Penalty.” Gregg R. Murray. Social Science Quarterly 84(4): 753-770.
Objectives: A major justification for capital punishment is its perceived public support, yet common measures of public opinion do not capture the complexity of death penalty attitudes. This research, first, examines the stability of attitudes regarding the fair application of the death penalty when those attitudes are expressed within the context of an enlarged pool of considerations about its administration and, second, evaluates the directional effect of the considerations on those attitudes. Methods: Data from a national telephone survey that capture the complexity of these attitudes are analyzed using ordered probit estimation. Results: These results indicate substantial instability in attitudes regarding the fair application of capital punishment given the context of more pertinent considerations. Furthermore, within this context respondents tend to indicate that the death penalty is less fairly applied. Conclusion: The justification for capital punishment may rest on oversimplified conceptions of attitudes toward the death penalty and its application.
Free-to-read, full-text article from Wiley Content Sharing: https://rdcu.be/bcmwQ.
Gregg R. Murray, Ph.D. / Department of Social Sciences / Augusta University / Augusta, GA 30904 USA / firstname.lastname@example.org