Public Opinion Quarterly最新文献

Interviewers' postinterview evaluations of respondents' performance (IEPs) are paradata, used to describe the quality of the data obtained from respondents. IEPs are driven by a combination of factors, including respondents' and interviewers' sociodemographic characteristics and what actually transpires during the interview. However, relatively few studies examine how IEPs are associated with features of the response process, including facets of the interviewer-respondent interaction and patterns of responding that index data quality. We examine whether features of the response process-various respondents' behaviors and response quality indicators-are associated with IEPs in a survey with a diverse set of respondents focused on barriers and facilitators to participating in medical research. We also examine whether there are differences in IEPs across respondents' and interviewers' sociodemographic characteristics. Our results show that both respondents' behaviors and response quality indicators predict IEPs, indicating that IEPs reflect what transpires in the interview. In addition, interviewers appear to approach the task of evaluating respondents with differing frameworks, as evidenced by the variation in IEPs attributable to interviewers and associations between IEPs and interviewers' gender. Further, IEPs were associated with respondents' education and ethnoracial identity, net of respondents' behaviors, response quality indicators, and sociodemographic characteristics of respondents and interviewers. Future research should continue to build on studies that examine the correlates of IEPs to better inform whether, when, and how to use IEPs as paradata about the quality of the data obtained.

Among the numerous explanations that have been offered for recent errors in pre-election polls, selection bias due to non-ignorable partisan nonresponse bias, where the probability of responding to a poll is a function of the candidate preference that a poll is attempting to measure (even after conditioning on other relevant covariates used for weighting adjustments), has received relatively less focus in the academic literature. Under this type of selection mechanism, estimates of candidate preferences based on individual or aggregated polls may be subject to significant bias, even after standard weighting adjustments. Until recently, methods for measuring and adjusting for this type of non-ignorable selection bias have been unavailable. Fortunately, recent developments in the methodological literature have provided political researchers with easy-to-use measures of non-ignorable selection bias. In this study, we apply a new measure that has been developed specifically for estimated proportions to this challenging problem. We analyze data from 18 different pre-election polls: 9 different telephone polls conducted in 8 different states prior to the US presidential election in 2020, and nine different pre-election polls conducted either online or via telephone in Great Britain prior to the 2015 general election. We rigorously evaluate the ability of this new measure to detect and adjust for selection bias in estimates of the proportion of likely voters that will vote for a specific candidate, using official outcomes from each election as benchmarks and alternative data sources for estimating key characteristics of the likely voter populations in each context.