Application of the Rasch measurement framework to mammography positioning data

Patsy Whelehan*, Maria Pampaka, Jennifer Boyd, Sarah Armstrong, Andy Evans, Gozde Ozakinci

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

The purpose of this article is to provide raw data and measure-validation data pertaining to a co-submission to published in European Journal of Radiology and entitled: Development and validation of a novel measure of adverse patient positioning in mammography.
This Data in Brief article serves not only to provide greater detail than its companion article but also as an educational worked example of the Rasch measurement framework. Rasch measurement is a form of modern psychometric technique and our articles provide the first known example of its use in the evaluation of clinical radiological image quality.
The data consist of observations of mammographic images, selected relevant patient and examination data, and validation indices produced by subjecting the primary data to Rasch analysis. An expert observer generated the primary data by reviewing mammographic images to judge the presence or absence of a set of features developed through theory and consultation with other experts. The validation data were generated through Rasch analysis, performed using Winsteps® software, which mathematically models the probability of having a correct response (or a present feature in this dataset) to an item in a given measurement instrument (e.g. questionnaire), as a function of the participant's ability/position on the underlying construct under study.
The data can be reused by anyone wishing to learn and practice psychometric validation techniques. They can also form a basis for researchers wishing to build on our preliminary measure for the assessment of mammographic clinical image quality.
Original languageEnglish
Article number107387
Number of pages16
JournalData in Brief
Volume38
Early online date20 Sept 2021
DOIs
Publication statusPublished - Oct 2021

Keywords

  • Mammography
  • Breast imaging
  • Mammography image quality
  • Rasch model
  • Measurement theory

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