Pub Date : 2021-06-17DOI: 10.1080/2330443X.2023.2190368
Banafsheh Behzad, Bhavana Bheem, D. Elizondo, Deyana Marsh, Susan E. Martonosi
In recent years, scholars have raised concerns on the effects that unreliable news, or"fake news,"has on our political sphere, and our democracy as a whole. For example, the propagation of fake news on social media is widely believed to have influenced the outcome of national elections, including the 2016 U.S. Presidential Election, and the 2020 COVID-19 pandemic. What drives the propagation of fake news on an individual level, and which interventions could effectively reduce the propagation rate? Our model disentangles bias from truthfulness of an article and examines the relationship between these two parameters and a reader's own beliefs. Using the model, we create policy recommendations for both social media platforms and individual social media users to reduce the spread of untruthful or highly biased news. We recommend that platforms sponsor unbiased truthful news, focus fact-checking efforts on mild to moderately biased news, recommend friend suggestions across the political spectrum, and provide users with reports about the political alignment of their feed. We recommend that individual social media users fact check news that strongly aligns with their political bias and read articles of opposing political bias.
{"title":"Prevalence and Propagation of Fake News","authors":"Banafsheh Behzad, Bhavana Bheem, D. Elizondo, Deyana Marsh, Susan E. Martonosi","doi":"10.1080/2330443X.2023.2190368","DOIUrl":"https://doi.org/10.1080/2330443X.2023.2190368","url":null,"abstract":"In recent years, scholars have raised concerns on the effects that unreliable news, or\"fake news,\"has on our political sphere, and our democracy as a whole. For example, the propagation of fake news on social media is widely believed to have influenced the outcome of national elections, including the 2016 U.S. Presidential Election, and the 2020 COVID-19 pandemic. What drives the propagation of fake news on an individual level, and which interventions could effectively reduce the propagation rate? Our model disentangles bias from truthfulness of an article and examines the relationship between these two parameters and a reader's own beliefs. Using the model, we create policy recommendations for both social media platforms and individual social media users to reduce the spread of untruthful or highly biased news. We recommend that platforms sponsor unbiased truthful news, focus fact-checking efforts on mild to moderately biased news, recommend friend suggestions across the political spectrum, and provide users with reports about the political alignment of their feed. We recommend that individual social media users fact check news that strongly aligns with their political bias and read articles of opposing political bias.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47609705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-25DOI: 10.1080/2330443X.2022.2050327
W. Miao, Qing Pan, J. Gastwirth
Abstract In December 2020, Texas filed a motion to the U.S. Supreme Court claiming that the four battleground states: Pennsylvania, Georgia, Michigan, and Wisconsin did not conduct their 2020 presidential elections in compliance with the Constitution. Texas supported its motion with a statistical analysis purportedly demonstrating that it was highly improbable that Biden had more votes than Trump in the four battleground states. This article points out that Texas’s claim is logically flawed and the analysis submitted violated several fundamental principles of statistics.
{"title":"A Misuse of Statistical Reasoning: The Statistical Arguments Offered by Texas to the Supreme Court in an Attempt to Overturn the Results of the 2020 Election","authors":"W. Miao, Qing Pan, J. Gastwirth","doi":"10.1080/2330443X.2022.2050327","DOIUrl":"https://doi.org/10.1080/2330443X.2022.2050327","url":null,"abstract":"Abstract In December 2020, Texas filed a motion to the U.S. Supreme Court claiming that the four battleground states: Pennsylvania, Georgia, Michigan, and Wisconsin did not conduct their 2020 presidential elections in compliance with the Constitution. Texas supported its motion with a statistical analysis purportedly demonstrating that it was highly improbable that Biden had more votes than Trump in the four battleground states. This article points out that Texas’s claim is logically flawed and the analysis submitted violated several fundamental principles of statistics.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"9 1","pages":"67 - 73"},"PeriodicalIF":1.6,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46560436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-19DOI: 10.1080/2330443X.2022.2071369
Mikaela Meyer, Ahmed Hassafy, G. Lewis, Prasun Shrestha, A. Haviland, D. Nagin
Abstract We estimate changes in the rates of five FBI Part 1 crimes during the 2020 spring COVID-19 pandemic lockdown period and the period after the killing of George Floyd through December 2020. We use weekly crime rate data from 28 of the 70 largest cities in the United States from January 2018 to December 2020. Homicide rates were higher throughout 2020, including during early 2020 prior to March lockdowns. Auto thefts increased significantly during the summer and remainder of 2020. In contrast, robbery and larceny significantly declined during all three post-pandemic periods. Point estimates of burglary rates pointed to a decline for all four periods of 2020, but only the pre-pandemic period was statistically significant. We construct a city-level openness index to examine whether the degree of openness just prior to and during the lockdowns was associated with changing crime rates. Larceny and robbery rates both had a positive and significant association with the openness index implying lockdown restrictions reduced offense rates whereas the other three crime types had no detectable association. While opportunity theory is a tempting post hoc explanation of some of these findings, no single crime theory provides a plausible explanation of all the results. Supplementary materials for this article are available online.
