{"title":"对Gelman和Azari的回应(2017)","authors":"Corrie V. Hunt","doi":"10.1080/2330443X.2017.1399845","DOIUrl":null,"url":null,"abstract":"As Gelman and Azari make clear, there is no single smoking gun to point to as the primary explanation for the 2016 election that took somany of us by surprise. As a pollster at a progressive public opinion research firm, I will admit the election floored me in the most depressing and sickening of ways. It was not because I did not think it was possible. In fact, in the final weeks leading up to the election, I and many of my colleagues grew increasingly fearful that the tightening we saw in internal polls meant that aClinton victorywas far from certain. But I letmyself be reassured by the confidence of the analytics projections. One of the most important lessons practitioners and consumers of public opinion research can learn from this experience is to take a much closer examination of election prediction models (lesson #3) and how nonresponse bias (lesson #5) affects polls in general and the polls that feed into forecast models. And finally, we cannot let ourselves get so fixated on the horserace numbers that we forget to listen to what voters are actually telling us in the rest of the poll and in qualitative research.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1399845","citationCount":"0","resultStr":"{\"title\":\"Response to Gelman and Azari (2017)\",\"authors\":\"Corrie V. Hunt\",\"doi\":\"10.1080/2330443X.2017.1399845\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As Gelman and Azari make clear, there is no single smoking gun to point to as the primary explanation for the 2016 election that took somany of us by surprise. As a pollster at a progressive public opinion research firm, I will admit the election floored me in the most depressing and sickening of ways. It was not because I did not think it was possible. In fact, in the final weeks leading up to the election, I and many of my colleagues grew increasingly fearful that the tightening we saw in internal polls meant that aClinton victorywas far from certain. But I letmyself be reassured by the confidence of the analytics projections. One of the most important lessons practitioners and consumers of public opinion research can learn from this experience is to take a much closer examination of election prediction models (lesson #3) and how nonresponse bias (lesson #5) affects polls in general and the polls that feed into forecast models. And finally, we cannot let ourselves get so fixated on the horserace numbers that we forget to listen to what voters are actually telling us in the rest of the poll and in qualitative research.\",\"PeriodicalId\":43397,\"journal\":{\"name\":\"Statistics and Public Policy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/2330443X.2017.1399845\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistics and Public Policy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/2330443X.2017.1399845\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SOCIAL SCIENCES, MATHEMATICAL METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics and Public Policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/2330443X.2017.1399845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
As Gelman and Azari make clear, there is no single smoking gun to point to as the primary explanation for the 2016 election that took somany of us by surprise. As a pollster at a progressive public opinion research firm, I will admit the election floored me in the most depressing and sickening of ways. It was not because I did not think it was possible. In fact, in the final weeks leading up to the election, I and many of my colleagues grew increasingly fearful that the tightening we saw in internal polls meant that aClinton victorywas far from certain. But I letmyself be reassured by the confidence of the analytics projections. One of the most important lessons practitioners and consumers of public opinion research can learn from this experience is to take a much closer examination of election prediction models (lesson #3) and how nonresponse bias (lesson #5) affects polls in general and the polls that feed into forecast models. And finally, we cannot let ourselves get so fixated on the horserace numbers that we forget to listen to what voters are actually telling us in the rest of the poll and in qualitative research.