{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T18:53:01Z","timestamp":1791399181271,"version":"4.3.3"},"reference-count":29,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Educational and Psychological Measurement"],"abstract":"<jats:p>\n                    Computerized adaptive testing (CAT) aims to optimize measurement by tailoring item administration to individual examinees. The efficiency and precision of a CAT heavily depend on the choice of ability (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mi>\u03b8<\/mml:mi>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ) estimator and the termination criterion (stopping rule). Prior research suggests these components interact, but comprehensive evaluations across varying item bank characteristics remain limited. This simulation study investigated the interactive effects of four\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mi>\u03b8<\/mml:mi>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    estimators (maximum likelihood [MLE], weighted likelihood [WLE], maximum a posteriori [MAP], and expected a posteriori [EAP]) and four termination criteria (fixed-length, standard error of measurement [SEM], minimum information [MI], and change-in-estimate [\u0394\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mi>\u03b8<\/mml:mi>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ]) on measurement bias, precision (RMSE), and test length. These combinations were evaluated across low- (100-item) and high-information (500-item) item banks with both flat and peaked information distributions using the three-parameter logistic model. The results demonstrated that the optimal CAT configuration is contingent on item bank size and shape. Across all conditions, WLE emerged as the most robust estimator, effectively neutralizing the boundary estimation issues of MLE and the shrinkage bias characteristic of Bayesian estimators. In high-information banks, the SEM and fixed-length rules yielded the lowest conditional RMSE and bias regardless of bank shape. However, in low-information peaked banks, the strict SEM rule frequently failed to reach precision targets at the \u03b8 extremes, resulting in inefficient, maximum-length tests. Under these sparse conditions, the \u0394\u03b8 rule paired with WLE provided a superior balance of accuracy and efficiency by halting administration when precision gains stagnated. Conversely, the MI rule consistently exhibited the highest bias and RMSE. These findings underscore that optimal CAT design is not a one-size-fits-all solution. For high-quality banks, WLE paired with an SEM or fixed-length rule is recommended. For lower-quality banks, practitioners should adopt a\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mspace width=\"0.25em\"\/>\n                          <mml:mo>\u0394<\/mml:mo>\n                          <mml:mi>\u03b8<\/mml:mi>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    rule or a hybrid SEM approach to prevent inefficient test elongation.\n                  <\/jats:p>","DOI":"10.1177\/00131644261453945","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:17:16Z","timestamp":1781191036000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interactions Between Termination Criteria and Ability Estimators in Computerized Adaptive Testing"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6738-2973","authenticated-orcid":false,"given":"Xinyu","family":"Liu","sequence":"first","affiliation":[{"name":"University of Minnesota, Minneapolis, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J.","family":"Weiss","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"crossref","unstructured":"Babcock B. Weiss D. (2012). Termination criteria in computerized adaptive tests: Do variable-length CATs provide efficient and effective measurement? Journal of Computerized Adaptive Testing 1 1\u201318. https:\/\/www.jcatpub.net\/index.php\/jcat\/article\/view\/16","DOI":"10.7333\/1212-0101001"},{"key":"e_1_3_3_3_1","doi-asserted-by":"crossref","unstructured":"Baker F. B. Kim S.