{"title":"Changes in Crime Rates during the COVID-19 Pandemic","authors":"Mikaela Meyer, Ahmed Hassafy, G. Lewis, Prasun Shrestha, A. Haviland, D. Nagin","doi":"10.1080/2330443X.2022.2071369","DOIUrl":"https://doi.org/10.1080/2330443X.2022.2071369","url":null,"abstract":"Abstract We estimate changes in the rates of five FBI Part 1 crimes during the 2020 spring COVID-19 pandemic lockdown period and the period after the killing of George Floyd through December 2020. We use weekly crime rate data from 28 of the 70 largest cities in the United States from January 2018 to December 2020. Homicide rates were higher throughout 2020, including during early 2020 prior to March lockdowns. Auto thefts increased significantly during the summer and remainder of 2020. In contrast, robbery and larceny significantly declined during all three post-pandemic periods. Point estimates of burglary rates pointed to a decline for all four periods of 2020, but only the pre-pandemic period was statistically significant. We construct a city-level openness index to examine whether the degree of openness just prior to and during the lockdowns was associated with changing crime rates. Larceny and robbery rates both had a positive and significant association with the openness index implying lockdown restrictions reduced offense rates whereas the other three crime types had no detectable association. While opportunity theory is a tempting post hoc explanation of some of these findings, no single crime theory provides a plausible explanation of all the results. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"9 1","pages":"97 - 109"},"PeriodicalIF":1.6,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42372574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-19DOI: 10.1080/2330443X.2022.2086190
R. Heyard, Manuela Ott, G. Salanti, M. Egger
Abstract Funding agencies rely on peer review and expert panels to select the research deserving funding. Peer review has limitations, including bias against risky proposals or interdisciplinary research. The inter-rater reliability between reviewers and panels is low, particularly for proposals near the funding line. Funding agencies are also increasingly acknowledging the role of chance. The Swiss National Science Foundation (SNSF) introduced a lottery for proposals in the middle group of good but not excellent proposals. In this article, we introduce a Bayesian hierarchical model for the evaluation process. To rank the proposals, we estimate their expected ranks (ER), which incorporates both the magnitude and uncertainty of the estimated differences between proposals. A provisional funding line is defined based on ER and budget. The ER and its credible interval are used to identify proposals with similar quality and credible intervals that overlap with the provisional funding line. These proposals are entered into a lottery. We illustrate the approach for two SNSF grant schemes in career and project funding. We argue that the method could reduce bias in the evaluation process. R code, data and other materials for this article are available online.
