Level-of-Effort Paradata and Nonresponse Adjustment Models for a National Face-to-Face Survey

Wagner, James; Valliant, Richard; Hubbard, F. Patrick; Jiang, Li

doi:10.1093/jssam/smu012

Cited by 12 publications

(6 citation statements)

References 19 publications

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“…Along these lines, recent research has started to investigate the use of paradata to improve sample weights or other statistical methods aiming to reduce nonresponse bias (Kreuter 2013). Still, it is not clear, at least from our work, to what extent bias can be addressed by paradata or if using paradata is even warranted in the first place (Behaghel et al 2015;Heffetz and Reeves 2016;Wagner et al 2014). We propose that our explicit link between personality traits and unit nonresponse provides some theoretical guidance for the potential of paradata to improve the analysis of survey data and corrections for sample bias.…”

Section: Discussionmentioning

confidence: 95%

Personality as a Predictor of Unit Nonresponse in an Internet Panel

Cheng

Zamarro

Orriens

2018

Sociological Methods & Research

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Unit nonresponse in panel data sets is often a source of bias. Why certain individuals attrite from longitudinal studies and how to minimize this phenomenon have been examined by researchers. However, this research has typically focused on data sets collected via telephone, postal mail, or face-to-face interviews. Moreover, this research usually focuses on using demographic characteristics such as educational attainment or income to explain variation in the incidence of unit nonresponse. We make two contributions to the existing literature. First, we examine the incidence of unit nonresponse in an Internet panel, a relatively new, and hence understudied, approach to gathering longitudinal data. Second, we hypothesize that personality traits, which typically remain unobserved and unmeasured in many data sets, affect the likelihood of unit nonresponse. Using data from an Internet panel that includes self-reported measures of personality in its baseline survey, we find that conscientiousness and openness to experience predict the incidence of unit nonresponse in subsequent survey waves, even after controlling for cognitive ability and demographic characteristics that are usually available and used by researchers to correct for unit nonresponse. We also test the potential to use paradata as proxies for personality traits related to unit nonresponse. Although we show that these proxies are correlated with personality traits and predict unit nonresponse in the same way as self-reported measures of personality traits, it is also possible that they capture other idiosyncrasies related to future survey completion. Our results suggest that obtaining explicit measures of personality traits or finding better proxies for them could be valuable for more fully addressing the potential bias that may arise as a result of unit nonresponse.

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Section: Discussionmentioning

confidence: 95%

Personality as a Predictor of Unit Nonresponse in an Internet Panel

Cheng

Zamarro

Orriens

2018

Sociological Methods & Research

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“…Along these lines, recent research has started to investigate the use of paradata to improve sample weights or other statistical methods aiming to reduce panel attrition bias. Still, it is not clear to what extent bias can be addressed by paradata, or if using paradata is even warranted in the first place (Behaghel et al, 2015;Heffetz & Reeves, 2016;Wagner et al, 2014). We propose that our explicit link between personality traits and panel attrition provides some theoretical guidance for the potential of paradata to improve the analysis of survey data and corrections for sample bias.…”

Section: Discussionmentioning

confidence: 98%

Personality as a Predictor of Unit Nonresponse in Panel Data: An Analysis of an Internet-Based Survey

2016

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Unit nonresponse or attrition in panel data sets is often a source of nonrandom measurement error. Why certain individuals attrite from longitudinal studies and how to minimize this phenomenon have been examined by researchers. However, this research has typically focused on data sets collected via telephone, postal mail, or face-to-face interviews. Moreover, this research usually focuses on using demographic characteristics such as educational attainment or income to explain variation in the incidence of unit nonresponse. We make two contributions to the existing literature. First, we examine the incidence of unit nonresponse in an internet panel, a relatively new, and hence understudied, approach to gathering longitudinal data. Second, we hypothesize that personality traits, which typically remain unobserved and unmeasured in many data sets, affect the likelihood of unit nonresponse. Using data from an internet panel that includes self-reported measures of personality in its baseline survey, we find that conscientiousness and openness to experience predict the incidence of unit nonresponse in subsequent survey waves, even after controlling for cognitive ability and demographic characteristics that are usually available and used by researchers to correct for panel attrition. We also test the potential to use paradata as proxies for personality traits. Although we show that these proxies predict panel attrition in the same way as self-reported measures of personality traits, it is unclear to what extent they capture particular personality traits versus other individual circumstances related to future survey completion. Our results suggest that obtaining explicit measures of personality traits or finding better proxies for them are crucial to more fully address the potential bias that may arise as a result of panel attrition.

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“…These paradata are also used to examine the effect of increased fieldwork efforts (e.g., repeated contact attempts, refusal conversion) on reducing nonresponse bias (Lynn & Clarke, 2002;Moore et al, 2018). In contrast, call record data and doorstep interactions proved to be of little use in improving the quality of nonresponse adjustments (Biemer et al, 2013;Maitland et al, 2009;Peytchev & Olson, 2007;Wagner et al, 2014)-either due to low correlations between paradata-derived indicators and key survey variables (Hanly et al, 2016;Kreuter & Kohler, 2009;Peytchev & Olson, 2007;Wagner et al, 2014) or due to underreporting of contact attempts by interviewers (Biemer et al, 2013). Interviewer observations about the housing unit and its members (e.g., type of household, presence of children, receipt of unemployment benefits) also do not have substantial utility in nonresponse adjustment, as they do not predict response outcomes and key survey variables well (Kreuter et al, 2010;West et al, 2014).…”

Section: Purposes Of Interviewer-observed Paradatamentioning

confidence: 99%

Interviewer-Observed Paradata in Mixed-Mode and Innovative Data Collection

Kunz

Daikeler

Ackermann-Piek

2023

International Journal of Market Research

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In this research note, we address the potentials of using interviewer-observed paradata, typically collected during face-to-face-only interviews, in mixed-mode and innovative data collection methods that involve an interviewer at some stage (e.g., during the initial contact or during the interview). To this end, we first provide a systematic overview of the types and purposes of the interviewer-observed paradata most commonly collected in face-to-face interviews—contact form data, interviewer observations, and interviewer evaluations—using the methodology of evidence mapping. Based on selected studies, we illustrate the main purposes of interviewer-observed paradata we identified—including fieldwork management, propensity modeling, nonresponse bias analysis, substantive analysis, and survey data quality assessment. Based on this, we discuss the possible use of interviewer-observed paradata in mixed-mode and innovative data collection methods. We conclude with thoughts on new types of interviewer-observed paradata and the potential of combining paradata from different survey modes.

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Level-of-Effort Paradata and Nonresponse Adjustment Models for a National Face-to-Face Survey

Cited by 12 publications

References 19 publications

Personality as a Predictor of Unit Nonresponse in an Internet Panel

Personality as a Predictor of Unit Nonresponse in an Internet Panel

Personality as a Predictor of Unit Nonresponse in Panel Data: An Analysis of an Internet-Based Survey

Interviewer-Observed Paradata in Mixed-Mode and Innovative Data Collection

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