The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Identifying careless responses in survey data.
Abstract
When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%-12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data.
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“According to Meade and Craig’s [40] recommendations, questionnaires with the same option (e.g., choice 1) throughout the whole questionnaire were excluded.”
From Effects of attractions and social attributes on peoples' usage intention and media dependence towards chatbot: The mediating role of parasocial interaction and emotional support (2025) · cites this paper as Meade and Craig’s [40]
In the citing paper
“three attention check questions were included to measure the quality of responses”
From Fear of missing out, social media influencers, and the social, psychological and financial wellbeing of young consumers (2025) · cites this paper as [96]
In the citing paper
“Increased error in some population (whether systematic or random) makes measures less capable at capturing individual differences in the trait of interest in that population (see e.g., refs. 20–22; see also ref. 23).”
From Simpson’s gender-equality paradox (2025) · cites this paper as [22]
In the citing paper
“It is difficult to completely account for error (see refs. 21 and 22), but scale reliability may be used as an indication of the prevalence of quality responses.”
From Simpson’s gender-equality paradox (2025) · cites this paper as [22]
In the citing paper
“It also uses more extensive data quality controls, as recommended in ref. 22.”
From Simpson’s gender-equality paradox (2025) · cites this paper as [22]
In the citing paper
“Invalid responses were excluded if there are the same responses for all items and if items receive consistent extreme values (Meade & Craig, 2012). The valid rate was 100% as a result of applying these criteria.”
From University students' Motivated Attention and use of regulation strategies on social media (2015) · cites this paper as Meade & Craig, 2012
In the citing paper
“These responses can compromise the quality and accuracy of the data, weaken the relationships among survey variables, and impact factor loading and structure”
From Measuring undergraduate students’ reliance on generative AI during problem-solving: Scale development and validation. (2025) · cites this paper as Meade & Craig, 2012
In the citing paper
“Additionally, we checked our results for responses where the first option is selected for each question [24].We did not find such cases.”
From Using AI-based coding assistants in practice: State of affairs, perceptions, and ways forward (2025) · cites this paper as [24]
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Metadata
- Authors
- Meade, A. W., & Craig, S. B.
- Affiliations
- North Carolina State University (US)
- Venue
- Psychological Methods
- Published
- 2012-09
- Volume
- 17
- Issue
- 3
- Pages
- 437-455
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2116323796
- Cited by
- 3,865
- References
- 50
- Citations by year
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- FWCI
- 209.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Keywords
- survey data quality, reaction time (psychology), data simulation, online surveys
- MeSH terms
- Data Collection, Humans, Surveys and Questionnaires, Reaction Time, Research Design, Logistic Models, Internet, Self Report
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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