The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index.
Reputation as a sufficient condition for data quality on Amazon Mechanical Turk
Open accessAbstract
Data quality is one of the major concerns of using crowdsourcing websites such as Amazon Mechanical Turk (MTurk) to recruit participants for online behavioral studies. We compared two methods for ensuring data quality on MTurk: attention check questions (ACQs) and restricting participation to MTurk workers with high reputation (above 95% approval ratings). In Experiment 1, we found that high-reputation workers rarely failed ACQs and provided higher-quality data than did low-reputation workers; ACQs improved data quality only for low-reputation workers, and only in some cases. Experiment 2 corroborated these findings and also showed that more productive high-reputation workers produce the highest-quality data. We concluded that sampling high-reputation workers can ensure high-quality data without having to resort to using ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The cited source says what the claim attributes to it.
In the citing paper
“Data obtained from MTurk has been demonstrated to be valid and reliable when appropriate selection criteria and attention checks are used (Casler, Bickel, & Hackett, 2013; Peer, Vosgerau, & Acquisti, 2014), as was the case in this study.”
From The development and evaluation of a brief form of the Normative Male Alexithymia Scale (NMAS-BF). (2019) · cites this paper as Peer, Vosgerau, & Acquisti, 2014
The cited source says what the claim attributes to it.
In the citing paper
“Most of the sites we reviewed have supervision and reputation mechanisms designed to measure the quality of submitted work, and to allow researchers to invite workers with high reputation (similar to MTurk's”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
In the citing paper
“Amazon Mechanical Turk is an online sample recruiting platform that, although not nationally representative, yields high data quality through the use of attention checks 44 and worker reputation scores 45 .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [45]
In the citing paper
“Following prior research, we selected participants with an approval rating above 95% on Amazon Mechanical Turk 45 .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [45]
In the citing paper
“which showed that ACQs have low diagnostic ability when data quality is already high.”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., (2014)
In the citing paper
“they are reliable and validated scales, and b) they have been previously used with success to measure data quality on MTurk (Peer et al., 2014).”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
In the citing paper
“they are reliable and validated scales, and b) they have been previously used with success to measure data quality on MTurk (Peer et al., 2014).”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
In the citing paper
“Using two attention-check questions (Peer et al., 2014), we checked whether participants read and paid attention to our instructions.”
From Beyond the Turk: Alternative platforms for crowdsourcing behavioral research (2017) · cites this paper as Peer et al., 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691610393980 · Cited by 9,996
A very brief measure of the Big-Five personality domains
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003) · Journal of Research in Personality · doi:10.1016/s0092-6566(03)00046-1 · Cited by 8,307
Running experiments on Amazon Mechanical Turk
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500002205 · Cited by 3,767
Instructional manipulation checks: Detecting satisficing to increase statistical power
Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2009.03.009 · Cited by 3,493
Robins, R. W., Hendin, H. M., & Trzesniewski, K. H. (2001) · Personality and Social Psychology Bulletin · doi:10.1177/0146167201272002 · Cited by 3,041
The Efficient Assessment of Need for Cognition
Cacioppo, J. T., Petty, R. E., & Feng Kao, C. (1984) · Journal of Personality Assessment · doi:10.1207/s15327752jpa4803_13 · Cited by 2,913
Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013) · Journal of Behavioral Decision Making · doi:10.1002/bdm.1753 · Cited by 2,242
Rosenberg, M. (2011) · PsycTESTS Dataset · doi:10.1037/t01038-000 · Cited by 1,226
Cited by
Sorted by citations
Items 201–300 of 1,000
Schaarschmidt, M., & Walsh, G. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.11.027 · Cited by 76
van den Broek‐Altenburg, E. M., & Atherly, A. J. (2020) · Health Economics Review · doi:10.1186/s13561-020-00276-x · Cited by 70
Arli, D. I., Septianto, F., & Chowdhury, R. M. M. I. (2020) · Journal of Business Ethics · doi:10.1007/s10551-019-04414-2 · Cited by 72
Clusters of trauma types as measured by the Life Events Checklist for DSM–5.
