Using Markov Chains to Detect Careless Responding in Survey Research Article Swipe
Torsten Biemann
,
Irmela Koch‐Bayram
,
Madleen Meier‐Barthold
,
Herman Aguinis
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/10944281251334778
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/10944281251334778
Careless responses by survey participants threaten data quality and lead to misleading substantive conclusions that result in theory and practice derailments. Prior research developed valuable precautionary and post-hoc approaches to detect certain types of careless responding. However, existing approaches fail to detect certain repeated response patterns, such as diagonal-lining and alternating responses. Moreover, some existing approaches risk falsely flagging careful response patterns. To address these challenges, we developed a methodological advancement based on first-order Markov chains called Lazy Respondents (Laz.R) that relies on predicting careless responses based on prior responses. We analyzed two large datasets and conducted an experimental study to compare careless responding indices to Laz.R and provide evidence that its use improves validity. To facilitate the use of Laz.R, we describe a procedure for establishing sample-specific cutoff values for careless respondents using the “kneedle algorithm” and make an R Shiny application available to produce all calculations. We expect that using Laz.R in combination with other approaches will help mitigate the threat of careless responses and improve the accuracy of substantive conclusions in future research.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/10944281251334778
- OA Status
- hybrid
- Cited By
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- References
- 62
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4411609206Canonical identifier for this work in OpenAlex
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https://doi.org/10.1177/10944281251334778Digital Object Identifier
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Using Markov Chains to Detect Careless Responding in Survey ResearchWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-06-24Full publication date if available
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Torsten Biemann, Irmela Koch‐Bayram, Madleen Meier‐Barthold, Herman AguinisList of authors in order
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https://doi.org/10.1177/10944281251334778Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1177/10944281251334778Direct OA link when available
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Markov chain, Psychology, Survey research, Social psychology, Econometrics, Computer science, Applied psychology, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.types | 32 |
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| abstract_inverted_index.chains | 75 |
| abstract_inverted_index.cutoff | 128 |
| abstract_inverted_index.detect | 30, 41 |
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| abstract_inverted_index.certain | 31, 42 |
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| abstract_inverted_index.falsely | 57 |
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| abstract_inverted_index.indices | 104 |
| abstract_inverted_index.produce | 145 |
| abstract_inverted_index.provide | 108 |
| abstract_inverted_index.quality | 7 |
| abstract_inverted_index.Careless | 0 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.accuracy | 169 |
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| abstract_inverted_index.careless | 34, 84, 102, 131, 164 |
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| abstract_inverted_index.evidence | 109 |
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| abstract_inverted_index.flagging | 58 |
| abstract_inverted_index.improves | 113 |
| abstract_inverted_index.mitigate | 160 |
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| abstract_inverted_index.repeated | 43 |
| abstract_inverted_index.research | 22 |
| abstract_inverted_index.response | 44, 60 |
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| abstract_inverted_index.valuable | 24 |
| abstract_inverted_index.Moreover, | 52 |
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| abstract_inverted_index.patterns. | 61 |
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| abstract_inverted_index.research. | 175 |
| abstract_inverted_index.responses | 1, 85, 165 |
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| abstract_inverted_index.approaches | 28, 38, 55, 157 |
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| abstract_inverted_index.predicting | 83 |
| abstract_inverted_index.responding | 103 |
| abstract_inverted_index.responses. | 51, 89 |
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| abstract_inverted_index.Respondents | 78 |
| abstract_inverted_index.advancement | 70 |
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| abstract_inverted_index.challenges, | 65 |
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| abstract_inverted_index.first-order | 73 |
| abstract_inverted_index.respondents | 132 |
| abstract_inverted_index.responding. | 35 |
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| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5054305892 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I177802217 |
| citation_normalized_percentile.value | 0.95213503 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |