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Abstract A prior study found that mailing prepaid incentives with $5 cash visible from outside the envelope increased the response rate to a mail survey by 4 percentage points compared to cash that was not externally visible. This “visible cash effect” suggests opportunities to improve survey response at little or no cost, but many unknowns remain. Among them: Does the visible cash effect generalize to different survey modes, respondent burdens, and cash amounts? Does it differ between fresh samples and reinterview samples? Does it affect data quality or survey costs? This article examines these questions using two linked studies where incentive visibility was randomized in a large probability sample for the American National Election Studies. The first study used $10 incentives with invitations to a long web questionnaire (median 71 minutes, n = 17,849). Visible cash increased response rates in a fresh sample for both screener and extended interview response (by 6.7 and 4.8 percentage points, respectively). Visible cash did not increase the response rate in a reinterview sample where the baseline reinterview response rate was very high (72 percent). The second study used $5 incentives with invitations to a mail-back paper questionnaire (n = 8,000). Visible cash increased the response rate in a sample of prior nonrespondents by 4.0 percentage points (from 31.5 to 35.5), but it did not increase the response rate in a reinterview sample where the baseline reinterview rate was very high (84 percent). In the two studies, several aspects of data quality were investigated, including speeding, non-differentiation, item nonresponse, nonserious responses, noncredible responses, sample composition, and predictive validity; no adverse effects of visible cash were detected, and sample composition improved marginally. Effects on survey costs were either negligible or resulted in net savings. Accumulated evidence now shows that visible cash can increase incentives’ effectiveness in several circumstances.
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Data Exclusion in Policy Survey and Questionnaire Data: Aberrant Responses and Missingness
Data preprocessing is an integral step prior to analyzing data in psychological science, with implications for its potentially guiding policy. This article reports how psychological researchers address data preprocessing or quality concerns, with a focus on aberrant responses and missing data in self-report measures. 240 articles were sampled from four journals: Psychological Science, Journal of Personality and Social Psychology, Developmental Psychology, and Abnormal Psychology from 2012 to 2018. Nearly half of the studies did not report any missing data treatment (111/240; 46.25%), and if they did, the most common approach was listwise deletion (71/240; 29.6%). Studies that remove data due to missingness removed, on average, 12% of the sample. Likewise, most studies do not report any aberrant responses (194/240; 80%), but if they did, they classified 4% of the sample as suspect. Most studies are either not transparent enough about their data preprocessing steps or may be leveraging suboptimal procedures. Recommendations can improve transparency and data quality.
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- Award ID(s):
- 1853166
- PAR ID:
- 10401828
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Policy Insights from the Behavioral and Brain Sciences
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2372-7322
- Format(s):
- Medium: X Size: p. 11-17
- Size(s):
- p. 11-17
- Sponsoring Org:
- National Science Foundation
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