TY - CHAP

T1 - Introductory Analysis of the Rasch Model

T2 - Smart Innovation, Systems and Technologies

AU - Storey, I.

N1 - Export Date: 12 July 2022
Correspondence Address: Storey, I.; Lecturer Torrens UniversityAustralia; email: ian.storey@icloud.com
References: Andrich, D., Controversy and the rasch model: A characteristic of incompatible paradigms? (2004) Medical Care, 42 (1), pp. I7-I16; Bond, T.G., Fox, C.M., (2001) Applying the Rasch Model: Fundamental Measurement in the Human Sciences, , Routledge; Hatzinger, R. (2010). Rasch models and the R package eRM. https://eeecon.uibk.ac.at/psychoco/2010/slides/Hatzinger.pdf, October 9, 2018; Linacre, J. M. (2001). Rasch model estimation: Calculating calibrations and mean-squares with JMLE. Linacre, 2001. https://www.youtube.com/watch?v=LvE8npeSjZ0. Jun 28, 2020; Linacre, J. M. (2020). Fit diagnosis: infit outfit mean-square standardized, Winsteps(R). https://www.winsteps.com/winman/misfitdiagnosis.htm. June 22, 2020; Magis, D., Yan, D., von Davier, A.A., (2017) Computerized Adaptive and Multistage Testing with R: Using Packages Catr and Mstr, , Springer Publishing Company; Nuzzo, R., Scientific method: Statistical errors (2014) Nature, 506 (7487), p. 150; Smith, A.B., Rush, R., Fallowfield, L.J., Velikova, G., Sharpe, M., Rasch fit statistics and sample size considerations for polytomous data (2008) BMC Medical Research Methodology, 8 (33), p. 33; Walker, A.A., Jennings, J.K., Engelhard, G., Using person response functions to investigate areas of person misfit related to item characteristics (2018) Educational Assessment, 23 (1), pp. 47-68; Wikipedia. (2021). Rasch model, Wikipedia. https://en.wikipedia.org/wiki/Rasch_model, Jul 2, 2021; Wilson, E.B., Hilferty, M.M., The distribution of Chi-square (1931) Proceedings of the National Academy of Sciences of the United States of America, 17 (12), pp. 684-688; Wright, B., Panchapakesan, N., A procedure for sample-free item analysis (1969) Educational and Psychological Measurement, 29 (1), pp. 23-48; Wu, M., Adams, R., Applying the Rasch model to psycho-social measurement: A practical approach (2007) Educational Measurement Solutions

PY - 2022

Y1 - 2022

N2 - The author’s encounter with the Rasch model came after a long experience teaching traditional statistical approaches, then being called in to advise a Ph.D. student on test items used for developing tests with a ‘new’ methodology: the Rasch technique. This technique develops efficient psychometric tests that produce interval measures. This is an astonishing feat, and it took the author some time to recognize that this was solidly based on probability theory, particularly regression. This experience, plus becoming involved in the Ph.D. supervision, led to an investigation of the theory behind the Rasch model, its ‘philosophy’, as well as the process of calibration, particularly the use of fitness measures. This kind of introduction to Rasch is certainly not rare. Furthermore, statistics is traditionally a hurdle for a large cohort of students, so perhaps the explanations given here benefit also from the teachers’ perspective, even if potentially suffering because of it. The text therefore relates elements from probability theory. It also explains basic elements of psychometric testing, which is rarely covered in basic statistical texts. This text also provides a critique of significance and sample size as it relates to Rasch and, to some degree, to regression techniques in general. This subtle matter is perhaps appreciated by many, but it is briefly analysed here. © 2022, Springer Nature Switzerland AG.

AB - The author’s encounter with the Rasch model came after a long experience teaching traditional statistical approaches, then being called in to advise a Ph.D. student on test items used for developing tests with a ‘new’ methodology: the Rasch technique. This technique develops efficient psychometric tests that produce interval measures. This is an astonishing feat, and it took the author some time to recognize that this was solidly based on probability theory, particularly regression. This experience, plus becoming involved in the Ph.D. supervision, led to an investigation of the theory behind the Rasch model, its ‘philosophy’, as well as the process of calibration, particularly the use of fitness measures. This kind of introduction to Rasch is certainly not rare. Furthermore, statistics is traditionally a hurdle for a large cohort of students, so perhaps the explanations given here benefit also from the teachers’ perspective, even if potentially suffering because of it. The text therefore relates elements from probability theory. It also explains basic elements of psychometric testing, which is rarely covered in basic statistical texts. This text also provides a critique of significance and sample size as it relates to Rasch and, to some degree, to regression techniques in general. This subtle matter is perhaps appreciated by many, but it is briefly analysed here. © 2022, Springer Nature Switzerland AG.

KW - Education computing

KW - Students

KW - Basic-elements

KW - Fitness measures

KW - Probability theory

KW - Psychometric test

KW - Psychometric testing

KW - Rasch modeling

KW - Regression techniques

KW - Sample sizes

KW - Statistical approach

KW - Teachers'

KW - Probability

U2 - 10.1007/978-3-030-86316-6_3

DO - 10.1007/978-3-030-86316-6_3

M3 - Chapter

SN - 21903018 (ISSN)

VL - 261

BT - Springer Science and Business Media

PB - Springer Science and Business Media Deutschland GmbH

ER -