The DOI matches its Crossref record.
A law of comparative judgment.
Abstract
This chapter describes a new psychophysical law which may be called the law of comparative judgment and to show some of its special applications in the measurement of psychological values. The law of comparative judgment is applicable not only to the comparison of physical stimulus intensities but also to qualitative comparative judgments such as those of excellence of specimens in an educational scale. The scale difference between the discriminal processes of two specimens which are involved in the same judgment will be called the discriminal difference on that occasion. The law of comparative judgment is basic for all experimental work on Weber's law, Fechner's law, and for all educational and psychological scales in which comparative judgments are involved. The formulation of the law of comparative judgment involves the use of a new psychophysical concept, namely, the discriminal dispersion.
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.
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
Cited by
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Elimination by aspects: A theory of choice
Tversky, A. (1972) · Psychological Review · doi:10.1037/h0032955 · Cited by 3,365
The trouble with overconfidence.
Moore, D. A., & Healy, P. J. (2008) · Psychological Review · doi:10.1037/0033-295x.115.2.502 · Cited by 2,358
Textural Features Corresponding to Visual Perception
Tamura, H., Mori, S., & Yamawaki, T. (1978) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1978.4309999 · Cited by 2,155
Tufford (2017) · doi:10.1007/s · Cited by 2,003
Defining Twenty-First Century Skills
Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M. (2012) · Assessment and Teaching of 21st Century Skills · doi:10.1007/978-94-007-2324-5_2 · Cited by 1,809
Conducting Discrete Choice Experiments to Inform Healthcare Decision Making: A User??s Guide
Lancsar, E., & Louviere, J. (2008) · PharmacoEconomics · doi:10.2165/00019053-200826080-00004 · Cited by 1,707
Tourism motivation and expectation formation
Gnoth, J. (1997) · Annals of Tourism Research · doi:10.1016/s0160-7383(97)80002-3 · Cited by 1,020
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906
3-D sound for virtual reality and multimedia
Madole, D., & Begault, D. (1995) · Computer Music Journal · doi:10.2307/3680997 · Cited by 868
Discrete Choice Experiments Are Not Conjoint Analysis
Louviere, J. J., Flynn, T. N., & Carson, R. T. (2010) · Journal of Choice Modelling · doi:10.1016/s1755-5345(13)70014-9 · Cited by 738
Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.
Pleskac, T. J., & Busemeyer, J. R. (2010) · Psychological Review · doi:10.1037/a0019737 · Cited by 722
A Sequential Theory of Psychological Discrimination
Link, S. W., & Heath, R. A. (1975) · Psychometrika · doi:10.1007/bf02291481 · Cited by 492
Attention gating in short-term visual memory.
Reeves, A., & Sperling, G. (1986) · Psychological Review · doi:10.1037/0033-295x.93.2.180 · Cited by 422
The Measurement of Values and Individualism-Collectivism
Oishi, S., Schimmack, U., Diener, E., & Suh, E. M. (1998) · Personality and Social Psychology Bulletin · doi:10.1177/01461672982411005 · Cited by 327
Human-machine collaboration in managerial decision making
Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106730 · Cited by 280
Why Partisans Do Not Sort: The Constraints on Political Segregation
Mummolo, J., & Nall, C. (2017) · The Journal of Politics · doi:10.1086/687569 · Cited by 269
The relative judgment theory of two choice response time
Link, S. W. (1975) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(75)90053-x · Cited by 251
Harvey, N. (1997) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(97)01014-0 · Cited by 239
The accumulator model of two-choice discrimination
Smith, P. L., & Vickers, D. (1988) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(88)90043-0 · Cited by 223
Neural tuning and representational geometry
Kriegeskorte, N., & Wei, X.-X. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00502-3 · Cited by 187
A Psychometric Analysis of Chess Expertise
Van Der Maas, H. L. J., & Wagenmakers, E.-J. (2005) · The American Journal of Psychology · doi:10.2307/30039042 · Cited by 186
New Developments in Econometric Methods for Labor Market Analysis
(1999) · Handbook of Labor Economics · doi:10.1016/s1573-4463(99)03005-9 · Cited by 152
Algorithmic monoculture and social welfare
Kleinberg, J., & Raghavan, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018340118 · Cited by 147
Brown, N. J. L., & Heathers, J. A. J. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550616673876 · Cited by 127
Goldstein, W. M., & Weber, E. U. (1995) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60308-4 · Cited by 119
Schneider, W. (1986) · Journal of Experimental Child Psychology · doi:10.1016/0022-0965(86)90024-x · Cited by 99
A Revealed Preference Ranking of U.S. Colleges and Universities *
Avery, C. N., Glickman, M. E., Hoxby, C. M., & Metrick, A. (2013) · The Quarterly Journal of Economics · doi:10.1093/qje/qjs043 · Cited by 97
The burden of social proof: Shared thresholds and social influence
MacCoun, R. J. (2012) · Psychological Review · doi:10.1037/a0027121 · Cited by 84
Auslander, L. A., & Jeste, D. V. (2002) · Community Mental Health Journal · doi:10.1023/a:1019808412017 · Cited by 68
A Survey of Reinforcement Learning from Human Feedback
Kaufmann, T., Weng, P., Bengs, V., & Hüllermeier, E. (2023) · arXiv · doi:10.48550/arxiv.2312.14925 · Cited by 43
Consumer Demand for Circular Products: Identifying Customer Segments in the Circular Economy
Boyer, R. H. W., Hunka, A. D., & Whalen, K. A. (2021) · Sustainability · doi:10.3390/su132212348 · Cited by 39
Estimating Item Difficulty With Comparative Judgments
Attali, Y., Saldivia, L., Jackson, C., Schuppan, F., & Wanamaker, W. (2014) · ETS Research Report Series · doi:10.1002/ets2.12042 · Cited by 22
Texting while driving: a discrete choice experiment
Foreman, A. M., Friedel, J. E., Hayashi, Y., & Wirth, O. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105823 · Cited by 20
Seo, J., Heidenreich, S., Aldalooj, E., Poon, J. L., Spaepen, E., Eby, E. L., & Newson, R. S. (2023) · The Patient - Patient-Centered Outcomes Research · doi:10.1007/s40271-022-00610-x · Cited by 19
Changing Values over the Last Half-Century: The Story of Thurstone's Crime Scales
Krus, D. J., Sherman, J. L., & Krus, P. H. (1977) · Psychological Reports · doi:10.2466/pr0.1977.40.1.207 · Cited by 15
Ou, C., Buschek, D., Mayer, S., & Butz, A. (2022) · Mensch und Computer 2022 · doi:10.1145/3543758.3543761 · Cited by 7
It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing
Rupp, F., Puddu, A., Becker-Asano, C., & Eckert, K. (2024) · 2024 IEEE Conference on Games (CoG) · doi:10.1109/cog60054.2024.10645642 · Cited by 4
Comparative judgement for advancing research in applied linguistics
Thwaites, P., & Paquot, M. (2024) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2024.100142 · Cited by 3
Characterizing AI-Generated Misinformation on Social Media
Drolsbach, C., Demirel, E., & Pröllochs, N. (2025) · arXiv · doi:10.48550/arxiv.2505.10266
Algorithmic monoculture and social welfare
(2023) · The Societal Impacts of Algorithmic Decision-Making · doi:10.1145/3603195.3603202
Metadata
- Authors
- Thurstone, L. L.
- Venue
- Psychological Review
- Published
- 1927-07
- Volume
- 34
- Issue
- 4
- Pages
- 273-286
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2033442452
- Cited by
- 5,210
- References
- 0
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
