Aspects of Nonresponse Bias in RDD Telephone Surveys

Montaquila, Jill M.; Brick, J. Michael; Hagedorn, Mary; Kennedy, Courtney; Keeter, Scott

doi:10.1002/9780470173404.ch25

Cited by 19 publications

(20 citation statements)

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“…Curtin, Presser, and Singer (2000) found that excluding late responders from the analysis did not lead to many differences in estimates of the Index of Consumer Sentiment. Montaquila et al (2008) found few differences when no refusal conversions were attempted and fewer callbacks were made. Groves and Peytcheva (2008) performed a meta-analysis of 59 surveys, and determined that there is a very weak relationship between the nonresponse rate and nonresponse bias.…”

Section: Nonresponse Biasmentioning

confidence: 93%

“…We can infer the variables of interest for the nonrespondents by extrapolating from the low-propensity respondents. The "classes" model (O'Neil 1979;Stinchcombe, Jones, and Sheatsley 1981;Smith 1984;Lin and Schaeffer 1995;Curtin, Presser, and Singer 2000;Montaquila et al 2008) instead posits that there are groups of respondents who resemble the nonrespondents. For example, refusers may resemble temporary refusers and unlocated individuals may resemble hardto-locate individuals.…”

Section: Models Of Survey Nonresponsementioning

confidence: 99%

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Correcting for Survey Nonresponse Using Variable Response Propensity

Peress

2010

Journal of the American Statistical Association

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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 nonresponse bias can be a serious problem, particularly for items that relate to political participation. I find that my method is successful in substantially reducing nonresponse bias.

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Section: Nonresponse Biasmentioning

confidence: 93%

Section: Models Of Survey Nonresponsementioning

confidence: 99%

Correcting for Survey Nonresponse Using Variable Response Propensity

Peress

2010

Journal of the American Statistical Association

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“…1,2 Recent survey research literature, however, suggests that response rates are a poor measure of not only nonresponse bias but also data quality. [3][4][5][6][7] The decline in survey response rates over the past several decades has led to a number of rigorous studies and innovative methods to explore the relationship between survey response rates and bias. A meta-analysis that examined response rates and nonresponse bias in 59 surveys found no clear association between nonresponse rates and nonresponse bias.…”

Section: Exploring Nonresponse Bias In a Health Survey Using Neighbormentioning

confidence: 99%

“…Call screening devices, phone usage, and at-home patterns affect accessibility, and calling strategy (e.g., number of call attempts and timing of calls) directly influences contact rates. 7,12 Refusal occurs only after contact is made. The decision to participate or not is an indicator of the respondent's amenability to the survey and is also influenced by other factors.…”

Section: Exploring Nonresponse Bias In a Health Survey Using Neighbormentioning

confidence: 99%

“…Noncontact and refusal may affect different types of potential biases, and these biases may offset one another. 7,15 For example, measures on volunteerism may be biased through noncontact because those who spend much time volunteering may be hard to reach in randomdigit-dialed surveys. On the other hand, those who refuse to participate in the same survey may have opinions and behaviors related to volunteerism that differ dramatically from those of persons who are never contacted.…”

Section: Exploring Nonresponse Bias In a Health Survey Using Neighbormentioning

confidence: 99%

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Exploring Nonresponse Bias in a Health Survey Using Neighborhood Characteristics

et al. 2009

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Objectives. We examined potential nonresponse bias in a large-scale, population-based, random-digit-dialed telephone survey in California and its association with the response rate. Methods. We used California Health Interview Survey (CHIS) data and US Census data and linked the two data sets at the census tract level. We compared a broad range of neighborhood characteristics of respondents and nonrespondents to CHIS. We projected individual-level nonresponse bias using the neighborhood characteristics. Results. We found little to no substantial difference in neighborhood characteristics between respondents and nonrespondents. The response propensity of the CHIS sample was similarly distributed across these characteristics. The projected nonresponse bias appeared very small. Conclusions. The response rate in CHIS did not result in significant nonresponse bias and did not substantially affect the level of data representativeness, and it is not valid to focus on response rates alone in determining the quality of survey data.

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Paradata for Nonresponse Error Investigation

Kreuter

Olson

2013

Improving Surveys With Paradata

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Aspects of Nonresponse Bias in RDD Telephone Surveys

Cited by 19 publications

References 0 publications

Correcting for Survey Nonresponse Using Variable Response Propensity

Correcting for Survey Nonresponse Using Variable Response Propensity

Exploring Nonresponse Bias in a Health Survey Using Neighborhood Characteristics

Paradata for Nonresponse Error Investigation

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