The DOI matches its Crossref record.
Item Response Theory
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Item Response Theory (IRT), regarded as a modern test theory, models the probability of a correct response as a function of an examinee’s latent proficiency (θ) and item characteristics, independent of the specific test-taker sample—unlike Classical Test Theory, which yields sample-dependent scores24,25.”
From Leveraging computerized adaptive testing for cost-effective evaluation of large language models in medical benchmarking (2026) · cites this paper as [25]
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Items 501–600 of 1,001
Kyriakides, L. E., Charalambous, E., Creemers, B., Antoniou, P., Devine, D., Papastylianou, D., & Fahie, D. (2018) · Educational Assessment Evaluation and Accountability · doi:10.1007/s11092-018-9289-1 · Cited by 31
Collaborators OVAMA consensus group, Horbach, S. E. R., Rongen, A. P. M., Elbers, R. G., van der Horst, C. M. A. M., Prinsen, C. A. C., & Spuls, P. I. (2019) · Quality of Life Research · doi:10.1007/s11136-019-02301-x · Cited by 30
Zimmer, F., Draxler, C., & Debelak, R. (2022) · Psychometrika · doi:10.1007/s11336-022-09883-5 · Cited by 31
Item Response Theory: A Statistical Framework for Educational and Psychological Measurement
Chen, Y., Li, X., Liu, J., & Ying, Z. (2025) · Statistical Science · doi:10.1214/23-sts896 · Cited by 30
A Comparison of Graded Response and Rasch Partial Credit Models with Subjective Well-Being
Baker, J. G., Rounds, J., & Zevon, M. A. (2000) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986025003253 · Cited by 30
Sensitivity of the Linear Logistic Test Model to Misspecification of the Weight Matrix
Baker, F. B. (1993) · Applied Psychological Measurement · doi:10.1177/014662169301700301 · Cited by 30
GENERATIVE RESPONSE MODELING: LEVERAGING THE COMPUTER AS A TEST DELIVERY MEDIUM
Bejar, I. I. (1996) · ETS Research Report Series · doi:10.1002/j.2333-8504.1996.tb01691.x · Cited by 30
The utility of item response modeling in marketing research
Raykov, T., & Calantone, R. J. (2014) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0391-8 · Cited by 30
A Method for the Comparison of Item Selection Rules in Computerized Adaptive Testing
Barrada, J. R., Olea, J., Ponsoda, V., & Abad, F. J. (2010) · Applied Psychological Measurement · doi:10.1177/0146621610370152 · Cited by 30
Standard‐Setting Methods as Measurement Processes
Nichols, P. D., Twing, J. S., Mueller, C. D., & O’Malley, K. J. (2010) · Educational Measurement Issues and Practice · doi:10.1111/j.1745-3992.2009.00166.x · Cited by 30
Eckes, T. (2013) · Language Testing · doi:10.1177/0265532213492969 · Cited by 30
Gibbons, R. D., Alegria, M. M., Markle, S. L., Fuentes, L., Zhang, L., Carmona, R., Francisco Collazos, D., Wang, Y., & Baca‐García, E. (2020) · Addiction · doi:10.1111/add.14938 · Cited by 30
Scale Shrinkage and the Estimation of Latent Distribution Parameters
Camilli, G. (1988) · Journal of Educational Statistics · doi:10.2307/1164653 · Cited by 30
Prince, R. N., & Frith, V. (2019) · ZDM · doi:10.1007/s11858-019-01063-7 · Cited by 29
Identifying Local Dependence With a Score Test Statistic Based on the Bifactor Logistic Model
Liu, Y., & Thissen, D. (2012) · Applied Psychological Measurement · doi:10.1177/0146621612458174 · Cited by 29
Penfield, R. D., & Bergeron, J. M. (2005) · Applied Psychological Measurement · doi:10.1177/0146621604270412 · Cited by 30
Escorial, S., & Navas, M. J. (2007) · Educational and Psychological Measurement · doi:10.1177/0013164406299108 · Cited by 29
Mokken Scale Analysis: Theoretical Considerations and an Application to Transitivity Tasks
Sijtsma, K., & Verweij, A. C. (1992) · Applied Measurement in Education · doi:10.1207/s15324818ame0504_5 · Cited by 29
Holt, J. C. T., van Duijn, M. A. J., & Boomsma, A. (2010) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 29
Female Sexual Function Index Short Version: A MsFLASH Item Response Analysis
Carpenter, J. S., Jones, S. M. W., Studts, C. R., Heiman, J. R., Reed, S. D., Newton, K. M., Guthrie, K. A., Larson, J. C., Cohen, L. S., Freeman, E. W., et al. (2016) · Archives of Sexual Behavior · doi:10.1007/s10508-016-0804-5 · Cited by 29
Huynh, H. (1998) · Journal of Educational and Behavioral Statistics · doi:10.2307/1165347 · Cited by 29
Qualitative or Quantitative Differences?
