Paradata for Nonresponse Error Investigation

Kreuter, Frauke; Olson, Kristen

doi:10.1002/9781118596869.ch2

Cited by 19 publications

(10 citation statements)

References 56 publications

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“…With increased availability to survey paradata, it is useful to calculate level-of-effort (LOE) metrics as a proxy way to classify hard-to-survey groups (Kreuter and Olson 2013;Lee et al 2018). The continuum of resistance theory uses such paradata to study respondents from the tail end of data collection postulating they are similar to nonrespondents in that without the extra time, resources, and effort, they would be nonrespondents (Lin and Schaeffer 1995;Meiklejohn et al 2012).…”

Section: Methodsmentioning

confidence: 99%

Are Sexual Minorities Hard-to-Survey? Insights from the 2020 Census Barriers, Attitudes, and Motivators Study (CBAMS) Survey

Bates

Trejo

Vines

2019

Journal of Official Statistics

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As a stigmatized and vulnerable population, sexual minorities are often assumed to also be a hard-to-survey population. Despite this implicit assumption, there is little empirical evidence on the topic. Using a nationally representative survey that included sexual orientation (the Census Barriers, Attitudes, and Motivators Survey), we examine level of effort, the Census Bureau’s Low Response Score (LRS), and stated intent to respond to the 2020 Census as proxy measures to explore this assumption. We found no evidence that sexual minorities required higher levels of effort to secure participation in the survey. Additionally, we found that compared to straight respondents, lesbians, gays, and bisexuals had a higher intent to respond to the 2020 Census. We surmise the current social climate in the United States may be a contributing factor to these findings.

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Section: Methodsmentioning

confidence: 99%

Are Sexual Minorities Hard-to-Survey? Insights from the 2020 Census Barriers, Attitudes, and Motivators Study (CBAMS) Survey

Bates

Trejo

Vines

2019

Journal of Official Statistics

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“…Paradata are a by‐product of the regular data collection and refer to the data collection process itself (cf., e.g. Durrant & Kreuter, ; Kreuter & Olson, ). Examples are interviewer observations about the housing situation of (unit‐)non‐respondents to account for non‐response bias or interviewer assessments of the respondents' embarrassment with regard to certain questions or response times (cf., e.g.…”

Section: Exploiting Partial Knowledge About the Coarseningmentioning

confidence: 99%

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

Plass

Cattaneo

Augustin

et al. 2019

Int Statistical Rev

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Summary In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.

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“…We add to these efforts information useful for investigating a variety of errors throughout the survey process. For example, as Kreuter and Olson (2013) summarize, a wealth of studies have examined call records, contact history, and time to completion in studying nonresponse in other modes. However, only a handful of studies (e.g., Crawford, Couper, & Lamias, 2001; Sauermann & Roach, 2013) have investigated similar information for web surveys or utilized prior survey paradata to understand nonresponse in a panel setting (McLauchlan & Schonlau, 2016; Roßmann & Gummer, 2016).…”

Section: Framework For Categorizing Paradatamentioning

confidence: 99%

A Typology of Web Survey Paradata for Assessing Total Survey Error

McClain

Couper

Hupp

et al. 2018

Social Science Computer Review

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This article reviews the existing literature on the collection of paradata in web surveys and extends the research in this area beyond the commonly studied measurement error problem to paradata that can be collected for managing and mitigating other important sources of error. To do so, and in keeping with the nature of paradata as process-oriented, we develop a typology of web survey paradata that incorporates information from all steps in the web survey process. We first define web survey paradata and describe general phases of paradata that run parallel to the steps in fielding a typical web survey. Within each phase, we enumerate several errors within the total survey error paradigm that can be examined with paradata, discussing case studies and motivating examples that illustrate innovative uses of paradata across the web survey process. We conclude with a discussion of open questions and opportunities for further work in this area. Overall, we develop this typology keeping technological advancements at the center of our discussion, but with flexibility to continuously incorporate new developments and trends in both technology and study design. Our typology encourages researchers to think about paradata as tools that can be used to investigate a broader range of outcomes than previously studied.

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Paradata for Nonresponse Error Investigation

Cited by 19 publications

References 56 publications

Are Sexual Minorities Hard-to-Survey? Insights from the 2020 Census Barriers, Attitudes, and Motivators Study (CBAMS) Survey

Are Sexual Minorities Hard-to-Survey? Insights from the 2020 Census Barriers, Attitudes, and Motivators Study (CBAMS) Survey

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

A Typology of Web Survey Paradata for Assessing Total Survey Error

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