Introductory Analysis of the Rasch Model: Smart Innovation, Systems and Technologies

I. Storey

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationSpringer Science and Business Media
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages32
Volume261
ISBN (Print)21903018 (ISSN)
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Education computing
  • Students
  • Basic-elements
  • Fitness measures
  • Probability theory
  • Psychometric test
  • Psychometric testing
  • Rasch modeling
  • Regression techniques
  • Sample sizes
  • Statistical approach
  • Teachers'
  • Probability

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