Cited by 151Mobile Crowdsensing and CrowdsourcingSurvey Methodology and NonresponsePsychometric Methodologies and Testing

The DOI matches its Crossref record; 18 of the 20 references it cites were found in a registry or an index.

Inattentive Responding in MTurk and Other Online Samples

Fleischer, Avi, Mead, Alan D., Huang, Jialin·2015·Industrial and Organizational Psychology

DOI 10.1017/iop.2015.25

Open this paper in CoChat — free account

Abstract

The focal article by Landers and Behrend (2015) makes the case that samples collected on microtask websites like Amazon's Mechanical Turk (MTurk) are inherently no better or worse than traditional samples of convenience from university students or organizations. We wholeheartedly agree. However, having successfully used MTurk and other online sources for data collection, we feel that the focal article was insufficient regarding the caution required in identifying inattentive respondents and the problems that can arise if such individuals are not removed from the dataset. Although we focus on MTurk, similar issues arise for most “low-stakes” assessments, including student samples, which seem to be increasingly collected online.

Integrity signals

Registry

Crossref · 10.1017/iop.2015.25

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Attention and Effort
Egeth, H. E., & Kahneman, D. J. (1975) · The American Journal of Psychology · doi:10.2307/1421603 · Cited by 6,976
Identifying careless responses in survey data.
Meade, A. W., & Craig, S. B. (2012) · Psychological Methods · doi:10.1037/a0028085 · Cited by 3,865
Researching Internet-Based Populations: Advantages and Disadvantages of Online Survey Research, Online Questionnaire Authoring Software Packages, and Web Survey Services
Wright, K. B. (2006) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2005.tb00259.x · Cited by 2,550
Introduction to Measurement Theory
Allen, M. J. (1979) · Cited by 2,124
Detecting and Deterring Insufficient Effort Responding to Surveys
Huang, J. L. W., Curran, P. G., Keeney, J., Poposki, E. M., & DeShon, R. P. (2011) · Journal of Business and Psychology · doi:10.1007/s10869-011-9231-8 · Cited by 1,315
An inconvenient truth: Arbitrary distinctions between organizational, mechanical Turk, and other convenience samples
Landers, R. N., & Behrend, T. S. (2015) · Industrial and Organizational Psychology · doi:10.1017/iop.2015.13 · Cited by 714
Factors Defined by Negatively Keyed Items: The Result of Careless Respondents?
Schmitt, N., & Stuits, D. M. (1985) · Applied Psychological Measurement · doi:10.1177/014662168500900405 · Cited by 536
Careless Responding to Reverse-Worded Items: Implications for Confirmatory Factor Analysis
Woods, C. M. (2006) · Journal of Psychopathology and Behavioral Assessment · doi:10.1007/s10862-005-9004-7 · Cited by 402
Evidence for response bias as a source of error variance in applied assessment.
McGrath, R. E., Mitchell, M., Kim, B. H., & Hough, L. (2010) · Psychological Bulletin · doi:10.1037/a0019216 · Cited by 282
MMPI-2 random responding indices: Validation using a self-report methodology.
Berry, D. T. R., Wetter, M. W., Baer, R. A., Larsen, L. H., Clark, C. D., & Monroe, K. (1992) · Psychological Assessment · doi:10.1037/1040-3590.4.3.340 · Cited by 177

Show all 20 references

Research works printed but not linked: 2

Huang, 2015, Does the negatively-worded item effect exist in personality measures? A meta-analysis
Fleischer, 2015, Detecting inattentive pilot test examinees in an MTurk sample

