___

• Albano, A. D. (2013). Multilevel modeling of item position effects. Journal of Educational Measurement, 50(4), 408-426. https://doi.org/10.1111/jedm.12026
• Albano, A. D., McConnell, S. R., Lease, E. M., & Cai, L. (2020). Contextual interference effects in early assessment: Evaluating the psychometric benefits of item interleaving. Frontiers in Education, 5. https://doi.org/10.3389/feduc.2020.00133
• Asseburg, R., & Frey, A. (2013). Too hard, too easy, or just right? The relationship between effort or boredom and ability-difficulty fit. Psychological Test and Assessment Modeling, 55(1), 92-104. https://psycnet.apa.org/record/2013-18917-006
• Bates, D., Maechler, M., Bokler, B., & Walker, S. (2014). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48. https://doi.org/10.18637/jss.v067.i01
• Brennan, R. L. (1992). The context of context effects. Applied Measurement in Education, 5, 225-264. https://doi.org/10.1207/s15324818ame0503_4
• Breslow, N. E., & Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88(421), 9-25. https://doi.org/10.2307/2290687
• Bulut, O. (2021). eirm: Explanatory item response modeling for dichotomous and polytomous item responses (R package version 0.3.0) [Computer software]. https://doi.org/10.5281/zenodo.4556285
• Bulut, O., Quo, O., & Gierl, M. (2017). A structural equation modeling approach for examining position effects in large‑scale assessments. Large Scale in Assessments in Education, 5(8), 1-20. https://doi.org/10.1186/s40536-017-0042-x
• Christiansen, A., & Janssen, R. (2020). Item position effects in listening but not in reading in the European Survey of Language Competences. Educational Assessment, Evaluation and Accountability, 33(3), 49-69. https://doi.org/10.1007/s11092-020-09335-7
• Cook, L. L., & Petersen, N. S. (1987). Problems related to the use of conventional and item response theory equating methods in less than optimal circumstances. Applied Psychological Measurement, 11(1), 225-244. https://doi.org/10.1177/014662168701100302
• De Boeck, P., & Wilson, M. (2004). Explanatory item response models: A generalized linear and nonlinear approach. Springer.
• De Boeck, P., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuerlinckx, F., & Partchev, I. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software, 39(12), 1-28. https://doi.org/10.18637/jss.v039.i12
• Debeer, D., & Janssen, R. (2013). Modeling item-position effects within an IRT framework. Journal of Educational Measurement, 50(2), 164-185. https://doi.org/10.1111/jedm.12009
• Desjardins, C. D., & Bulut, O. (2018). Handbook of educational measurement and psychometrics using R. CRC Press.
• Fahrmeir, L., & Tutz, G. (2001). Multivariate statistical modeling based on generalized linear models (2nd ed.). Springer.
• Frey, A. & Bernhardt, R. (2012). On the importance of using balanced booklet designs in PISA. Psychological Test and Assessment Modeling, 54(4), 397-417. https://www.psychologie-aktuell.com/fileadmin/download/ptam/4-2012_20121224/05_Frey.pdf
• Frey, A., Hartig, J., & Rupp, A. (2009). An NCME instructional module on booklet designs in large-scale assessments of student achievement: Theory and practice. Educational Measurement: Issues and Practice, 28(3), 39-53. https://doi.org/10.1111/j.1745-3992.2009.00154.x
• Goff, M., & Ackerman, P. L. (1992). Personality-intelligence relations: Assessment of typical intellectual engagement. Journal of Educational Psychology, 84(4), 537-552. https://doi.org/10.1037/0022-0663.84.4.537
• Gonzalez, E., & Rutkowski, L. (2010). Principles of multiple matrix booklet designs and parameter recovery in large scale assessments. IERI Monograph Series: Issues and Methodologies in Large-Scale Assessments, 3, 125-156. https://www.ierinstitute.org/fileadmin/Documents/IERI_Monograph/IERI_Monograph_Volume_03_Chapter_6.pdf
• Guertin, W. H. (1954). The effect of instructions and item order on the arithmetic subtest of the Wechsler- Bellevue. Journal of Genetic Psychology, 85(1), 79-83. https://doi.org/10.1080/00221325.1954.10532863
• Hambleton, R. K., & Traub, R. E. (1974). The effects of item order in test performance and stress. Journal of Experimental Education, 43(1), 40-46. http://www.jstor.org/stable/20150989
• Hahne, J. (2008). Analyzing position effects within reasoning items using the LLTM for structurally incomplete data. Psychology Science Quarterly, 50(3), 379-390. https://www.psychologie-aktuell.com/fileadmin/download/PschologyScience/3-2008/05_Hahne.pdf
• Hartig, J., & Buchholz, J. (2012). A multilevel item response model for item position effects and individual persistence. Psychological Test and Assessment Modeling, 54(4), 418-431. https://www.proquest.com/scholarly-journals/multilevel-item-response-model-positioneffects/docview/1355923397