Contractor, A. A., Weiss, N. H., Batley, P. N., & Elhai, J. D. (2020) · International Journal of Stress Management · doi:10.1037/str0000179 · Cited by 72
How Consumers Value Retailer's Return Policy Leniency Levers: An Empirical Investigation
Abdulla, H., Abbey, J. D., & Ketzenberg, M. E. (2019) · Production and Operations Management · doi:10.1111/poms.13640 · Cited by 70
Wang, Z., & Jiang, F. (2023) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2023.103860 · Cited by 69
Beymer, M. R., Holloway, I. W., & Grov, C. (2017) · Archives of Sexual Behavior · doi:10.1007/s10508-016-0932-y · Cited by 69
Workplace spirituality buffers the effects of emotional labour on employee well-being
Zou, W., & Dahling, J. J. (2017) · European Journal of Work and Organizational Psychology · doi:10.1080/1359432x.2017.1358164 · Cited by 69
Vacation Posts on Facebook: A Model for Incidental Vicarious Travel Consumption
Marder, B. L., Archer‐Brown, C., Colliander, J., & Lambert, A. (2018) · Journal of Travel Research · doi:10.1177/0047287518786465 · Cited by 69
How to evaluate data visualizations across different levels of understanding
Burns, A., Xiong, C., Franconeri, S., Cairo, A., & Mahyar, N. (2020) · IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) · doi:10.1109/beliv51497.2020.00010 · Cited by 68
Shafieizadeh, K., Alotaibi, S., & Tao, C.-W. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102800 · Cited by 68
Who Wears the MAGA Hat? Racial Beliefs and Faith in Trump
Graham, A., Cullen, F. T., Butler, L. C., Burton, A. L., & Burton, V. S. (2021) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023121992600 · Cited by 69
Behavior Ever Follows Intention?
Egelman, S., Harbach, M., & Péer, E. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858265 · Cited by 70
The effects of time frames on self-report
Walentynowicz, M., Schneider, S., & Stone, A. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201655 · Cited by 80
Wang, W., Kang, S., & Choi, S. B. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.807070 · Cited by 94
Self-construal and gender interact to cause social evaluative concerns
Russell, L. H., Gould, K., & Fergus, T. A. (2016) · Personality and Individual Differences · doi:10.1016/j.paid.2016.12.041 · Cited by 65
Kratom use as more than a “self-treatment”
Smith, K. E., Dunn, K. E., Rogers, J. M., Grundmann, O., McCurdy, C. R., Garcia‐Romeu, A., Schriefer, D., Swogger, M. T., & Epstein, D. H. (2022) · The American Journal of Drug and Alcohol Abuse · doi:10.1080/00952990.2022.2083967 · Cited by 66
Stadthagen-González, H., López, L., Couto, M. C. P., & Párraga, C. A. (2017) · Linguistic Approaches to Bilingualism · doi:10.1075/lab.16030.sta · Cited by 64
Schaarschmidt, M. (2016) · European Management Journal · doi:10.1016/j.emj.2016.02.005 · Cited by 73
Speaking to the Self: How Motivating Language Links With Self-Leadership
Mayfield, J. R., Mayfield, M., & Neck, C. P. (2017) · International Journal of Business Communication · doi:10.1177/2329488417731861 · Cited by 77
Cognitive failures in response to emotional contagion: Their effects on workplace accidents
Петитта, Л., Probst, T. M., Ghezzi, V., & Barbaranelli, C. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.01.018 · Cited by 63
Improving the Statistical Power and Reliability of Research Using Amazon Mechanical Turk
Bentley, J. W. (2021) · Accounting Horizons · doi:10.2308/horizons-18-052 · Cited by 61
Meixner, O., & Katt, F. (2020) · Sustainability · doi:10.3390/su12187270 · Cited by 62
Kim, M., & Beehr, T. A. (2021) · The International Journal of Human Resource Management · doi:10.1080/09585192.2021.1934711 · Cited by 69
Estimates of Non-Heterosexual Prevalence: The Roles of Anonymity and Privacy in Survey Methodology
Robertson, R. E., Tran, F. W., Lewark, L. N., & Epstein, R. (2017) · Archives of Sexual Behavior · doi:10.1007/s10508-017-1044-z · Cited by 61
Psychopathy and risk taking: Examining the role of risk perception
Hosker-Field, A. M., Molnar, D. S., & Book, A. S. (2015) · Personality and Individual Differences · doi:10.1016/j.paid.2015.11.059 · Cited by 61