Sorted by citations

A Tale of Two Sample Sources: Do Results from Online Panel Data and Conventional Data Converge?
Walter, S. L., Seibert, S. E., Goering, D. D., & O’Boyle, E. H. (2018) · Journal of Business and Psychology · doi:10.1007/s10869-018-9552-y · Cited by 593
Amazon mechanical Turk in organizational psychology: an evaluation and practical recommendations
Cheung, J. H., Burns, D. K., Sinclair, R. R., & Sliter, M. (2017) · Journal of Business and Psychology · doi:10.1007/s10869-016-9458-5 · Cited by 474
Systems perspective of Amazon mechanical Turk for organizational research: review and recommendations
Keith, M. G., Tay, L., & Harms, P. D. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01359 · Cited by 319
Gamification and the impact of extrinsic motivation on needs satisfaction: Making work fun?
Mitchell, R., Schuster, L., & Jin, H. S. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.11.022 · Cited by 367
The quality of data collected online: An investigation of careless responding in a crowdsourced sample
Brühlmann, F., Petralito, S., Aeschbach, L. F., & Opwis, K. (2020) · Methods in Psychology · doi:10.1016/j.metip.2020.100022 · Cited by 168
Innovations in Sampling: Improving the Appropriateness and Quality of Samples in Organizational Research
Zickar, M. J., & Keith, M. G. (2022) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-120920-052946 · Cited by 231
Virtual reality adoption during the COVID-19 pandemic: A uses and gratifications perspective
Ball, C., Huang, K.-T., & Francis, J. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101728 · Cited by 152
“Previous research also demonstrates that MTurk data is as reliable as other common convenience samples (i.e., college student samples) (Fleischer et al., 2015).”
The MTurkification of Social and Personality Psychology
Anderson, C. A., Allen, J. J., Plante, C. N., Quigley‐McBride, A., Lovett, A., & Rokkum, J. N. (2018) · Personality and Social Psychology Bulletin · doi:10.1177/0146167218798821 · Cited by 138
Intolerance of uncertainty as a factor linking obsessive-compulsive symptoms, health anxiety and concerns about the spread of the novel coronavirus (COVID-19) in the United States
Wheaton, M. G., Messner, G. R., & Marks, J. B. (2021) · Journal of Obsessive-Compulsive and Related Disorders · doi:10.1016/j.jocrd.2020.100605 · Cited by 111
Amazon's Mechanical Turk: A Comment
Ford, J. B. (2017) · Journal of Advertising · doi:10.1080/00913367.2016.1277380 · Cited by 112
Reports of Recovered Memories of Abuse in Therapy in a Large Age-Representative U.S. National Sample: Therapy Type and Decade Comparisons
Patihis, L., & Pendergrast, M. H. (2018) · Clinical Psychological Science · doi:10.1177/2167702618773315 · Cited by 109
Congruence and Incongruence in Thematic Advertisement–Medium Combinations: Role of Awareness, Fluency, and Persuasion Knowledge
Germelmann, C. C., Herrmann, J.-L., Kacha, M., & Darke, P. R. (2020) · Journal of Advertising · doi:10.1080/00913367.2020.1745110 · Cited by 92
Drivers of Data Quality in Advertising Research: Differences across MTurk and Professional Panel Samples
Berry, C., Kees, J., & Burton, S. (2022) · Journal of Advertising · doi:10.1080/00913367.2022.2079026 · Cited by 90
Using Mechanical Turk Data in IS Research: Risks, Rewards, and Recommendations
Jia, R., Steelman, Z., & Reich, B. H. (2017) · Communications of the Association for Information Systems · doi:10.17705/1cais.04114 · Cited by 94
To grit or not to grit, that is the question!
Vazsonyi, A. T., Ksinan, A. J., Jiskrova, G. K., Mikuška, J., Javakhishvili, M., & Cui, G. (2018) · Journal of Research in Personality · doi:10.1016/j.jrp.2018.12.006 · Cited by 87
Objectification, Masculinity, and Muscularity: A Test of Objectification Theory with Heterosexual Men
Davids, C. M., Watson, L. B., & Gere, M. P. (2018) · Sex Roles · doi:10.1007/s11199-018-0940-6 · Cited by 85
Consumers’ Social Media Brand Behaviors: Uncovering Underlying Motivators and Deriving Meaningful Consumer Segments