{"title":"Rethinking the Funding Line at the Swiss National Science Foundation: Bayesian Ranking and Lottery","authors":"R. Heyard, Manuela Ott, G. Salanti, M. Egger","doi":"10.1080/2330443X.2022.2086190","DOIUrl":"https://doi.org/10.1080/2330443X.2022.2086190","url":null,"abstract":"Abstract Funding agencies rely on peer review and expert panels to select the research deserving funding. Peer review has limitations, including bias against risky proposals or interdisciplinary research. The inter-rater reliability between reviewers and panels is low, particularly for proposals near the funding line. Funding agencies are also increasingly acknowledging the role of chance. The Swiss National Science Foundation (SNSF) introduced a lottery for proposals in the middle group of good but not excellent proposals. In this article, we introduce a Bayesian hierarchical model for the evaluation process. To rank the proposals, we estimate their expected ranks (ER), which incorporates both the magnitude and uncertainty of the estimated differences between proposals. A provisional funding line is defined based on ER and budget. The ER and its credible interval are used to identify proposals with similar quality and credible intervals that overlap with the provisional funding line. These proposals are entered into a lottery. We illustrate the approach for two SNSF grant schemes in career and project funding. We argue that the method could reduce bias in the evaluation process. R code, data and other materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"9 1","pages":"110 - 121"},"PeriodicalIF":1.6,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43492548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1919260
Ruoqi Yu, Dylan S. Small, David J. Harding, J. Aveldanes, P. Rosenbaum
Abstract A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covariates. Another concern is that quantitative measures may be misinterpreted by investigators in the absence of context that is not recorded in quantitative data. When text information is automatically coded to form quantitative measures, examination of the narrative context can reveal the limitations of initial coding efforts. An existing proposal entails a narrative description of a subset of matched pairs, hoping in a subset of pairs to observe quite a bit more of what was not quantitatively measured or automatically encoded. A subset of pairs cannot rule out subtle biases that materially affect analyses of many pairs, but perhaps a subset of pairs can inform discussion of such biases, perhaps leading to a reinterpretation of quantitative data, or perhaps raising new considerations and perspectives. The large literature on qualitative research contends that open-ended, narrative descriptions of a subset of people can be informative. Here, we discuss and apply a form of optimal matching that supports such an integrated, quantitative-plus-qualitative study. The optimal match provides many closely matched pairs plus a subset of exceptionally close pairs suitable for narrative interpretation. We illustrate the matching technique using data from a recent study of police responses to domestic violence in Philadelphia, where the police report includes both quantitative and narrative information.
{"title":"Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research","authors":"Ruoqi Yu, Dylan S. Small, David J. Harding, J. Aveldanes, P. Rosenbaum","doi":"10.1080/2330443X.2021.1919260","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1919260","url":null,"abstract":"Abstract A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covariates. Another concern is that quantitative measures may be misinterpreted by investigators in the absence of context that is not recorded in quantitative data. When text information is automatically coded to form quantitative measures, examination of the narrative context can reveal the limitations of initial coding efforts. An existing proposal entails a narrative description of a subset of matched pairs, hoping in a subset of pairs to observe quite a bit more of what was not quantitatively measured or automatically encoded. A subset of pairs cannot rule out subtle biases that materially affect analyses of many pairs, but perhaps a subset of pairs can inform discussion of such biases, perhaps leading to a reinterpretation of quantitative data, or perhaps raising new considerations and perspectives. The large literature on qualitative research contends that open-ended, narrative descriptions of a subset of people can be informative. Here, we discuss and apply a form of optimal matching that supports such an integrated, quantitative-plus-qualitative study. The optimal match provides many closely matched pairs plus a subset of exceptionally close pairs suitable for narrative interpretation. We illustrate the matching technique using data from a recent study of police responses to domestic violence in Philadelphia, where the police report includes both quantitative and narrative information.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"8 1","pages":"42 - 52"},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1919260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49042689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1906806
Jonathan Auerbach, Steve Pierson
Abstract We estimate the change in the reported number of voter fraud cases when states switch to conducting elections by mail. We consider two types of states in which voting is facilitated by mail: states where a large number of voters receive ballots by mail (receive-by-mail states, RBM) and a subset of these states where registered voters are automatically sent ballots by mail (vote-by-mail states, VBM). We then compare the number of voter fraud cases in RBM (VBM) states to the number of cases in non-RBM (non-VBM) states, using two approaches standard in the social sciences. We find no evidence that voting by mail increases the risk of voter fraud overall. Between 2016 and 2019, RBM (VBM) states reported similar fraud rates to non-RBM (non-VBM) states. Moreover, we estimate Washington would have reported 73 more cases of fraud between 2011 and 2019 had it not introduced its VBM law. While our analysis of the data considers only two of many possible approaches, we argue our findings are unlikely were fraud more common when elections are held by mail.
