A method for shortening instruments using the Rasch model - Validation on a hand functional measure

Citation
C. Luquet et al., A method for shortening instruments using the Rasch model - Validation on a hand functional measure, REV EPIDEM, 49(3), 2001, pp. 273-285
Citations number
30
Categorie Soggetti
Envirnomentale Medicine & Public Health
Journal title
REVUE D EPIDEMIOLOGIE ET DE SANTE PUBLIQUE
ISSN journal
03987620 → ACNP
Volume
49
Issue
3
Year of publication
2001
Pages
273 - 285
Database
ISI
SICI code
0398-7620(200106)49:3<273:AMFSIU>2.0.ZU;2-4
Abstract
Background: Many measurement instruments, particularly measures of hand fun ctional ability, frequently comprise a large number of items. Reduced versi ons of these instruments can facilitate their use. This work proposes a new method for shortening an instrument. Methods: The method proposed was based on a scale of item difficulty calcul ated using the Rasch model. It was applied on a hand functional measure com prising 67 tests. The sample included 194 patients with hand lesions. The s hortened instrument obtained was compared with those provided by classic me thods used in the literature, with item random choice, and with shortened v ersions proposed by four independent experts, two rehabilitation physicians and two occupational therapists, who are clinicians familiar with the tool . All the statistical analyses were carried out on a random sub-group of tw o-thirds of the sample. A cross validation was then carried out on the rema ining third. Results: The reduction obtained had score non significantly different from that of the original instrument, In addition, the intra-class correlation c oefficient and the Cronbach alpha coefficient were high. Among the differen t degrees of reduction investigated the 12-item version seemed to be approp riate. Our method appeared to provide better results in terms of discrimina nt validity and internal validity than the choices of the four experts, The reductions produced were also better than those obtained by classic method s based on principal component analysis and multiple linear regression, as well as those obtained by random choices of items. Conclusion: The method presented is pertinent and useful. The reduction obt ained appeared to be better than the choices of experts and the reductions provided by classic methods. The method could be used in other fields.