Dimitriu, R., & Guesalaga, R. (2017) · Psychology and Marketing · doi:10.1002/mar.21007 · Cited by 79
When Patients Overreport Symptoms: More Than Just Malingering
Merckelbach, H. L. G. J., Dandachi‐FitzGerald, B., van Helvoort, D. H. J., Jelícic, M., & Otgaar, H. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419837681 · Cited by 79
Fear and anxiety in the face of COVID-19: Negative dispositions towards risk and uncertainty as vulnerability factors
Millroth, P., & Frey, R. (2021) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2021.102454 · Cited by 88
A panel for lemons? Positivity bias, reputation systems and data quality on MTurk
Matherly, T. (2019) · European Journal of Marketing · doi:10.1108/ejm-07-2017-0491 · Cited by 79
Lie prevalence, lie characteristics and strategies of self-reported good liars
Verigin, B. L., Meijer, E. H., Bogaard, G., & Vrij, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0225566 · Cited by 88
Beating the blues by viewing Green: Depressive symptoms predict greater restoration from stress and negative affect after viewing a nature video
Meuwese, D., Dijkstra, K., Maas, J., & Koole, S. L. (2021) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2021.101594 · Cited by 61
Identity Asymmetries: An Experimental Investigation of Social Identity and Information Exchange in Multiteam Systems
Mell, J. N., DeChurch, L. A., Leenders, R., & Contractor, N. (2019) · Academy of Management Journal · doi:10.5465/amj.2018.0325 · Cited by 60
Swiping away the moments that make up a dull day: Narcissism, boredom, and compulsive smartphone use
Ksinan, A. J., Mališ, J., & Vazsonyi, A. T. (2019) · Current Psychology · doi:10.1007/s12144-019-00228-7 · Cited by 54
Coping strategies initiated by COVID-19-related stress, individuals' motives for social media use, and perceived stress reduction
Bae, M. (2022) · Internet Research · doi:10.1108/intr-05-2021-0269 · Cited by 52
Hardiness moderates the effects of COVID-19 stress on anxiety and depression
Bartone, P. T., McDonald, K., Hansma, B. J., & Solomon, J. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.08.045 · Cited by 48
A Primer for Conducting Survey Research using MTurk
Chambers, S., Nimon, K., & Anthony-McMann, P. E. (2016) · International Journal of Adult Vocational Education and Technology · doi:10.4018/ijavet.2016040105 · Cited by 41
Don’t Get Lost in the Crowd: Best Practices for Using Amazon’s Mechanical Turk in Behavioral Research
Smith, B. R. (2019) · Journal of the Midwest Association for Information Systems · doi:10.17705/3jmwa.000050 · Cited by 42
Person–Environment Fit and Work Satisfaction: Exploring the Conditional Effects of Age
Rauvola, R. S., Rudolph, C. W., Ebbert, L. K., & Zacher, H. (2019) · Work Aging and Retirement · doi:10.1093/workar/waz011 · Cited by 39
Towards Understanding and Detecting Cyberbullying in Real-world Images
Vishwamitra, N., Hu, H., Luo, F., & Cheng, L. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24260 · Cited by 39
Using pre-test explanations to improve test-taker reactions: Testing a set of “wise” interventions
McCarthy, J. M., Bauer, T. N., Truxillo, D. M., Campion, M. C., Iddekinge, C. H. V., & Campion, M. A. (2017) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2017.04.002 · Cited by 41
Happiness at Your Fingertips: Assessing Mental Health with Smartphone Photoplethysmogram-Based Heart Rate Variability Analysis
Liu, I., Ni, S., & Peng, K. (2020) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2019.0283 · Cited by 42
Moving Toward a New Model of Sexual Consent: The Development of the Process-Based Consent Scale
Glace, A. M., Zatkin, J. G., & Kaufman, K. L. (2020) · Violence Against Women · doi:10.1177/1077801220952159 · Cited by 35
Development of a Measure of Informal Workplace Social Interactions
Winslow, C. J., Sabat, I. E., Anderson, A. J., Kaplan, S. A., & Miller, S. J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02043 · Cited by 34
