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Health Measurement Scales: A practical guide to their development and use
Abstract
Health Measurement Scales is the ultimate online guide to developing and validating measurement scales that are to be used in the health sciences. It covers how the individual items are developed; various biases that can affect responses (e.g. social desirability, yea-saying, framing); various response options; how to select the best items in the set; how to combine them into a scale; and finally how to determine the reliability and validity of the scale. It concludes with a discussion of ethical issues that may be encountered, and guidelines for reporting the results of the scale development process. Appendices include a comprehensive guide to finding existing scales, and a brief introduction to exploratory and confirmatory factor analysis.
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“DASS was translated by Livheim34 by using a back-translation technique.35”
From Acceptance and commitment therapy (ACT) for adult type 1 diabetes management: Study protocol for a randomised controlled trial (2018) · cites this paper as [35]
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Metadata
- Authors
- Streiner, D. L., Norman, G. R., & Cairney, J.
- Affiliations
- University of Toronto (CA)
- McMaster University (CA)
- Publisher
- Oxford University PressOxford
- Published
- 2014-11
- OpenAlex
- W2480133395
- Cited by
- 2,564
- References
- 0
- Citations by year
- 2026: 146 · 2025: 232 · 2024: 302 · 2023: 264 · 2022: 280 · 2021: 296 · 2020: 235 · 2019: 176 · 2018: 117 · 2017: 88 · 2016: 58 · 2015: 24 · 2014: 7
- FWCI
- 32.49
- Type
- book
- Language
- en
- Open access
- closed
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Health Sciences › Nursing › Research and Theory › Health Education and Validation
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- scale development, scale validation, health measurement, item development, response bias, social desirability bias, framing effects, response options, item selection, reliability statistics, validity, research ethics, factor analysis
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