Yang, X., Shaftel, J., Glasnapp, D. R., & Poggio, J. P. (2005) · The Journal of Special Education · doi:10.1177/00224669050380040101 · Cited by 29
Lawton IADL scale in dementia: can item response theory make it more informative?
McGrory, S., Shenkin, S. D., Austin, E. J., & Starr, J. M. (2013) · Age and Ageing · doi:10.1093/ageing/aft173 · Cited by 29
Adaptive Measurement of Individual Change
Kang, G., & Weiss, D. J. (2008) · Zeitschrift für Psychologie · doi:10.1027/0044-3409.216.1.49 · Cited by 29
Assessing Essential Unidimensionality of Real Data
Nandakumar, R. (1993) · Applied Psychological Measurement · doi:10.1177/014662169301700108 · Cited by 29
Hambleton, R. K., & Pitoniak, M. J. (2002) · doi:10.1002/0471214426.pas0413 · Cited by 29
Self-citationPisa: What Makes the Difference? Explaining the Gap in Pisa Test Scores between Finland and GermanyPreprint
Ammermueller, A. (2004) · SSRN Electronic Journal · doi:10.2139/ssrn.555954 · Cited by 29
Observed-Score Equating as a Test Assembly Problem
van der Linden, W. J., & Luecht, R. M. (1998) · Psychometrika · doi:10.1007/bf02294862 · Cited by 28
Measurement Properties of Two Innovative Item Formats in a Computer-Based Test
Wan, L., & Henly, G. A. (2012) · Applied Measurement in Education · doi:10.1080/08957347.2012.635507 · Cited by 28
PE Metrics: Background, Testing Theory, and Methods
Zhu, W., Rink, J., Placek, J. H., Graber, K. C., Fox, C., Fisette, J. L., Dyson, B. P., Park, Y., Avery, M., Franck, M., et al. (2011) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2011.568363 · Cited by 28
Battisti, B. T., Hanegan, N. L., Sudweeks, R. R., & Cates, R. G. (2009) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-009-9189-4 · Cited by 28
Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items
Aybek, E. C., & Demirtaşlı, R. N. (2017) · International Journal of Research in Education and Science · doi:10.21890/ijres.327907 · Cited by 28
Analysis of Students Critical Thinking Skills Using Partial Credit Models (PCM) in Physics Learning
Asysyifa, D. S., Jumadi, J., Wilujeng, İ., & Kuswanto, H. (2019) · International Journal of Educational Research Review · doi:10.24331/ijere.518068 · Cited by 28
Wind, S. A., & Guo, W. (2019) · Educational and Psychological Measurement · doi:10.1177/0013164419834613 · Cited by 28
Item Response Theory and the Measurement of Motor Behavior
Safrit, M. J., Cohen, A. S., & Costa, M. G. (1989) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.1989.10607459 · Cited by 28
Comparing Simulated and Theoretical Sampling Distributions of the U3 Person-Fit Statistic
Emons, W. H. M., Meijer, R. R., & Sijtsma, K. (2002) · Applied Psychological Measurement · doi:10.1177/0146621602026001006 · Cited by 28
Computerized Adaptive Testing: What It is and How It Works.
Straetmans, G., & Eggen, T. J. H. M. (1998) · Educational Technology archive · Cited by 28
On Item Parameter Estimation in Certain Latent Trait Models
Pfanzagl, J. (1994) · Recent research in psychology · doi:10.1007/978-1-4612-4308-3_19 · Cited by 28
Lautenschiager, G. J., & Park, D.-G. (1988) · Applied Psychological Measurement · doi:10.1177/014662168801200404 · Cited by 28
The Comparison of Accuracy Scores on the Paper and Pencil Testing vs. Computer-Based Testing.