Do blended working arrangements enhance organizational attractiveness and organizational citizenship behaviour intentions? An individual difference perspective
Wörtler, B., Yperen, N. W. V., & Barelds, D. P. H. (2020) · European Journal of Work and Organizational Psychology · doi:10.1080/1359432x.2020.1844663 · Cited by 37
Tailoring service recovery messages to consumers’ affective states
Cummings, K. H., & Yule, J. A. (2020) · European Journal of Marketing · doi:10.1108/ejm-02-2019-0122 · Cited by 30
Investigating the Accessibility of Crowdwork Tasks on Mechanical Turk
Uzor, S., Jacques, J. T., Dudley, J. J., & Kristensson, P. O. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445291 · Cited by 28
Work hard, play hard…or maybe not: A look at the relationships between workaholism, work-leisure conflict, and work stress
Meier, E., Aziz, S., Wuensch, K. L., & Dolbier, C. L. (2020) · Journal of Leisure Research · doi:10.1080/00222216.2020.1778589 · Cited by 35
Insufficient Effort Responding as a Potential Confound between Survey Measures and Objective Tests
Huang, J. L. W., & DeSimone, J. A. (2020) · Journal of Business and Psychology · doi:10.1007/s10869-020-09707-2 · Cited by 27
Kaufman Domains of Creativity Scale: Relationship to Occupation and Measurement Invariance across Gender
Miroshnik, K. G., Shcherbakova, O., & Kaufman, J. C. (2021) · Creativity Research Journal · doi:10.1080/10400419.2021.1953823 · Cited by 26
Protecting their turf: When and why supervisors undermine employee boundary spanning.
Mell, J. N., Quintane, E., Hirst, G., & Carnegie, A. B. (2021) · Journal of Applied Psychology · doi:10.1037/apl0000960 · Cited by 26
Application of the heuristic-systematic model to computer code trustworthiness: The influence of reputation and transparency
Alarcon, G. M., Gamble, R., Jessup, S. A., Walter, C., Ryan, T. J., Wood, D. W., & Calhoun, C. (2017) · Cogent Psychology · doi:10.1080/23311908.2017.1389640 · Cited by 24
Alt-White? A Gendered Look at “Victim” Ideology and the Alt-Right
Boehme, H. M., & Isom, D. A. (2019) · Victims & Offenders · doi:10.1080/15564886.2019.1679308 · Cited by 24
Do truth-telling oaths improve honesty in crowd-working?
Jacquemet, N., James, A. G., Luchini, S., Murphy, J. J., & Shogren, J. F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244958 · Cited by 24
Using surveytainment to counter declining survey data quality
Kostyk, A., Zhou, W., & Hyman, M. R. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.10.024 · Cited by 25
Development and Validation of the Ableist Microaggression Impact Questionnaire
Aydemir-Döke, D., & Herbert, J. T. (2021) · Rehabilitation Counseling Bulletin · doi:10.1177/00343552211014259 · Cited by 21
Can we trust online crowdworkers?
Jonell, P., Kucherenko, T., Torre, I., & Beskow, J. (2020) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3383652.3423860 · Cited by 25
Factor Structure and Psychometric Properties of the Digital Communication Empathy Scale (DCES)
Collins, A. M., Warburton, W., Bussey, K., & Sweller, N. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103183 · Cited by 21
Talking Past Each Other: Construal Level, Utilitarian Motives, and Entrepreneurial Team Formation
Gray, S. M., Howell, T., & Sackett, E. (2024) · Organization Science · doi:10.1287/orsc.2022.16693 · Cited by 19
Anxiety Sensitivity and Intolerance of Uncertainty as Factors Related to Adult Separation Anxiety Disorder Symptoms
Wheaton, M. G., & Kaiser, N. (2021) · International Journal of Cognitive Therapy · doi:10.1007/s41811-021-00114-w · Cited by 18
Workplace Anger Costs Women Irrespective of Race
Marshburn, C. K., Cochran, K. J., Flynn, E., & Levine, L. J. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.579884 · Cited by 19
A Psychometric Evaluation of the Intrasexual Competition Scale
Albert, G. O., Richardson, G. B., Arnocky, S. A., Bird, B. M., Fisher, M. L., Hlay, J. K., McHale, T. S., & Hodges‐Simeon, C. R. (2022) · Archives of Sexual Behavior · doi:10.1007/s10508-021-02167-6 · Cited by 18
Preventing satisficing: A narrative review