{"title":"Does Voting by Mail Increase Fraud? Estimating the Change in Reported Voter Fraud When States Switch to Elections By Mail","authors":"Jonathan Auerbach, Steve Pierson","doi":"10.1080/2330443X.2021.1906806","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1906806","url":null,"abstract":"Abstract We estimate the change in the reported number of voter fraud cases when states switch to conducting elections by mail. We consider two types of states in which voting is facilitated by mail: states where a large number of voters receive ballots by mail (receive-by-mail states, RBM) and a subset of these states where registered voters are automatically sent ballots by mail (vote-by-mail states, VBM). We then compare the number of voter fraud cases in RBM (VBM) states to the number of cases in non-RBM (non-VBM) states, using two approaches standard in the social sciences. We find no evidence that voting by mail increases the risk of voter fraud overall. Between 2016 and 2019, RBM (VBM) states reported similar fraud rates to non-RBM (non-VBM) states. Moreover, we estimate Washington would have reported 73 more cases of fraud between 2011 and 2019 had it not introduced its VBM law. While our analysis of the data considers only two of many possible approaches, we argue our findings are unlikely were fraud more common when elections are held by mail.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"8 1","pages":"18 - 41"},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1906806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45385684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1900762
K. Klauenberg, Cord A. Müller, C. Elster
Abstract Millions of measuring instruments are verified each year before being placed on the markets worldwide. In the EU, such initial conformity assessments are regulated by the Measuring Instruments Directive (MID). The MID modules F and F1 on product verification allow for statistical acceptance sampling, whereby only random subsets of instruments need to be inspected. This article re-interprets the acceptance sampling conditions formulated by the MID. The new interpretation is contrasted with the one advanced in WELMEC guide 8.10, and three advantages have become apparent. First, an economic advantage of the new interpretation is a producers’ risk bounded from above, such that measuring instruments with sufficient quality are accepted with a guaranteed probability of no less than 95%. Second, a conceptual advantage is that the new MID interpretation fits into the well known, formal framework of statistical hypothesis testing. Thirdly, the new interpretation applies unambiguously to finite-sized lots, even very small ones. We conclude that the new interpretation is to be preferred and suggest re-formulating the statistical sampling conditions in the MID. Re-interpreting the MID conditions implies that currently available sampling plans are either not admissible or not optimal. We derive a new acceptance sampling scheme and recommend its application. Supplementary materials for this article are available online.
{"title":"Hypothesis-based Acceptance Sampling for Modules F and F1 of the European Measuring Instruments Directive","authors":"K. Klauenberg, Cord A. Müller, C. Elster","doi":"10.1080/2330443X.2021.1900762","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1900762","url":null,"abstract":"Abstract Millions of measuring instruments are verified each year before being placed on the markets worldwide. In the EU, such initial conformity assessments are regulated by the Measuring Instruments Directive (MID). The MID modules F and F1 on product verification allow for statistical acceptance sampling, whereby only random subsets of instruments need to be inspected. This article re-interprets the acceptance sampling conditions formulated by the MID. The new interpretation is contrasted with the one advanced in WELMEC guide 8.10, and three advantages have become apparent. First, an economic advantage of the new interpretation is a producers’ risk bounded from above, such that measuring instruments with sufficient quality are accepted with a guaranteed probability of no less than 95%. Second, a conceptual advantage is that the new MID interpretation fits into the well known, formal framework of statistical hypothesis testing. Thirdly, the new interpretation applies unambiguously to finite-sized lots, even very small ones. We conclude that the new interpretation is to be preferred and suggest re-formulating the statistical sampling conditions in the MID. Re-interpreting the MID conditions implies that currently available sampling plans are either not admissible or not optimal. We derive a new acceptance sampling scheme and recommend its application. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"8 1","pages":"9 - 17"},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1900762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46190994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1971126
A. Gelman
Abstract The recent successes and failures of political polling invite several questions: Why did the polls get it wrong in some high-profile races? Conversely, how is it that polls can perform so well, even given all the evident challenges of conducting and interpreting them?
{"title":"Failure and Success in Political Polling and Election Forecasting","authors":"A. Gelman","doi":"10.1080/2330443X.2021.1971126","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1971126","url":null,"abstract":"Abstract The recent successes and failures of political polling invite several questions: Why did the polls get it wrong in some high-profile races? Conversely, how is it that polls can perform so well, even given all the evident challenges of conducting and interpreting them?","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"8 1","pages":"67 - 72"},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49048962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1932645
J. Fox, Nathan Sanders, Emma E. Fridel, G. Duwe, M. Rocque
ABSTRACT Mass public shootings have generated significant levels of fear in the recent years, with many observers criticizing the media for fostering a moral panic, if not an actual rise in the frequency of such attacks. Scholarly research suggests that the media can potentially impact the prevalence of mass shootings in two respects: (i) some individuals may be inspired to mimic the actions of highly publicized offenders; and (ii) a more general contagion process may manifest as a temporary increase in the likelihood of shootings associated with a triggering event. In this study of mass shootings since 2000, we focus on short-term contagion, rather than imitation that can traverse years. Specifically, after highlighting the sequencing of news coverage prior and subsequent to mass shootings, we apply multivariate point process models to disentangle the correlated incidence of mass public shootings and news coverage of such events. The findings suggest that mass public shootings have a strong effect on the level of news reporting, but that news reporting on the topic has little impact, at least in the relative short-term, on the subsequent prevalence of mass shootings. Finally, the results appear to rule out the presence of strong self-excitation of mass shootings, placing clear limits on generalized short-term contagion effects. Supplementary files for this article are available online.