Retnawati, H. (2015) · The Turkish Online Journal of Educational Technology · Cited by 27
Tavakol, M., & Pinner, G. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-029208 · Cited by 27
Caycho‐Rodríguez, T., Vilca, L. W., Vivanco-Vidal, A., Saroli-Araníbar, D., Carbajal‐León, C., Gallegos, W. L. A., White, M., & Lee, S. A. (2021) · Trends in Psychology · doi:10.1007/s43076-021-00091-1 · Cited by 29
Designing and Implementing an Adaptive Online Examination System
Yağcı, M., & Ünal, M. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.01.711 · Cited by 27
On the Efficiency of IRT Models When Applied to Different Sampling Designs
Berger, M. P. F. (1991) · Applied Psychological Measurement · doi:10.1177/014662169101500310 · Cited by 27
Statistical Inference Based on Latent Ability Estimates
Hoijtink, H., & Boomsma, A. (1996) · Psychometrika · doi:10.1007/bf02294342 · Cited by 27
A NONPARAMETRIC ITEM ANALYSIS OF THE MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE—CHINESE VERSION
Sachs, J., Law, Y. K., Chan, C. K. K., & Rao, N. (2001) · PSYCHOLOGIA · doi:10.2117/psysoc.2001.197 · Cited by 27
MODEL DIAGNOSTICS FOR BAYESIAN NETWORKS
Sinharay, S. (2004) · ETS Research Report Series · doi:10.1002/j.2333-8504.2004.tb01944.x · Cited by 26
A Comparison of IRT Proficiency Estimation Methods Under Adaptive Multistage Testing
Kim, S., Moses, T. P., & Yoo, H. (2015) · Journal of Educational Measurement · doi:10.1111/jedm.12063 · Cited by 26
R Packages for Item Response Theory Analysis: Descriptions and Features
Choi, Y., & Asilkalkan, A. (2019) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366367.2019.1586404 · Cited by 26
Item Response Theory Observed-Score Kernel Equating
Andersson, B., & Wiberg, M. (2016) · Psychometrika · doi:10.1007/s11336-016-9528-7 · Cited by 26
An Item Response Model for Characterizing Test Compromise
Segall, D. O. (2002) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986027002163 · Cited by 26
Improving the measurement accuracy of the effort-reward imbalance scales
Tsutsumi, A., Iwata, N., Wakita, T., Kumagai, R., Noguchi, H., & Kawakami, N. (2008) · International Journal of Behavioral Medicine · doi:10.1080/10705500801929718 · Cited by 26
Measurement in psychology: A case of ignoramus et ignorabimus ? A rejoinder
Trendler, G. (2013) · Theory & Psychology · doi:10.1177/0959354313490451 · Cited by 28
Hol, A. M., Vorst, H. C. M., & Mellenbergh, G. J. (2007) · Applied Psychological Measurement · doi:10.1177/0146621606297314 · Cited by 26
Gopichandran, V., Wouters, E., & Chetlapalli, S. K. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-007305 · Cited by 26
Validating a scale that measures scientists’ self-efficacy for public engagement with science
Evia, J. R., Peterman, K., Cloyd, E. T., & Besley, J. C. (2017) · International Journal of Science Education Part B · doi:10.1080/21548455.2017.1377852 · Cited by 26
DOES NEIGHBORHOOD COLLECTIVE EFFICACY FOR FAMILIES CHANGE OVER TIME? THE BOSTON NEIGHBORHOOD SURVEY
Schmidt, N. M., Tchetgen, E. J. T., Ehntholt, A., Almeida, J., Nguyen, Q. C., Molnar, B. E., Azrael, D. R., & Osypuk, T. L. (2013) · Journal of Community Psychology · doi:10.1002/jcop.21594 · Cited by 26
Increasing the Precision of Subscale Scores by Using Out-of-Scale Information
Kahraman, N., & Kamata, A. (2004) · Applied Psychological Measurement · doi:10.1177/0146621604268736 · Cited by 26
Person-Fit and the Rasch Model, with an Application to Knowledge of logical Quantors
Molenaar, I. W., & Hoijtink, H. J. A. (1996) · Applied Measurement in Education · doi:10.1207/s15324818ame0901_4 · Cited by 26
Two Simulated Feasibility Studies in Computerised Adaptive Testing
Stocking, M. L. (1987) · Applied Psychology · doi:10.1111/j.1464-0597.1987.tb01191.x · Cited by 26
Item Response Theory Analysis of the Five Facet Mindfulness Questionnaire and Its Short Forms