Fight or flight: Integral emotions and violent intentions
Barnum, T. C., & Solomon, S. J. (2019) · Criminology · doi:10.1111/1745-9125.12222 · Cited by 61
An analyst by any other surname: Surname favorability and market reaction to analyst forecasts
Jung, J. H., Kumar, A., Lim, S. S., & Yoo, C. (2019) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2019.02.002 · Cited by 60
Distinction without a difference? An assessment of MTurk Worker types
Loepp, E. D., & Kelly, J. T. (2020) · Research & Politics · doi:10.1177/2053168019901185 · Cited by 60
Condoms and bananas: Shock advertising explained through congruence theory
Lee, M., Septianto, F., Frethey‐Bentham, C., & Gao, E. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102228 · Cited by 59
How Black Are Lakisha and Jamal? Racial Perceptions from Names Used in Correspondence Audit StudiesPreprint
Gaddis, S. M. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2807176 · Cited by 59
Luo, A., & Mattila, A. S. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102613 · Cited by 58
Not All Homes Are Safe: Family Violence Following the Onset of the Covid-19 Pandemic
Drotning, K. J., Doan, L., Sayer, L. C., Fish, J. N., & Rinderknecht, R. G. (2022) · Journal of Family Violence · doi:10.1007/s10896-022-00372-y · Cited by 69
It’s Not Me, It’s Not You, It’s Us! An Empirical Examination of Relational Attributions
Eberly, M., Holley, E. C., Johnson, M. D., & Mitchell, T. R. (2017) · Journal of Applied Psychology · doi:10.1037/apl0000187 · Cited by 56
Gebauer, J. E., Sedikides, C., & Schrade, A. (2017) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000140 · Cited by 59
Food waste prevention behavior in the context of hedonic and utilitarian shopping value
Katt, F., & Meixner, O. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.122878 · Cited by 61
Moore, Z., Harrison, D. E., & Hair, J. F. (2021) · International Journal of Market Research · doi:10.1177/14707853211052183 · Cited by 76
Ali, F., El-Manstrly, D. A., & Abbasi, G. A. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114138 · Cited by 63
Cunningham, J. A., Godinho, A., & Kushnir, V. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0440-3 · Cited by 57
Schema learning for the cocktail party problem
Woods, K. J. P., & McDermott, J. H. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801614115 · Cited by 56
Simulated thoughts in virtual reality for negotiation training enhance self-efficacy and knowledge
Ding, D., Brinkman, W., & Neerincx, M. A. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102400 · Cited by 59
Parenting Stress, Parenting, and Adolescent Externalizing Problems
Kochanova, K., Pittman, L. D., & Pabis, J. M. (2021) · Journal of Child and Family Studies · doi:10.1007/s10826-021-01996-2 · Cited by 56
Exploring uses and gratifications and psychological outcomes of engagement with Instagram Stories
Lu, J.-D., & Lin, J.-S. (2022) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2022.100198 · Cited by 82
Iorio, A. (2022) · Administrative Science Quarterly · doi:10.1177/00018392221092242 · Cited by 57
Mate Value Discrepancy and Mate Retention Behaviors of Self and Partner
Sela, Y., Mogilski, J. K., Shackelford, T. K., Zeigler‐Hill, V., & Fink, B. (2016) · Journal of Personality · doi:10.1111/jopy.12281 · Cited by 56
Amazon’s Mechanical Turk as a Viable Source for Organizational and Occupational Health Research
Michel, J. S., O’Neill, S. K., Hartman, P., & Lorys, A. (2017) · Occupational Health Science · doi:10.1007/s41542-017-0009-x · Cited by 65
Reacting to the scope of a data breach: The differential role of fear and anger
Chatterjee, S., Gao, X., Sarkar, S., & Uzmanoglu, C. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.04.024 · Cited by 55
“I Guess I’ll Never Know…”: Non-Initiators Account-Making After Being Ghosted
LeFebvre, L. E., Rasner, R. D., & Allen, M. R. (2019) · Journal of Loss and Trauma · doi:10.1080/15325024.2019.1694299 · Cited by 55
Do judgments of learning directly enhance learning of educational materials?
