Evaluating the European Health Literacy Survey Questionnaire in Patients with Stroke: A Latent Trait Analysis Using Rasch Modeling

Yi Jing Huang, Cheng Te Chen, Gong Hong Lin, Tzu Yi Wu, Sheng Shiung Chen, Li Fong Lin, Wen Hsuan Hou, Ching Lin Hsieh

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: The effective self-management and treatment of long-term disability after stroke depends greatly on the health literacy of patients. The European Health Literacy Survey Questionnaire (HLS-EU-Q) is a comprehensive and theory-based measure that captures multiple self-perceived competencies of health literacy and covers a diverse range of health contexts. However, there is no psychometric evidence on the HLS-EU-Q in the stroke population. Objective: The aim of this study was to examine the validity of the HLS-EU-Q in patients with stroke using Rasch analysis. Methods: We compared the model deviance among the one-domain, three-domain, four-domain, and 12-domain structures using likelihood ratio tests to determine the dimensionality of the HLS-EU-Q. Thereafter, we examined the unidimensionality of each domain, local independence, item fit, response categories, and differential item functioning (DIF) for the best fitting structure. Results: A total of 311 patients with stroke participated in this study. Rasch analysis revealed that the 12-domain HLS-EU-Q demonstrated the best data–model fit. The original 4-point scales showed disordering, which can be corrected by rescaling them as 3-point scales with the two middle categories collapsed. All 47 items in the rescaled HLS-EU-Q fit the 12-domain Rasch model, demonstrated local independence, assessed the 12 unidimensional domains respectively, and had invariant difficulties between different age or education groups of the patients with stroke. Conclusion: We recommend using the 12-domain scores of the rescaled HLS-EU-Q to comprehensively and accurately capture the competencies to access, understand, appraise, and apply health information within the three health contexts of healthcare, disease prevention, and health promotion for patients with stroke.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalPatient
DOIs
Publication statusPublished - Feb 1 2018

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Health Literacy
Health Surveys
Stroke
Health
Patient Education
Self Care
Health Promotion
Psychometrics
Surveys and Questionnaires
Delivery of Health Care
Population

ASJC Scopus subject areas

  • Nursing (miscellaneous)

Cite this

Evaluating the European Health Literacy Survey Questionnaire in Patients with Stroke : A Latent Trait Analysis Using Rasch Modeling. / Huang, Yi Jing; Chen, Cheng Te; Lin, Gong Hong; Wu, Tzu Yi; Chen, Sheng Shiung; Lin, Li Fong; Hou, Wen Hsuan; Hsieh, Ching Lin.

In: Patient, 01.02.2018, p. 1-14.

Research output: Contribution to journalArticle

Huang, Yi Jing ; Chen, Cheng Te ; Lin, Gong Hong ; Wu, Tzu Yi ; Chen, Sheng Shiung ; Lin, Li Fong ; Hou, Wen Hsuan ; Hsieh, Ching Lin. / Evaluating the European Health Literacy Survey Questionnaire in Patients with Stroke : A Latent Trait Analysis Using Rasch Modeling. In: Patient. 2018 ; pp. 1-14.
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abstract = "Background: The effective self-management and treatment of long-term disability after stroke depends greatly on the health literacy of patients. The European Health Literacy Survey Questionnaire (HLS-EU-Q) is a comprehensive and theory-based measure that captures multiple self-perceived competencies of health literacy and covers a diverse range of health contexts. However, there is no psychometric evidence on the HLS-EU-Q in the stroke population. Objective: The aim of this study was to examine the validity of the HLS-EU-Q in patients with stroke using Rasch analysis. Methods: We compared the model deviance among the one-domain, three-domain, four-domain, and 12-domain structures using likelihood ratio tests to determine the dimensionality of the HLS-EU-Q. Thereafter, we examined the unidimensionality of each domain, local independence, item fit, response categories, and differential item functioning (DIF) for the best fitting structure. Results: A total of 311 patients with stroke participated in this study. Rasch analysis revealed that the 12-domain HLS-EU-Q demonstrated the best data–model fit. The original 4-point scales showed disordering, which can be corrected by rescaling them as 3-point scales with the two middle categories collapsed. All 47 items in the rescaled HLS-EU-Q fit the 12-domain Rasch model, demonstrated local independence, assessed the 12 unidimensional domains respectively, and had invariant difficulties between different age or education groups of the patients with stroke. Conclusion: We recommend using the 12-domain scores of the rescaled HLS-EU-Q to comprehensively and accurately capture the competencies to access, understand, appraise, and apply health information within the three health contexts of healthcare, disease prevention, and health promotion for patients with stroke.",
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