Pelham III, W. E., Gonzalez, O., Metcalf, S. A., Whicker, C. L., Scherer, E. A., Witkiewitz, K., Marsch, L. A., & Mackinnon, D. P. (2019) · Mindfulness · doi:10.1007/s12671-019-01105-x · Cited by 25
Modifying biased teacher expectations in mathematics and German: A teacher intervention study
Neuenschwander, M. P., Mayland, C., Niederbacher, E., & Garrote, A. (2021) · Learning and Individual Differences · doi:10.1016/j.lindif.2021.101995 · Cited by 26
Modeling Local Item Dependence Due to Common Test Format With a Multidimensional Rasch Model
Baghaei, P., & Aryadoust, V. (2014) · International Journal of Testing · doi:10.1080/15305058.2014.941108 · Cited by 25
Curtailment and Stochastic Curtailment to Shorten the CES-D
Finkelman, M., Smits, N., Kim, W., & Riley, B. (2012) · Applied Psychological Measurement · doi:10.1177/0146621612451647 · Cited by 25
The Effect of Person Misfit on Classification Decisions
Hendrawan, I. W., Glas, C. A. W., & Meijer, R. R. (2004) · Applied Psychological Measurement · doi:10.1177/0146621604270902 · Cited by 25
Zampetakis, L. A. (2011) · Research on emotion in organizations · doi:10.1108/s1746-9791(2011)0000007016 · Cited by 30
A Cognitive Diagnosis Model for Continuous Response
Minchen, N., TORRE, J. D. L., & Liu, Y. (2017) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998617703060 · Cited by 25
Ha, L. A., & Yaneva, V. (2018) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w18-0548 · Cited by 25
Bayesian Model Selection Methods for Multilevel IRT Models: A Comparison of Five DIC‐Based Indices
Zhang, X., Tao, J., Chun, W., & Shi, N. (2019) · Journal of Educational Measurement · doi:10.1111/jedm.12197 · Cited by 25
The Relationships Between Parental Influence and Student Achievement in Seventh Grade Mathematics
Wang, J., Wildman, L., & Calhoun, G. (1996) · School Science and Mathematics · doi:10.1111/j.1949-8594.1996.tb15862.x · Cited by 25
Evaluating Item Fit for Multidimensional Item Response Models
Zhang, B., & Stone, C. A. (2007) · Educational and Psychological Measurement · doi:10.1177/0013164407301547 · Cited by 25
Dynamic paired-comparison scaling
Batchelder, W. H., Bershad, N. J., & Simpson, R. S. (1992) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(92)90036-7 · Cited by 25
A Similarity-Based Smoothing Approach to Nondimensional Item Analysis
Ramsay, J. O. (1995) · Psychometrika · doi:10.1007/bf02294378 · Cited by 25
Vrignaud, P. (2006) · Revue française de pédagogie · doi:10.4000/rfp.409 · Cited by 24
Data-based Decision Rules to Personalize Depression Follow-up
Lin, Y., Huang, S., Simon, G. E., & Liu, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-23326-1 · Cited by 24
Longitudinal invariance and information of the Depression, Anxiety, and Stress Scales
Anghel, E. (2020) · Journal of Clinical Psychology · doi:10.1002/jclp.22968 · Cited by 24
Simultaneously construct IRT-based parallel tests based on an adapted CLONALG algorithm
Chang, T., & Shiu, Y.-F. (2011) · Applied Intelligence · doi:10.1007/s10489-011-0308-x · Cited by 24
Modern Psychometric Methodology
Reid, C. A., Kolakowsky‐Hayner, S. A., Lewis, A. N., & Armstrong, A. J. (2007) · Rehabilitation Counseling Bulletin · doi:10.1177/00343552070500030501 · Cited by 24
Design Decisions in Developing Learning Trajectories–Based Assessments in Mathematics: A Case Study
Penuel, W. R., Confrey, J., Maloney, A., & Rupp, A. A. (2013) · Journal of the Learning Sciences · doi:10.1080/10508406.2013.866118 · Cited by 26
Effects of Ignoring Item Interaction on Item Parameter Estimation and Detection of Interacting Items
Chen, C.-T., & Wang, W. (2007) · Applied Psychological Measurement · doi:10.1177/0146621606297309 · Cited by 24
Estimation Methods for One‐Parameter Testlet Models
Jiao, H., Wang, S., & He, W. (2013) · Journal of Educational Measurement · doi:10.1111/jedm.12010 · Cited by 24
Bi-Factor MIRT Observed-Score Equating for Mixed-Format Tests
Lee, G., & Lee, W. (2016) · Applied Measurement in Education · doi:10.1080/08957347.2016.1171770 · Cited by 24