Blazek, D. R., & Siegel, J. T. (2023) · International Journal of Social Research Methodology · doi:10.1080/13645579.2023.2239086 · Cited by 16
Enhancing Self-Administered Questionnaire Response Quality Using Code of Conduct Reminders
Saunders, C., & Kulchitsky, J. D. (2021) · International Journal of Market Research · doi:10.1177/14707853211055060 · Cited by 21
Using a consistency check during data collection to identify invalid responding in an online cannabis screening survey
Schell, C., Godinho, A., & Cunningham, J. A. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01556-2 · Cited by 15
The Perceived Impacts of COVID-19 on Users’ Acceptance of Virtual Reality Hardware: A Digital Divide Perspective
Huang, K.-T., Ball, C., & Francis, J. (2023) · American Behavioral Scientist · doi:10.1177/00027642231156775 · Cited by 19
Dual effects of self-focused and other-focused power on sexual harassment intentions
Stockdale, M. S., Gilmer, D. O., & Dinh, T. (2019) · Equality Diversity and Inclusion An International Journal · doi:10.1108/edi-09-2018-0160 · Cited by 15
On Text-based Personality Computing: Challenges and Future Directions
Fang, Q., Giachanou, A., Bagheri, A., Boeschoten, L., van Kesteren, E., Kamalabad, M. S., & Oberski, D. L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.691 · Cited by 14
Private organization’s moral behaviour and citizen support for public-private partnerships: evidence from a survey experiment
Kalesnikaite, V., & Baker, K. P. (2024) · Public Management Review · doi:10.1080/14719037.2024.2303030 · Cited by 14
The Self-Report Symptom Inventory as an Instrument for Detecting Symptom Over-Reporting
Bošković, I., Merckelbach, H. L. G. J., Merten, T., Hope, L., & Jelícic, M. (2019) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000547 · Cited by 14
Playing alone: can game design elements satisfy user needs in gamified mHealth services?
Mitchell, R., Schuster, L., & Jin, H. S. (2021) · Health Promotion International · doi:10.1093/heapro/daab168 · Cited by 14
PACT : Developing and Evaluating a Digital Sexual Consent Program for Youth
Javidi, H., Widman, L. M., Maheux, A. J., McCrimmon, J., Evans-Paulson, R., & Becker, W. (2023) · The Journal of Sex Research · doi:10.1080/00224499.2023.2208560 · Cited by 14
Relations among psychopathy, moral competence, and moral intuitions in student and community samples
Gay, J. G., Vitacco, M. J., Hackney, A. A., Beussink, C. N., & Lilienfeld, S. O. (2018) · Legal and Criminological Psychology · doi:10.1111/lcrp.12128 · Cited by 14
The structured interview's resistance to gender discrimination under cognitive load
Kith, S., Cassidy, S. A., & Powell, D. M. (2022) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12361 · Cited by 17
When altruistic decisions shape egoistic motives: motivation shift in sequential charitable support
Fanggidae, J. P., Jin, H. S., Kim, H. J., & Kelly, L. (2022) · International Journal of Advertising · doi:10.1080/02650487.2022.2140962 · Cited by 13
Shoppers’ susceptibility to information overload: scale development and validation
Hunter, G. L., Taylor, S. A., & Kallen, P. H. (2022) · The Journal of Marketing Theory and Practice · doi:10.1080/10696679.2022.2121287 · Cited by 16
Using a foreign language does not make you think more: Null effects of using a foreign language on cognitive reflection and numeracy
Milczarski, W., Borkowska, A., Paruzel‐Czachura, M., & Białek, M. (2023) · International Journal of Bilingualism · doi:10.1177/13670069231206701 · Cited by 16
Key in socially driven game dynamics, open the doors of agility - an empirical study on gamification and employee agility
Nivedhitha, K. S. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2093792 · Cited by 14
Risk attitudes: The central tendency bias
Akbari, K., Eigruber, M., & Vetschera, R. (2023) · EURO Journal on Decision Processes · doi:10.1016/j.ejdp.2023.100042 · Cited by 17
Credentials or Chemistry? Entrepreneur Gender and Cofounder Selection
Gray, S., Howell, T., Strassman, J., & Yamamoto, K. (2024) · Academy of Management Journal · doi:10.5465/amj.2022.0640 · Cited by 11