Ariel, R., Karpicke, J. D., Witherby, A. E., & Tauber, S. K. (2021) · Educational Psychology Review · doi:10.1007/s10648-020-09556-8 · Cited by 55
Social class, social self-esteem, and conspicuous consumption
Oh, G. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06318 · Cited by 68
Strickland, J. C., Reed, D. D., Hursh, S. R., Schwartz, L. P., Foster, R. N. S., Gelino, B. W., LeComte, R. S., Oda, F. S., Salzer, A. R., Schneider, T. D., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0258828 · Cited by 63
Map LineUps: Effects of spatial structure on graphical inference
Beecham, R., Dykes, J. A., Meulemans, W., Slingsby, A., Turkay, Ç., & Wood, J. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598862 · Cited by 55
Smith, K. E., Rogers, J. M., Dunn, K. E., Grundmann, O., McCurdy, C. R., Schriefer, D., & Epstein, D. H. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.765917 · Cited by 54
Barbaro, N., Pham, M. N., & Shackelford, T. K. (2015) · Personality and Individual Differences · doi:10.1016/j.paid.2015.02.033 · Cited by 55
Factors Motivating Customization and Echo Chamber Creation Within Digital News Environments
Auxier, B. E., & Jessica Vitak, B. (2019) · Social Media + Society · doi:10.1177/2056305119847506 · Cited by 71
Weinshel, B., Wei, M., Mondal, M., Choi, E., Shan, S., Dolin, C., Mazurek, M. L., & Ur, B. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363200 · Cited by 63
Mathis, F., Vaniea, K. E., & Khamis, M. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445478 · Cited by 53
Shin, Y. H., Jung, S. E., Im, J., & Severt, K. (2020) · Journal of Foodservice Business Research · doi:10.1080/15378020.2020.1770043 · Cited by 53
Jang, W. W., Byon, K. K., Baker, T., & Tsuji, Y. (2020) · Sport Business and Management An International Journal · doi:10.1108/sbm-10-2019-0087 · Cited by 62
Who Are Virtual Reality Headset Owners? A Survey and Comparison of Headset Owners and Non-Owners
Kelly, J. W., Cherep, L. A., Lim, A., Doty, T. A., & Gilbert, S. B. (2021) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr50410.2021.00095 · Cited by 55
Learning to recommend similar items from human judgments
Trattner, C., & Jannach, D. (2019) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-019-09245-4 · Cited by 52
Ziv, M., Izhikevich, L., Ruth, K., Izhikevich, K., & Durumeric, Z. (2021) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3487552.3487853 · Cited by 52
Green consumer segmentation: consumer motivations for purchasing pro-environmental products
Lee, J., & Haley, E. (2022) · International Journal of Advertising · doi:10.1080/02650487.2022.2038431 · Cited by 65
Smidt, A. M., Adams-Clark, A. A., & Freyd, J. J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278830 · Cited by 61
Blockchain empowerment: enhancing consumer trust and outreach through supply chain transparency
Duan, Y., & Zhu, Q. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2434951 · Cited by 62
