Stroke self-efifcacy questionnaire: A Rasch-refined measure of confidence post stroke

Afsane Riazi, Trefor Aspden, Fiona Jones

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

38 Dyfyniadau(SciVal)
537 Wedi eu Llwytho i Lawr (Pure)


Measuring self-efficacy during rehabilitation provides an important insight into understanding recovery post stroke. A Rasch analysis of the Stroke Self-efficacy Questionnaire (SSEQ) was undertaken to establish its use as a clinically meaningful and scientifically rigorous measure.

One hundred and eighteen stroke patients completed the SSEQ with the help of an interviewer. Participants were recruited from local acute stroke units and community stroke rehabilitation teams. Data were analysed with confirmatory factor analysis conducted using AMOS and Rasch analysis conducted using RUMM2030 software.

Confirmatory factor analysis and Rasch analyses demonstrated the presence of two separate scales that measure stroke survivors' self-efficacy with: i) self-management and ii) functional activities. Guided by Rasch analyses, the response categories of these two scales were collapsed from an 11-point to a 4-point scale. Modified scales met the expectations of the Rasch model. Items satisfied the Rasch requirements (overall and individual item fit, local response independence, differential item functioning, unidimensionality). Furthermore, the two subscales showed evidence of good construct validity.

The new SSEQ has good psychometric properties and is a clinically useful assessment of self-efficacy after stroke. The scale measures stroke survivors' self-efficacy with self-management and activities as two unidimensional constructs. It is recommended for use in clinical and research interventions, and in evaluating stroke self-management interventions
Iaith wreiddiolSaesneg
Tudalennau (o-i)406-412
Nifer y tudalennau7
CyfnodolynJournal of Rehabilitation Medicine
Rhif cyhoeddi5
Dyddiad ar-lein cynnar24 Maw 2014
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 31 Mai 2014

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