The Experts below are selected from a list of 1662 Experts worldwide ranked by ideXlab platform
Emilia Peytcheva - One of the best experts on this subject based on the ideXlab platform.
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Public Opinion Quarterly 2008, pp. 1–23 THE IMPACT OF NONRESPONSE RATES ON
2016Co-Authors: Nonresponse Bias, Robert M. Groves, A Meta-analysis, Emilia PeytchevaAbstract:Abstract Fifty-nine methodological studies were designed to esti-mate the magnitude of nonresponse bias in statistics of interest. These studies use a variety of designs: sampling frames with rich variables, data from administrative records matched to sample case, use of screening-interview data to describe Nonrespondents to main interviews, followup of Nonrespondents to initial phases of field effort, and measures of be-havior intentions to respond to a survey. This permits exploration of which circumstances produce a relationship between nonresponse rates and nonresponse bias and which, do not. The predictors are design fea-tures of the surveys, characteristics of the sample, and attributes of the survey statistics computed in the surveys
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THE IMPACT OF NONRESPONSE RATES ON NONRESPONSE
2016Co-Authors: A Meta-analysis, Robert M. Groves, Emilia PeytchevaAbstract:Abstract Fifty-nine methodological studies were designed to esti-mate the magnitude of nonresponse bias in statistics of interest. These studies use a variety of designs: sampling frames with rich variables, data from administrative records matched to sample case, use of screening-interview data to describe Nonrespondents to main interviews, followup of Nonrespondents to initial phases of field effort, and measures of be-havior intentions to respond to a survey. This permits exploration of which circumstances produce a relationship between nonresponse rates and nonresponse bias and which, do not. The predictors are design fea-tures of the surveys, characteristics of the sample, and attributes of the survey statistics computed in the surveys
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The Impact of Nonresponse Rates on Nonresponse Bias A Meta-Analysis
Public Opinion Quarterly, 2008Co-Authors: Robert M. Groves, Emilia PeytchevaAbstract:Fifty-nine methodological studies were designed to esti- mate the magnitude of nonresponse bias in statistics of interest. These studies use a variety of designs: sampling frames with rich variables, data from administrative records matched to sample case, use of screening- interview data to describe Nonrespondents to main interviews, followup of Nonrespondents to initial phases of field effort, and measures of be- havior intentions to respond to a survey. This permits exploration of which circumstances produce a relationship between nonresponse rates and nonresponse bias and which, do not. The predictors are design fea- tures of the surveys, characteristics of the sample, and attributes of the survey statistics computed in the surveys.
Lorenzo Richiardi - One of the best experts on this subject based on the ideXlab platform.
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analysis of nonresponse bias in a population based case control study on lung cancer
Journal of Clinical Epidemiology, 2002Co-Authors: Lorenzo Richiardi, Paolo Boffetta, Franco MerlettiAbstract:The objective of this study was to identify characteristics of Nonrespondents and late respondents in a population-based case-control study on lung cancer conducted in the city of Turin (Italy). Information about demographic and socioeconomic variables of 634 cases and 859 controls who responded to an interview, as well as of 154 cases and 154 controls who did not respond were obtained from census and the public register of Turin. The socioeconomic level of Nonrespondents was high in cases but low in controls. Late respondent controls (i.e., individuals who responded after contact through their general practitioner) had socioeconomic characteristics comparable with those of Nonrespondents, while they were similar to respondents with respect to demographic variables. The interview of late respondents halved, from 14 to 7%, the magnitude of the bias introduced by nonresponse on the estimate of the association between educational level and lung cancer. Nonresponse, associated with socioeconomic status, is an important potential source of bias in population-based case-control studies, which should always be considered and discussed. The direction and magnitude of the bias can be quantified. General practitioners may contribute to decrease nonresponse bias. Caution should be used in inferring characteristics of Nonrespondents on the basis of those of late respondents.
Michael Davern - One of the best experts on this subject based on the ideXlab platform.