A Latent Factor Analysis of Working Memory Measures Using Large-Scale Data
Waris, O., Soveri, A., Ahti, M., Hoffing, R. C., Ventus, D., Jaeggi, S. M., Seitz, A. R., & Laine, M. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01062 · Cited by 51
Wang, S. (2020) · Digital Journalism · doi:10.1080/21670811.2020.1851279 · Cited by 56
Qiao, W., Yan, Z., & Wang, X. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102634 · Cited by 52
Kamphorst, B. A., Nauts, S., de Ridder, D. T. D., & Anderson, J. H. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00252 · Cited by 54
Attention checks and how to use them: Review and practical recommendations
Muszyński, M. A. (2023) · Ask Research and Methods · doi:10.18061/ask.v32i1.0001 · Cited by 68
Desire or Disease? Framing Obesity to Influence Attributions of Responsibility and Policy Support
McGlynn, J., & McGlone, M. S. (2018) · Health Communication · doi:10.1080/10410236.2018.1431025 · Cited by 51
Dunlop, P. D., Bourdage, J. S., de Vries, R. E., McNeill, I. M., Jorritsma, K., Orchard, M., Austen, T., Baines, T., & Choe, W.-K. (2019) · Journal of Applied Psychology · doi:10.1037/apl0000463 · Cited by 56
Saffran, L., Hu, S.-S., Hinnant, A., Scherer, L. D., & Nagel, S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226711 · Cited by 41
Jeste, D. V., Thomas, M. L., Liu, J., Daly, R. E., Tu, X. M., Treichler, E. B. H., Palmer, B. W., & Lee, E. E. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.09.033 · Cited by 50
Informant Discrepancies in Judgments About Change During Mental Health Treatments
Marsh, J. K., Zeveney, A. S., & Reyes, A. D. L. (2020) · Clinical Psychological Science · doi:10.1177/2167702619894905 · Cited by 50
Karwatzki, S., Trenz, M., & Veit, D. J. (2022) · Information Systems Journal · doi:10.1111/isj.12386 · Cited by 64
Sharing Policies in Multiuser Privacy Scenarios
Fogues, R., Murukannaiah, P. K., Such, J. M., & Singh, M. P. (2017) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3038920 · Cited by 55
The search for meaningful work: A network analysis of personality and the job characteristics model
Simonet, D. V., & Castille, C. M. (2019) · Personality and Individual Differences · doi:10.1016/j.paid.2019.109569 · Cited by 66
Kim, J., & Loewenstein, J. (2020) · Journal of Business Ethics · doi:10.1007/s10551-020-04457-w · Cited by 50
Does social influence turn pessimistic consumers green?
Sadiq, M., Adil, M., & Paul, J. (2021) · Business Strategy and the Environment · doi:10.1002/bse.2780 · Cited by 50
Péer, E., Samat, S., Brandimarte, L., & Acquisti, A. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2594183 · Cited by 44
Self-citationSupply chain transparency and willingness-to-pay for refurbished products
Duan, Y., & Aloysius, J. A. (2019) · The International Journal of Logistics Management · doi:10.1108/ijlm-01-2019-0025 · Cited by 49
Supply chain carbon transparency to consumers via blockchain: does the truth hurt?