Evaluating Performance of Missing Data Imputation Methods in IRT Analyses
Kalkan, Ö. K., Kara, Y., & Kelecioğlu, H. (2018) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.430720 · Cited by 23
On the Conceptualization of Criminal Capital
Nguyen, H. (2019) · Journal of Research in Crime and Delinquency · doi:10.1177/0022427819869422 · Cited by 25
Incidental Versus Random Nuisance Parameters
Pfanzagl, J. (1993) · The Annals of Statistics · doi:10.1214/aos/1176349392 · Cited by 24
Lane, S., Stone, C. A., Ankenmann, R. D., & Liu, M. (1995) · Applied Measurement in Education · doi:10.1207/s15324818ame0804_3 · Cited by 24
TIME TO COMPLETION OF WEB‐BASED PHYSICS PROBLEMS WITH TUTORING
Warnakulasooriya, R., Palazzo, D. J., & Pritchard, D. E. (2007) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.2007.70-06 · Cited by 24
Shea, J. A., & Fortna, G. S. (2002) · Springer international handbooks of education · doi:10.1007/978-94-010-0462-6_4 · Cited by 24
Screening for Problematic Worry in Adults With a Single Item From the Penn State Worry Questionnaire
Schroder, H. S., Clark, D. A., & Moser, J. S. (2017) · Assessment · doi:10.1177/1073191117694453 · Cited by 23
Sbeglia, G. C., & Nehm, R. H. (2020) · Evolution Education and Outreach · doi:10.1186/s12052-020-00137-5 · Cited by 25
A Competence-Aware Curriculum for Visual Concepts Learning via Question Answering
Li, Q., Huang, S., Hong, Y., & Zhu, S. C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_9 · Cited by 25
Calderón, C., Beyle, C., Véliz‐García, Ó., & Bekios-Calfa, J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251137 · Cited by 23
Jump-Starting Item Parameters for Adaptive Language Tests
McCarthy, A. D., Yancey, K. P., LaFlair, G. T., Egbert, J., Liao, M., & Settles, B. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.67 · Cited by 27
Validation of Cognitive Structures: A Structural Equation Modeling Approach
Dimitrov, D. M., & Raykov, T. (2003) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3801_1 · Cited by 23
Cook, K. F., Roddey, T., O’Malley, K. J., & Gartsman, G. M. (2005) · Journal of Shoulder and Elbow Surgery · doi:10.1016/j.jse.2004.09.024 · Cited by 23
A Comparison of Approaches for Improving the Reliability of Objective Level Scores
Skorupski, W. P., & Carvajal, J. (2009) · Educational and Psychological Measurement · doi:10.1177/0013164409355694 · Cited by 23
A formative assessment model of critical thinking in mathematics learning in junior high school
Rosnawati, R., Kartowagiran, B., & Jailani, J. (2015) · REID (Research and Evaluation in Education) · doi:10.21831/reid.v1i2.6472 · Cited by 23
The cat came back: Evaluating arguments against psychological measurement
Markus, K. A., & Borsboom, D. (2011) · Theory & Psychology · doi:10.1177/0959354310381155 · Cited by 23
Supporting Assessment in Undergraduate Mathematics.
Steen, L. A. (2006) · Cited by 23
Development and Calibration of an Item Bank for PE Metrics Assessments: Standard 1
Zhu, W., Fox, C., Park, Y., Fisette, J. L., Dyson, B. P., Graber, K. C., Avery, M., Franck, M., Placek, J. H., Rink, J., et al. (2011) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2011.568366 · Cited by 23
Metadata
- Authors
- Hambleton, R. K., & Swaminathan, H.
- Venue
- Springer eBooks
- Publisher
- Springer Netherlands
- Published
- 1985
- OpenAlex
- W2505213109
- Cited by
- 1,777
- References
- 0
- Citations by year
- 2026: 30 · 2025: 38 · 2024: 29 · 2023: 46 · 2022: 50 · 2021: 80 · 2020: 66 · 2019: 87 · 2018: 69 · 2017: 85 · 2016: 65 · 2015: 70 · 2014: 79 · 2013: 64 · 2012: 66
- FWCI
- 20.09
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Education › Educational Assessment and Pedagogy
- Social Sciences › Psychology › Applied Psychology › Psychological Testing and Assessment
- Keywords
- item response theory, psychometrics, measurement theory, test theory
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