-H. (Eds.). (2004 July 19). Item response theory: Parameter estimation techniques Second edition (2nd ed.). CRC Press. https:\/\/doi.org\/10.1201\/9781482276725","DOI":"10.1201\/9781482276725"},{"key":"e_1_3_3_4_1","doi-asserted-by":"crossref","unstructured":"Bock R. D. (1983). The discrete Bayesian. In Wainer H. Messick S. (Eds.) Principals of modern psychological measurement (pp. 103\u2013115). Routledge. https:\/\/doi.org\/10.4324\/9780203056653","DOI":"10.4324\/9780203056653"},{"key":"e_1_3_3_5_1","doi-asserted-by":"crossref","unstructured":"Bock R. D. Aitkin M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika 46(4) 443\u2013459. https:\/\/doi.org\/10.1007\/BF02293801","DOI":"10.1007\/BF02293801"},{"key":"e_1_3_3_6_1","doi-asserted-by":"crossref","unstructured":"Choi S. W. Grady M. W. Dodd B. G. (2011). A new stopping rule for computerized adaptive testing. Educational and Psychological Measurement 71(1) 37\u201353. https:\/\/doi.org\/10.1177\/0013164410387338","DOI":"10.1177\/0013164410387338"},{"key":"e_1_3_3_7_1","doi-asserted-by":"crossref","unstructured":"Dwahdh L. Alshraifin N. (2025). The impact of computerized adaptive test termination rules on accuracy across different ability estimation methods. Eurasia Journal of Mathematics Science and Technology Education 21(1) em2571. https:\/\/doi.org\/10.29333\/ejmste\/15897","DOI":"10.29333\/ejmste\/15897"},{"key":"e_1_3_3_8_1","unstructured":"Gialluca K. A. Weiss D. J. (1979). Efficiency of an adaptive inter-subtest branching strategy in the measurement of classroom achievement (RR796). Defense Technical Information Center. https:\/\/apps.dtic.mil\/sti\/html\/tr\/ADA080956\/"},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"Hambleton R. K. Swaminathan H. (1985). Item response theory: Principles and applications. Springer. https:\/\/doi.org\/10.1177\/014662168500900315","DOI":"10.1007\/978-94-017-1988-9"},{"key":"e_1_3_3_10_1","doi-asserted-by":"crossref","unstructured":"Hart D. L. Mioduski J. E. Stratford P. W. (2005). Simulated computerized adaptive tests for measuring functional status were efficient with good discriminant validity in patients with hip knee or foot\/ankle impairments. Journal of Clinical Epidemiology 58(6) 629\u2013638. https:\/\/doi.org\/10.1016\/j.jclinepi.2004.12.004","DOI":"10.1016\/j.jclinepi.2004.12.004"},{"key":"e_1_3_3_11_1","doi-asserted-by":"crossref","unstructured":"Hart D. L. Mioduski J. E. Werneke M. W. Stratford P. W. (2006). Simulated computerized adaptive test for patients with lumbar spine impairments was efficient and produced valid measures of function. Journal of Clinical Epidemiology 59(9) 947\u2013956. https:\/\/doi.org\/10.1016\/j.jclinepi.2005.10.017","DOI":"10.1016\/j.jclinepi.2005.10.017"},{"key":"e_1_3_3_12_1","doi-asserted-by":"crossref","unstructured":"Lee C. Han K. T. (2024). Evaluating effectiveness of standard error of score estimation as a CAT termination criterion. Journal of Computerized Adaptive Testing 11(2) 13\u201329. https:\/\/doi.org\/10.7333\/2410-1102013","DOI":"10.7333\/2410-1102013"},{"key":"e_1_3_3_13_1","doi-asserted-by":"crossref","unstructured":"Lord F. M. (1983). Unbiased estimators of ability parameters of their variance and of their parallel-forms reliability. Psychometrika 48(2) 233\u2013245. https:\/\/doi.org\/10.1007\/BF02294018","DOI":"10.1007\/BF02294018"},{"key":"e_1_3_3_14_1","unstructured":"Maurelli V. A. Weiss D. J. (1981 November). Factors influencing the psychometric characteristics of an adaptive testing strategy for test batteries. https:\/\/eric.ed.gov\/?id=ED212676"},{"key":"e_1_3_3_15_1","doi-asserted-by":"crossref","unstructured":"Meijer R. R. Nering M. L. (1999). Computerized adaptive testing: Overview and introduction. Applied Psychological Measurement 23(3) 187\u2013194. https:\/\/doi.org\/10.1177\/01466219922031310","DOI":"10.1177\/01466219922031310"},{"key":"e_1_3_3_16_1","doi-asserted-by":"crossref","unstructured":"Ravand H. (2015). Assessing testlet effect impact differential testlet and item functioning using cross-classified multilevel measurement modeling. Sage Open 5(2) 1\u20139. https:\/\/doi.org\/10.1177\/2158244015585607","DOI":"10.1177\/2158244015585607"},{"key":"e_1_3_3_17_1","first-page":"1","article-title":"Estimation of latent ability using a response pattern of graded scores","author":"Samejima F.","year":"1969","unstructured":"Samejima F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika, 34(S1), 1\u201397.","journal-title":"Psychometrika, 34"},{"key":"e_1_3_3_18_1","doi-asserted-by":"crossref","unstructured":"Stafford R. E. Runyon C. R. Casabianca J. M. Dodd B. G. (2019). Comparing computer adaptive testing stopping rules under the generalized partial-credit model. Behavior Research Methods 51(3) 