{"title":"The Contagion of Mass Shootings: The Interdependence of Large-Scale Massacres and Mass Media Coverage","authors":"J. Fox, Nathan Sanders, Emma E. Fridel, G. Duwe, M. Rocque","doi":"10.1080/2330443X.2021.1932645","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1932645","url":null,"abstract":"ABSTRACT Mass public shootings have generated significant levels of fear in the recent years, with many observers criticizing the media for fostering a moral panic, if not an actual rise in the frequency of such attacks. Scholarly research suggests that the media can potentially impact the prevalence of mass shootings in two respects: (i) some individuals may be inspired to mimic the actions of highly publicized offenders; and (ii) a more general contagion process may manifest as a temporary increase in the likelihood of shootings associated with a triggering event. In this study of mass shootings since 2000, we focus on short-term contagion, rather than imitation that can traverse years. Specifically, after highlighting the sequencing of news coverage prior and subsequent to mass shootings, we apply multivariate point process models to disentangle the correlated incidence of mass public shootings and news coverage of such events. The findings suggest that mass public shootings have a strong effect on the level of news reporting, but that news reporting on the topic has little impact, at least in the relative short-term, on the subsequent prevalence of mass shootings. Finally, the results appear to rule out the presence of strong self-excitation of mass shootings, placing clear limits on generalized short-term contagion effects. Supplementary files for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"8 1","pages":"53 - 66"},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1932645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44339845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1978354
James Rosenberger, G. Ridgeway, Lingzhou Xue
Abstract Government reports document more than 14,000 homicides and more than 195,000 aggravated assaults with firearms in 2017. In addition, there were 346 mass shootings, with 4 or more victims, including over 2000 people shot. These statistics do not include suicides (two-thirds of gun deaths) or accidents (5% of gun deaths). This article describes statistical issues discussed at a national forum to stimulate collaboration between statisticians and criminologists. Topics include: (i) available data sources and their shortcomings and efforts to improve the quality, and alternative new data registers of shootings; (ii) gun violence patterns and trends, with statistical models and clustering effects in urban areas; (iii) research for understanding effective strategies for gun violence prevention and the role of the police in solving gun homicides; (iv) the role of reliable forensic science in solving cases involving shootings; and (v) the topic of police shootings, where they are more prevalent and the characteristics of the officers involved. The final section calls the statistical community to engage in collaborations with social scientists to provide the most effective methodological tools for understanding and mitigating the societal problem of gun violence.
{"title":"Statisticians Engage in Gun Violence Research","authors":"James Rosenberger, G. Ridgeway, Lingzhou Xue","doi":"10.1080/2330443X.2021.1978354","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1978354","url":null,"abstract":"Abstract Government reports document more than 14,000 homicides and more than 195,000 aggravated assaults with firearms in 2017. In addition, there were 346 mass shootings, with 4 or more victims, including over 2000 people shot. These statistics do not include suicides (two-thirds of gun deaths) or accidents (5% of gun deaths). This article describes statistical issues discussed at a national forum to stimulate collaboration between statisticians and criminologists. Topics include: (i) available data sources and their shortcomings and efforts to improve the quality, and alternative new data registers of shootings; (ii) gun violence patterns and trends, with statistical models and clustering effects in urban areas; (iii) research for understanding effective strategies for gun violence prevention and the role of the police in solving gun homicides; (iv) the role of reliable forensic science in solving cases involving shootings; and (v) the topic of police shootings, where they are more prevalent and the characteristics of the officers involved. The final section calls the statistical community to engage in collaborations with social scientists to provide the most effective methodological tools for understanding and mitigating the societal problem of gun violence.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"8 1","pages":"73 - 79"},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46355685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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