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Books on the topic 'Item response theory – Mathematical models'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

L, Nering Michael, ed. Polytomous item response theory models. Sage Publications, 2006.

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2

Ostini, Remo. Polytomous item response theory models. Sage Publications, 2005.

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3

Handbook of item response theory. CRC Press, 2015.

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4

K, Hambleton Ronald, ed. Handbook of modern item response theory. Springer, 1997.

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5

The theory and practice of item response theory. Guildord Press, 2009.

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6

Ackerman, Terry A. The robustness of LOGIST and BILOG IRT estimation programs to violations of local independence. American College Testing Program, 1988.

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7

Woodruff, David. Estimation of item response models using the EM algorithm for finite mixtures. American College Testing Program, 1996.

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8

Zeng, Lingjia. An IRT scale transformation method for parameters calibrated from multiple samples of subjects. American College Testing Program, 1996.

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9

Russell, Waugh, ed. Applications of Rasch measurement in education. Nova Science Publishers, 2009.

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10

Ban, Jae-Chun. Data sparseness and online pretest item calibration/scaling methods in CAT. ACT, Inc., 2002.

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11

Ban, Jae-Chun. Data sparseness and online pretest item calibration/scaling methods in CAT. ACT, Inc., 2002.

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12

Ostini, Remo, and Michael Nering. Polytomous Item Response Theory Models. SAGE Publications, Inc., 2006. http://dx.doi.org/10.4135/9781412985413.

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13

Langeheine, Rolf, Wilhelm F. Kempf, and Jürgen Rost. Item-Response-Modelle in der sozialwissenschaftlichen Forschung. Regener, 2012.

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14

Woodruff, David J. Estimation of item response models using the EM algorithm for finite mixtures. American College Testing Program, 1996.

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15

Hanson, Bradley A. Application of a polynomial loglinear model to assessing differential item functioning for common items in the common-item equating design. ACT, Inc., 1997.

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16

Tang, K. Linda. Polytomous item response theory (IRT) models and their applications in large-scale testing programs: Review of literature. Educational Testing Service, 1996.

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17

Reckase, Mark D. Models for multidimensional tests and hierarchically structured training materials: Final report. American College Testing Program, Assessment Programs Area, Test Development Division, 1985.

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18

Bertrand, Richard. Mode les de mesure: L'apport de la the orie des re ponses aux items. Presses de l'Universite du Que bec, 2004.

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19

Dow, James. Trading, communication and the response of price to new information. National Bureau of Economic Research, 1991.

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20

1922-, Williams Carl D., ed. Confirmation learning theory. P. Lang, 1989.

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21

Almond, Russell G. Graphical models and computerized adaptive testing. Educational Testing Service, 1998.

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22

Fernando, Alvarez. On the sluggish response of prices to money in an inventory-theoretic model of money demand. National Bureau of Economic Research, 2003.

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23

Assessment and evaluation of developmental learning: Qualitative individual assessment and evaluation models. Praeger, 1998.

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24

L, Nering Michael, and Ostini Remo, eds. Handbook of polytomous item response theory models. Routledge, 2010.

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25

Falenchuk, Olesya. A study of unidimensional IRT models for items scored in multiple ordered response catagories. 2006.

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26

Buhr, Dianne C. Variability in the estimation of item option characteristic curves for the multiple-category scoring model. 1989.

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27

Ark, L. Andries van der, Croon Marcel A, and Sijtsma K. 1955-, eds. New developments in categorical data analysis for the social and behavioral sciences. Lawrence Erlbaum, 2005.

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28

New Developments in Categorical Data Analysis for the Social & Behavioral Science (Quantitative Methodology Series) (Quantitative Methodology Series). Lawrence Erlbaum, 2004.

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29

L. Andries van der Ark, Klaas Sijtsma, and Marcel A. Croon. New Developments in Categorical Data Analysis for the Social and Behavioral Sciences. Taylor & Francis Group, 2013.

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30

Nering, Michael. Handbook of Polytomous Item Response Theory Models. Routledge, 2011. http://dx.doi.org/10.4324/9780203861264.

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31

Gamerman, Dani, Tufi M. Soares, and Flávio Gonçalves. Bayesian analysis in item response theory applied to a large-scale educational assessment. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.22.

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Abstract:

This article discusses the use of a Bayesian model that incorporates differential item functioning (DIF) in analysing whether cultural differences may affect the performance of students from different countries in the various test items which make up the OECD’s Programme for International Student Assessment (PISA) test of mathematics ability. The PISA tests in mathematics and other subjects are used to compare the educational attainment of fifteen-year old students in different countries. The article first provides a background on PISA, DIF and item response theory (IRT) before describing a hierarchical three-parameter logistic model for the probability of a correct response on an individual item to determine the extent of DIF remaining in the mathematics test of 2003. The results of Bayesian analysis illustrate the importance of appropriately accounting for all sources of heterogeneity present in educational testing and highlight the advantages of the Bayesian paradigm when applied to large-scale educational assessment.

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32

Carlson, James E. Introduction to Item Response Theory Models and Applications. Routledge, 2020. http://dx.doi.org/10.4324/9781003035886.

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33

A, Spray Judith, American College Testing Program, and United States. Office of Naval Research. Cognitive Science Research Program, eds. Comparison of two logistic multidimensional item response theory models. American College Testing Program, 1990.

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34

Mooney, Richard Francis. The effect of range restriction on invariance in item response models. 1987.

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35

Madera, Ebby Kadinya. Application of the graded response model to the assessment of student attitudes. 2003.

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36

Sijtsma, Klaas. Measurement Models for Psychological Attributes: Classical Test Theory, Factor Analysis, Item Response Theory, and Latent Class Models. Taylor & Francis Group, 2020.

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37

Sijtsma, Klaas. Measurement Models for Psychological Attributes: Classical Test Theory, Factor Analysis, Item Response Theory, and Latent Class Models. Taylor & Francis Group, 2020.

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38

Sijtsma, Klaas. Measurement Models for Psychological Attributes: Classical Test Theory, Factor Analysis, Item Response Theory, and Latent Class Models. Taylor & Francis Group, 2020.

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39

Sijtsma, Klaas. Measurement Models for Psychological Attributes: Classical Test Theory, Factor Analysis, Item Response Theory, and Latent Class Models. Taylor & Francis Group, 2020.

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40

Robustness of unidimensional latent trait models when applied to multidimensional data. 1989.

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41

Robustness of unidimensional latent trait models when applied to multidimensional data. 1989.

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42

Robustness of unidimensional latent trait models when applied to multidimensional data. 1991.

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43

Robustness of unidimensional latent trait models when applied to multidimensional data. 1991.

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44

Phase Response Curves In Neuroscience Theory Experiment And Analysis. Springer, 2011.

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45

(Editor), Paul De Boeck, and Mark Wilson (Editor), eds. Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach (Statistics for Social and Behavioral Sciences). Springer, 2004.

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46

The robustness of two model-data fit procedures to violations of the local independence assumption in item response theory. 1988.

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47

The robustness of two model-data fit procedures to violations of the local independence assumption in item response theory. 1988.

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48

The robustness of two model-data fit procedures to violations of the local independence assumption in item response theory. 1987.

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49

Mesbah, Mounir, Karl Bang Christensen, and Svend Kreiner. Rasch Models in Health. Wiley & Sons, Incorporated, John, 2013.

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50

Rasch Models In Health. Wiley-Iste, 2012.

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