1305\u20131320. https:\/\/doi.org\/10.3758\/s13428-018-1068-x","DOI":"10.3758\/s13428-018-1068-x"},{"key":"e_1_3_3_19_1","doi-asserted-by":"crossref","unstructured":"Tai M. H. DeWeese J. N. Weiss D. (2025). Stochastic curtailment: A new approach to improve efficiency of variable-length computerized adaptive tests. Journal of Computerized Adaptive Testing 12(4) 164\u2013235. https:\/\/doi.org\/10.7333\/2508-1204165","DOI":"10.7333\/2508-1204165"},{"key":"e_1_3_3_20_1","doi-asserted-by":"crossref","unstructured":"Wang C. Weiss D. J. Shang Z. (2019). Variable-length stopping rules for multidimensional computerized adaptive testing. Psychometrika 84(3) 749\u2013771. https:\/\/doi.org\/10.1007\/s11336-018-9644-7","DOI":"10.1007\/s11336-018-9644-7"},{"key":"e_1_3_3_21_1","doi-asserted-by":"crossref","unstructured":"Wang S. Wang T. (2001). Precision of Warm\u2019s weighted likelihood estimates for a polytomous model in computerized adaptive testing. Applied Psychological Measurement 25(4) 317\u2013331. https:\/\/doi.org\/10.1177\/01466210122032163","DOI":"10.1177\/01466210122032163"},{"key":"e_1_3_3_22_1","doi-asserted-by":"crossref","unstructured":"Wang T. Hanson B. A. Lau C.-M. A. (1999). Reducing bias in CAT trait estimation: A comparison of approaches. Applied Psychological Measurement 23(3) 263\u2013278. https:\/\/doi.org\/10.1177\/01466219922031383","DOI":"10.1177\/01466219922031383"},{"key":"e_1_3_3_23_1","doi-asserted-by":"crossref","unstructured":"Wang T. Vispoel W. P. (1998). Properties of ability estimation methods in computerized adaptive testing. Journal of Educational Measurement 35(2) 109\u2013135. https:\/\/doi.org\/10.1111\/j.1745-3984.1998.tb00530.x","DOI":"10.1111\/j.1745-3984.1998.tb00530.x"},{"key":"e_1_3_3_24_1","doi-asserted-by":"crossref","unstructured":"Ware J. E. Gandek B. Sinclair S. J. Bjorner J. B. (2005). Item response theory and computerized adaptive testing: Implications for outcomes measurement in rehabilitation. Rehabilitation Psychology 50(1) 71\u201378. https:\/\/doi.org\/10.1037\/0090-5550.50.1.71","DOI":"10.1037\/0090-5550.50.1.71"},{"key":"e_1_3_3_25_1","doi-asserted-by":"crossref","unstructured":"Warm T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika 54(3) 427\u2013450. https:\/\/doi.org\/10.1007\/BF02294627","DOI":"10.1007\/BF02294627"},{"key":"e_1_3_3_26_1","doi-asserted-by":"crossref","unstructured":"Weiss D. J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement 6(4) 473\u2013492. https:\/\/doi.org\/10.1177\/014662168200600408","DOI":"10.1177\/014662168200600408"},{"key":"e_1_3_3_27_1","doi-asserted-by":"crossref","unstructured":"Weiss D. J. (2014). New horizons in testing: Latent trait test theory and computerized adaptive testing. Elsevier. https:\/\/doi.org\/10.1016\/C2009-0-03014-1","DOI":"10.1016\/C2009-0-03014-1"},{"key":"e_1_3_3_28_1","doi-asserted-by":"crossref","unstructured":"Weiss D. J. Kingsbury G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement 21(4) 361\u2013375. https:\/\/doi.org\/10.1111\/j.1745-3984.1984.tb01040.x","DOI":"10.1111\/j.1745-3984.1984.tb01040.x"},{"key":"e_1_3_3_29_1","unstructured":"Weiss D. J. \u015eahin A. (2024). Computerized adaptive testing: From concept to implementation. The Guilford Press. https:\/\/www.guilford.com\/books\/Computerized-Adaptive-Testing\/Weiss-Sahin\/9781462554515"},{"key":"e_1_3_3_30_1","doi-asserted-by":"crossref","unstructured":"Yi Q. Wang T. Ban J.-C. (2001). Effects of scale transformation and test-termination rule on the precision of ability estimation in computerized adaptive testing. Journal of Educational Measurement 38(3) 267\u2013292. https:\/\/doi.org\/10.1111\/j.1745-3984.2001.tb01127.x","DOI":"10.1111\/j.1745-3984.2001.tb01127.x"}],"container-title":["Educational and Psychological Measurement"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00131644261453945","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00131644261453945","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00131644261453945","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T18:20:00Z","timestamp":1791397200000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00131644261453945"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":29,"alternative-id":["10.1177\/00131644261453945"],"URL":"https:\/\/doi.org\/10.1177\/00131644261453945","relation":{},"ISSN":["0013-1644","1552-3888"],"issn-type":[{"value":"0013-1644","type":"print"},{"value":"1552-3888","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]},"article-number":"00131644261453945"}}