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deployment of a mixed mode data collection strategy does not reduce nonresponse bias in a general population health survey
Health Services Research, 2012Co-Authors: Timothy J Beebe, Donna D Mcalpine, Jeanette Y Ziegenfuss, M Sarah S Jenkins, B Lindsey A Haas, Michael DavernAbstract:Survey participation is declining (Hox and de Leeuw 1994; Hartge 1999; Steeh et al. 2001; de Leeuw and de Heer 2002; Tickle et al. 2003; Curtin, Presser, and Singer 2005; Morton, Cahill, and Hartge 2006; Berk, Schur, and Feldman 2007); this trend is of great concern because response rate is the most widely used measure of survey quality (Atrostic et al. 2001) and nonresponse bias can be a serious threat to the validity of survey estimates (Sackett 1979; Barton et al. 1980). In an effort to increase response rates, and potentially reduce nonresponse bias, household surveys are increasingly turning to mixed-mode designs whereby instruments are designed to be administered in more than one mode, including mail, web, telephone, and/or in-person, and respondents are allowed to respond to the mode of their choice (De Leeuw 2005; Dillman, Smyth, and Christin 2009b). The attraction of mixed-mode designs is that the characteristics of Nonrespondents may vary by the mode of data collection (Groves 2006) and a second mode will bring in potentially different types of respondents. For this reason (among others), the data collection protocols for three major surveys, the Consumer Assessment of Healthcare Providers and Systems, the Experience of Care and Health Outcomes studies, and the American Community Survey (ACS), call for an initial contact by mail with telephone follow-up to encourage initial Nonrespondents to mail in their completed questionnaires or to complete a telephone interview. Available evidence supports the notion that some respondents exhibit mode preference (Siemiatycki 1979; Brambilla and McKinlay 1987; Link and Mokdad 2005) and that a sequential strategy of implementing multiple contacts allows prospective respondents to respond to a particular mode will improve response rates. For example, in work evaluating the effect of pairing a mixed mail and telephone methodology with a prepaid cash incentive on response rates in a survey of Medicaid enrollees response rates increased considerably after telephone follow-ups, from 54 to 69 percent in the incentive condition, and from 45 to 64 percent in the nonincentive condition (Beebe et al. 2005). Similarly, Gallagher, Fowler, and Stringfellow (2000) found that approximately 34 percent of a sample of Medicaid enrollees responded to a mailed survey and another 10–13 percent responded by telephone. Finally, the ACS, a large national demographic survey conducted by the U.S. Census Bureau, achieves a response rate of 56.2 percent to an initial mailed survey, an increase to 63.5 percent after telephone follow-up, and a final response rate of 95.4 percent after face-to-face interviews (Griffin and Obenski 2002). Although these studies demonstrate the ability of mixed-mode surveys to increase response rates, they do not clarify their effect on response bias because little information on Nonrespondents is available. Some research suggests that switching modes does bring in a different population from those that respond to the initial mode. For example, Fowler et al. (2002) found that telephone interviews with mail Nonrespondents produced a less biased final sample in terms of gender and age in a sample of 800 health plan enrollees. In one of the few mixed-mode studies to have more detailed health-related information on the full sample of 1,900 adult patients enrolled in a randomized controlled trial to promote smoking cessation, a telephone followed by mail design improved representativeness in a number of health-related areas, such as seeking treatment, cardio-pulmonary comorbidities, and substance abuse (Baines et al. 2007). However, these studies had limited information on respondents and Nonrespondents (Fowler et al. 2002); used an atypical sequential strategy (e.g., telephone followed by mail versus mail followed by telephone (Baines et al. 2007); and focused on specialized patient populations (Fowler et al. 2002; Baines et al. 2007) that render the generalizability of their results unclear. In a general population survey utilizing a mixed-mode, mail followed by telephone data collection approach, this article reports a systematic analysis of survey nonresponse bias using extensive sociodemographic and health-related information on both respondents and Nonrespondents to a general population survey. Our primary focus is to assess whether nonresponse bias was reduced by the utilization of a mixed-mode, mail and telephone data collection design.
Steven J Katz - One of the best experts on this subject based on the ideXlab platform.
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latinas and breast cancer outcomes population based sampling ethnic identity and acculturation assessment
Cancer Epidemiology Biomarkers & Prevention, 2009Co-Authors: Ann S Hamilton, Timothy P Hofer, Sarah T Hawley, Donna Morrell, Meryl Leventhal, Dennis Deapen, Barbara Salem, Steven J KatzAbstract:Purpose: Latinas and African-Americans with breast cancer, especially those of lower socioeconomic status and acculturation, have been underrepresented in studies assessing treatment satisfaction, decision-making, and quality of life. A study was designed to recruit a large and representative sample of these subgroups. Materials and Methods: Incident cases were selected by rapid case ascertainment (RCA) in the Los Angeles Surveillance, Epidemiology, and End Results Registry from 2005 to 2006, with oversampling of Latinas and African-Americans. Patients were mailed a questionnaire and $10 incentive 5 to 6 months after diagnosis; Nonrespondents were contacted by telephone. Multivariate analysis was used to assess possible response bias. The RCA definition of Hispanic origin was validated by self-reports. The Short Acculturation Scale for Hispanics index for Latina respondents was used. Results: One thousand six hundred and ninety-eight eligible breast cancer cases were selected and 1,223 participated, for a response rate of 72.0%, which varied little by race/ethnicity. Age, race/ethnicity, and clinical factors were not associated with response; however, respondents were slightly more likely to be married and from higher socioeconomic status census tracts than Nonrespondents. The RCA definition of Hispanic identity was highly sensitive (94.6%) and specific (90.0%). Lower acculturation was associated with lower education and literacy among Latinas. Discussion: High response rates among all subgroups were achieved due to the use of RCA, an incentive, extensive telephone follow-up, a native Spanish-speaking interviewer, and a focused questionnaire. The low acculturation index category identified a highly vulnerable subgroup. This large sample representing subgroups with greater problems will provide a basis for developing better interventions to assist these women. (Cancer Epidemiol Biomarkers Prev 2009;18(7):2022–9)
Michael Peress - One of the best experts on this subject based on the ideXlab platform.
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correcting for survey nonresponse using variable response propensity
Journal of the American Statistical Association, 2010Co-Authors: Michael PeressAbstract:All surveys with less than full response potentially suffer from nonresponse bias. Poststratification weights can only correct for selection into the sample based on observables whose distribution is known in the population. Variables such as gender, race, income, and region satisfy this requirement because they are available from the U.S. Census Bureau, but poststratification based on these variables may not eliminate nonresponse bias. I develop an approach for correcting for nonignorable nonresponse bias. Survey respondents can be classified by their “response propensity.” Proxies for response propensity include the number of attempted phone calls, indicators of temporary refusal, and interviewer-coded measures of cooperativeness. We can then learn about the population of Nonrespondents by extrapolating from the low-propensity respondents. I apply this new estimator to correct for unit nonresponse bias in the American National Election Study and in a CBS / New York Times preelection poll. I find that no...