• Hecht, M., Weirich, S., Siegle, T., & Frey, A. (2015). Effects of design properties on parameter estimation in large-scale assessments. Educational and Psychological Measurement, 75(6), 1021-1044. https://doi.org/10.1177/0013164415573311
• Hohensinn, C., Kubinger, K., Reif, M., Schleicher, E., & Khorramdel, L. (2011). Analysing item position effects due to test booklet design within large-scale assessment. Educational Research and Evaluation, 17(6), 497-509. https://doi.org/10.1080/13803611.2011.632668
• Janssen, R., Schepers, J., & Peres, D. (2004). Models with item and item group predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models (pp. 189-212). Springer. https://doi.org/10.1007/978-1-4757-3990-9_6
• Kingston, N. M., & Dorans, N. J. (1982). The effect of the position of an item within a test on item responding behavior: An analysis based on item response theory (GRE Board Professional Report GREB No. 79-12bP). Educational Testing Service. https://doi.org/10.1002/j.2333-8504.1982.tb01308.x
• Kingston, N. M., & Dorans, N. J. (1984). Item location effects and their implications for IRT equating and adaptive testing. Applied Psychological Measurement, 8(2), 147-154. https://doi.org/10.1177/014662168400800202
• Kolen, M. J., & Brennan, R. L. (2004). Testing equating, scaling, and linking: Methods and practice. Springer.
• Kolen, M. J., & Harris, D. (1990). Comparison of item pre-equating and random groups equating using IRT and equipercentile methods. Journal of Educational Measurement, 27(1), 27-29. https://doi.org/10.1111/j.1745-3984.1990.tb00732.x
• Le, L. T. (2007, July). Effects of item positions on their difficulty and discrimination: A study in PISA Science data across test language and countries. Paper presented at the 72nd Annual Meeting of the Psychometric Society, Tokyo. https://research.acer.edu.au/pisa/2/
• Leary, L. F., & Dorans, N. J. (1985). Implications for altering the context in which test items appear: A historical perspective on an immediate concern. Review of Educational Research, 55(3), 387-413.
• Lord, F. M. (1980). Applications of item response theory to practical testing problems. Erlbaum.
• Lord, F. N., & Novick, M. R. (1968). Statistical theories of mental test scores. Addison-Wesley.
• MacNicol, K. (1956). Effects of varying order of item difficulty in an unspeeded verbal test (Unpublished manuscript). Educational Testing Service.
• McCoach, D. B., & Black, A. C. (2008). Evaluation of model fit and adequacy. In A. A. O’Connell & D. B. McCoach (Ed.), Multilevel modeling of educational data (pp. 245-272). Information Age Publishing, Inc.
• McCullagh, P., & NeIder, J. A. (1989). Generalized linear models (2nd ed.). Chapman & Hall.
• McCulloch, C. E., & Searle, S. R. (2001). Generalized, linear, and mixed models. Wiley.
• Meyers, J. L., Miller, G. E., & Way, W. D. (2009). Item position and item difficulty change in an IRT- based common item equating design. Applied Measurement in Education, 22(1), 38-60. https://doi.org/10.1080/08957340802558342
• Mollenkopf, W. G. (1950). An experimental study of the effects on item-analysis data of changing item placement and test time limit. Psychometrika, 15(3), 291-315. https://doi.org/10.1007/BF02289044
• Nagy, G., Nagengast, B., Frey, A., Becker, M., & Rose, N. (2018). A multilevel study of position effects in PISA achievement tests: student- and school-level predictors in the German tracked school system. Assessment in Education: Principles, Policy & Practice, 26(4), 422-443. https://doi.org/10.1080/0969594X.2018.1449100
• Organisation for Economic Co-operation and Development. (2009). PISA 2006 technical report. Organisation for Economic Co-operation and Development. https://www.oecd.org/pisa/data/42025182.pdf
• Organisation for Economic Co-operation and Development. (2012). PISA 2009 technical report. Organisation for Economic Co-operation and Development. http://dx.doi.org/10.1787/9789264167872-en
• Organisation for Economic Co-operation and Development. (2014). PISA 2012 technical report. Organisation for Economic Co-operation and Development. https://www.oecd.org/pisa/pisaproducts/PISA-2012-technical-report-final.pdf
• Organisation for Economic Co-operation and Development. (2017). PISA 2015 technical report. Organisation for Economic Co-operation and Development. https://www.oecd.org/pisa/data/2015-technical-report/
• Okumura, T. (2014). Empirical differences in omission tendency and reading ability in PISA: An application of tree-based item response models. Educational and Psychological Measurement, 74(4), 611-626. https://doi.org/10.1177/0013164413516976
• R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
• Raven, J. C., Raven, J., & Court, J. H. (1997). Raven’s progressive matrices and vocabulary scales. J. C. Raven Ltd.