Destigmatization and Its Imbalanced Effects in Labor Markets
Negro, G., Williams, M. J., Pontikes, E., & Lopiano, G. R. (2021) · Management Science · doi:10.1287/mnsc.2020.3881 · Cited by 11
Understanding the relationships between personal growth initiative, hope, and abstinence self‐efficacy
Niles, J. K., Gutiérrez, D., Dukes, A. T., Mullen, P. R., & Goode, C. D. (2022) · Journal of Addictions & Offender Counseling · doi:10.1002/jaoc.12099 · Cited by 14
Conducting Survey Research Using MTurk
Chambers, S., & Nimon, K. (2018) · Advances in library and information science (ALIS) book series · doi:10.4018/978-1-5225-5164-5.ch016 · Cited by 11
Romance 2.0 on Instagram! “What Type of Girlfriend Would You Date?”
Jin, S. A., Ryu, E., & Muqaddam, A. (2019) · Evolutionary Psychology · doi:10.1177/1474704919826845 · Cited by 13
Is value statement an effective informal control for stimulating pro-environmental behaviors?
de Aguiar, A. B. (2020) · Revista Contabilidade & Finanças · doi:10.1590/1808-057x202011500 · Cited by 11
Individual differences in effective interview design: Factors affecting question choice
Speer, A. B., Tenbrink, A. P., Schwendeman, M. G., & Wegmeyer, L. J. (2020) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12297 · Cited by 11
Capitalist Views and Religion
Jones, A. T., Hadsell, L., & Burrus, R. T. (2019) · Eastern Economic Journal · doi:10.1057/s41302-019-00138-3 · Cited by 10
Energized people in prominent places: Political support networks, relational energy, and employee innovation implementation
Grosser, T. J., Sterling, C., & Piplani, R. S. (2023) · Journal of Organizational Behavior · doi:10.1002/job.2734 · Cited by 10
Generalizability of Effect Sizes Within Aviation Research: More Samples Are Needed
Mehta, R., Rice, S. C., Winter, S. R., Spence, T. B., Edwards, M., & Candelaria-Oquendo, K. (2019) · International Journal of Aviation Aeronautics and Aerospace · doi:10.15394/ijaaa.2019.1404 · Cited by 10
Higher-Order Musical Temporal Structure in Bird Song
Bilger, H., Vertosick, E., Vickers, A. J., Kaczmarek, K., & Prum, R. O. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.629456 · Cited by 9
The Effect of Training Self‐Efficacy on Computer‐Based Training Outcomes: Empirical Analysis of the Construct and Creation of Two Scales
Howard, M. C. (2019) · Performance Improvement Quarterly · doi:10.1002/piq.21301 · Cited by 9
A Mixture Model for Random Responding Behavior in Forced-Choice Noncognitive Assessment: Implication and Application in Organizational Research
Peng, S., Man, K., Veldkamp, B. P., Cai, Y., & Tu, D. (2023) · Organizational Research Methods · doi:10.1177/10944281231181642 · Cited by 9
The company you keep: Satisfaction with life, economic freedom, and preference-policy mismatch
Hadsell, L., & Jones, A. T. (2020) · Journal of Comparative Economics · doi:10.1016/j.jce.2019.12.002 · Cited by 8
Is Religious Self-Regulation a Risk or Protective Factor for Men’s Intimate Partner Violence Perpetration?
Davis, R. E., & Renzetti, C. M. (2021) · Journal of Interpersonal Violence · doi:10.1177/0886260520985497 · Cited by 11
Personalizing Affective Stimuli Using a Recommender Algorithm: An Example with Threatening Words for Trauma Exposed Populations
Niles, A. N., & O’Donovan, A. (2018) · Cognitive Therapy and Research · doi:10.1007/s10608-018-9923-8 · Cited by 8
Verifiability and Symptom Endorsement in Genuine, Exaggerated, and Malingered Pain
Akca, A. Y. E., Slootmaekers, L., & Bošković, I. (2020) · Psychological Injury and Law · doi:10.1007/s12207-020-09375-w · Cited by 8
Imagined contact with famous gay men and lesbians reduces heterosexuals’ misidentification concerns and sexual prejudice
LaCosse, J., & Plant, E. A. (2018) · European Journal of Social Psychology · doi:10.1002/ejsp.2391 · Cited by 7
Eyewitness identification performance is not affected by time-of-day optimality
Yaremenko, S., Sauerland, M., & Hope, L. (2021) · Scientific Reports · doi:10.1038/s41598-021-82628-z · Cited by 8