Zhu, Q., Duan, Y.-J., & Sarkis, J. (2023) · International Journal of Logistics Management · doi:10.1108/ijlm-03-2023-0109 · Cited by 36
Hong, E., & Park, J. (2023) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2023.11.005 · Cited by 50
Barbaro, N., Sela, Y., Atari, M., Shackelford, T. K., & Zeigler‐Hill, V. (2018) · Journal of Social and Personal Relationships · doi:10.1177/0265407517749330 · Cited by 56
Khan, R., Murtaza, G., Neveu, J., & Newman, A. (2021) · Applied Psychology · doi:10.1111/apps.12316 · Cited by 53
Smith, K. E., Dunn, K. E., Grundmann, O., Garcia‐Romeu, A., Rogers, J. M., Swogger, M. T., & Epstein, D. H. (2021) · Experimental and Clinical Psychopharmacology · doi:10.1037/pha0000518 · Cited by 51
Rubel, A. N., & Burleigh, T. J. (2018) · Sexualities · doi:10.1177/1363460718779781 · Cited by 49
Augmented reality enhanced experiences in restaurants: Scale development and validation
Ali, F. (2022) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2022.103180 · Cited by 48
Diminished Control in Crowdsourcing
Gould, S. J. J., Cox, A. L., & Brumby, D. P. (2016) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2928269 · Cited by 51
Dying Is Unexpectedly Positive
Goranson, A., Ritter, R. S., Waytz, A. G., Norton, M. I., & Gray, K. J. (2017) · Psychological Science · doi:10.1177/0956797617701186 · Cited by 48
Can Science Explain the Human Mind? Intuitive Judgments About the Limits of Science
Gottlieb, S., & Lombrozo, T. (2017) · Psychological Science · doi:10.1177/0956797617722609 · Cited by 48
Strickland, J. C., & Stoops, W. W. (2018) · Journal of the Experimental Analysis of Behavior · doi:10.1002/jeab.445 · Cited by 47
Towards A Taxonomy of Content Sensitivity and Sharing Preferences for Photos
Li, Y., Vishwamitra, N., Hu, H., & Caine, K. E. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376498 · Cited by 49
Effect of Conformity on Perceived Trustworthiness of News in Social Media
Wijenayake, S., Hettiachchi, D., Hosio, S. J., Kostakos, V., & Gonçalves, J. (2020) · IEEE Internet Computing · doi:10.1109/mic.2020.3032410 · Cited by 48
Bazzoli, A., & Probst, T. M. (2022) · Safety Science · doi:10.1016/j.ssci.2022.105703 · Cited by 48
Are Individuals More Willing to Lie to a Computer or a Human? Evidence from a Tax Compliance Setting
LaMothe, E. G., & Bobek, D. D. (2020) · Journal of Business Ethics · doi:10.1007/s10551-019-04408-0 · Cited by 49
Kulig, T. C., Graham, A., Cullen, F. T., Piquero, A. R., & Haner, M. (2020) · Journal of Criminology · doi:10.1177/0004865820969760 · Cited by 52
Insecure romantic attachment dimensions and frequency of mate retention behaviors
Barbaro, N., Pham, M. N., Shackelford, T. K., & Zeigler‐Hill, V. (2016) · Personal Relationships · doi:10.1111/pere.12146 · Cited by 56
Erskine, M. A., Khojah, M., & McDaniel, A. E. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.07.014 · Cited by 51
Metadata
- Authors
- Peer, E., Vosgerau, J., & Acquisti, A.
- Affiliations
- Tilburg University (NL)
- Carnegie Mellon University (US)
- Venue
- Behavior Research Methods
- Published
- 2013-12-20
- Volume
- 46
- Issue
- 4
- Pages
- 1023-1031
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2105753174
- Cited by
- 1,618
- References
- 18
- Citations by year
- 2026: 68 · 2025: 96 · 2024: 128 · 2023: 167 · 2022: 175 · 2021: 212 · 2020: 210 · 2019: 147 · 2018: 119 · 2017: 132 · 2016: 91 · 2015: 49 · 2014: 17 · 2013: 1
- FWCI
- 79.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Behavior Research Methods (publishedVersion)
- Research portal (Tilburg University) (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion)
- KiltHub Repository
- 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
- data quality, Amazon Mechanical Turk, worker reputation, crowdsourcing, selection bias, labor productivity, participant recruitment
- MeSH terms
- Data Collection, Humans, Research Design, Patient Selection, Internet, Behavioral Research, Crowdsourcing
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