• Rose, N., Davier, M., & Xu, X. (2010). Modeling nonignorable missing data with item response theory (IRT) (Report No. RR-10-11). Educational Testing Service.
• Rose, N., Nagy, G., Nagengast, B., Frey, A., & Becker, M. (2019). Modeling multiple item context effects with generalized linear mixed models. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.00248
• Sax, G., & Carr, A. (1962). An investigation of response sets on altered parallel forms. Educational and Psychological Measurement, 22(2), 371-376. https://doi.org/10.1177/001316446202200210
• Schweizer, K., Schreiner, M., & Gold, A. (2009). The confirmatory investigation of APM items with loadings as a function of the position and easiness of items: A two-dimensional model of APM. Psychology Science Quarterly, 51(1), 47-64. https://psycnet.apa.org/record/2009-06359-003
• Smouse, A. D., & Munz, D. C. (1968). The effects of anxiety and item difficulty sequence on achievement testing scores. Journal of Psychology, 68(2), 181-184. https://doi.org/10.1080/00223980.1968.10543421
• Trendtel, M., Robitzsch, A. (2018). Modeling item position effects with a Bayesian item response model applied to PISA 2009-2015 data. Psychological Test and Assessment Modeling, 60(2), 241-263. https://www.psychologie-aktuell.com/fileadmin/download/ptam/2-2018_20180627/06_PTAM-2-2018_Trendtel_v2.pdf
• Tuerlinckx, F., & De Boeck, P. (2004). Models for residual dependencies. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models (pp. 289-316). Springer.
• Wainer, H., & Kiely, G. L. (1987). Item clusters and computerized adaptive testing: A case for testlets. Journal of Educational Measurement, 24(3), 185-201. http://www.jstor.org/stable/1434630
• Weirich, S., Hecht, M., & Böhme, K. (2014). Modeling item position effects using generalized linear mixed models. Applied Psychological Measurement, 38(7), 535-548. https://doi.org/10.1177/0146621614534955
• Weirich, S., Hecht, M., Penk, C., Roppelt, A., & Böhme, K. (2016). Item position effects are moderated by changes in test-taking effort. Applied Psychological Measurement, 41(2), 115-129. https://doi.org/10.1177/0146621616676791
• Whitely, E., & Dawis, R. (1976). The influence of test context on item difficulty. Educational and Psychological Measurement, 36(2), 329-337. https://doi.org/10.1177/001316447603600211
• Wise, L. L., Chia, W. J., & Park, R. (1989, 27-31 March). Item position effects for test of word knowledge and arithmetic reasoning. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
• Wise, S. L., & DeMars, C. E. (2005). Low examinee effort in low-stakes assessment: Problems and potential solutions. Educational Assessment, 10(1), 1-17. https://doi.org/10.1207/s15326977ea1001_1
• Wise, S. L., & Kong, X. (2005). Response time effort: A new measure of examinee motivation in computer-based tests. Applied Measurement in Education, 18(2), 163-183. https://doi.org/10.1207/s15324818ame1802_2
• Wu, Q., Debeer, D. Buchholz, J., Hartig, J., & Janssen, R. (2019). Predictors of individual performance changes related to item positions in PISA assessments. Large Scale Assessment in Education, 7(5), 1-20. https://doi.org/10.1186/s40536-019-0073-6
• Yen, W. M. (1980). The extent, causes and importance of context effects on item parameters for two latent trait models. Journal of Educational Measurement, 17(4), 297-311. http://www.jstor.org/stable/1434871
• Zwick, R. (1991). Effects of item order and context on estimation of NAEP reading proficiency. Educational Measurement: Issues and Practice, 10(3), 10-16. https://doi.org/10.1111/j.1745-3992.1991.tb00198.x