Marketing Influences on Perceptions of Reduced Nicotine Content Cigarettes
Johnson, A. C., Mays, D. M., Villanti, A. C., NIAURA, R. S., Rehberg, K., Phan, L., Mercincavage, M., Luta, G., & Strasser, A. A. (2019) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntz167 · Cited by 7
Unpacking the Quantifying and Qualifying Potential of Semi-Open Job Satisfaction Questions through Computer-Aided Sentiment Analysis
Wijngaards, I., Burger, M., & van Exel, J. N. A. (2020) · Journal of Well-Being Assessment · doi:10.1007/s41543-021-00040-w · Cited by 9
When invisible eyes undermine proactive efforts: the effect of electronic performance monitoring on job crafting
Liu, W., Zhou, K., Ouyang, X., Chen, S., & Gao, K. (2025) · Internet Research · doi:10.1108/intr-03-2024-0422 · Cited by 6
Toloka platform as a source of online survey participants: an experience of assessing data quality
Gavrilov, K. (2022) · Sociology methodology methods mathematical modeling (Sociology 4M) · doi:10.19181/4m.2021.53.5 · Cited by 6
Good for the Many or Best for the Few?
Dove, G., Balestra, M., Mann, D., & Nov, O. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415239 · Cited by 7
System Design Choice in the Sharing Economy: How Different Institutional Logics Drive Consumer Perception and Consumers’ Intention to Use Sharing Systems
Clemens, V., Sabel, C. A., Foege, J. N., & Nüesch, S. (2022) · Schmalenbach Journal of Business Research · doi:10.1007/s41471-022-00133-z · Cited by 6
The verb–self link: An implicit association test study
Weis, P. P., Nikadon, J., Herbert, C., & Formanowicz, M. (2022) · Psychonomic Bulletin & Review · doi:10.3758/s13423-022-02105-0 · Cited by 6
Perceptions of plea bargains for driving under the influence (DUI) cases involving alcohol and marijuana
Webster, J. M., Golding, J. M., Malik, S. E., & Riederer, A. M. (2020) · Psychology Crime and Law · doi:10.1080/1068316x.2020.1742342 · Cited by 6
Grammatical Redundancy in Scales: Using the “ConGRe” Process to Create Better Measures
Alley, L., Kadolkar, I., Gupta, A., Cortina, J. M., & Winsler, K. (2024) · Journal of Management · doi:10.1177/01492063241291542 · Cited by 5
Learning From Mistakes: Impact of Careless Responses on Counseling Research Using Amazon’s Mechanical Turk
Kim, N., & Oh, J. (2022) · Measurement and Evaluation in Counseling and Development · doi:10.1080/07481756.2022.2073895 · Cited by 7
Novel Approaches for Improving data Quality from Self-Administered Questionnaires
Hyman, M. R., Kostyk, A., Zhou, W., & Paas, L. J. (2019) · International Journal of Market Research · doi:10.1177/1470785319870622a · Cited by 5
Predicting Personality with Smartphone Cameras: A Pilot Study
Liu, I., Ni, S., & Peng, K. (2020) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms49158.2020.9209354 · Cited by 5

Authors: Fleischer, A., Mead, A. D., & Huang, J.
Affiliations: Illinois Institute of Technology (US)
Corresponding author: Fleischer, Avi
ORCID: Huang, Jialin
Venue: Industrial and Organizational Psychology
Publisher: Cambridge University Press (CUP)
Published: 2015-06
Volume: 8
Issue: 2
Pages: 196-202
ISSN: 1754-9426 (ISSN-L)
1754-9434
DOI: 10.1017/iop.2015.25
OpenAlex: W2199108421
Cited by: 151
References: 20
Citations by year: 2026: 8 · 2025: 9 · 2024: 10 · 2023: 13 · 2022: 22 · 2021: 19 · 2020: 25 · 2019: 23 · 2018: 13 · 2017: 6 · 2016: 3
FWCI: 11.00
Type: journal article
Language: en
Open access: closed
Licence: www.cambridge.org/core/terms (unspecified, 57 d)
Locations: Industrial and Organizational Psychology (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Keywords: Amazon Mechanical Turk, online sample, sample quality, convenience samples, low-stakes assessment, student samples, data cleaning, response validity

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 ›

Are you an author, or do you see a problem with this page? Request a review

Inattentive Responding in MTurk and Other Online Samples

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks