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Odegaard F, Roos P. Measuring worksite health promotion programs: an application of structural equation modeling with ordinal data. Eur J Health Econ. 2012;14(4):639-53doi: 10.1007/s10198-012-0409-4.
Odegaard, F., & Roos, P. (2013). Measuring worksite health promotion programs: an application of structural equation modeling with ordinal data. The European journal of health economics : HEPAC : health economics in prevention and care, 14(4), 639-53. https://doi.org/10.1007/s10198-012-0409-4
Odegaard, Fredrik, and Roos, Pontus. "Measuring worksite health promotion programs: an application of structural equation modeling with ordinal data." The European journal of health economics : HEPAC : health economics in prevention and care vol. 14,4 (2013): 639-53. doi: https://doi.org/10.1007/s10198-012-0409-4
Odegaard F, Roos P. Measuring worksite health promotion programs: an application of structural equation modeling with ordinal data. Eur J Health Econ. 2013 Aug;14(4):639-53. doi: 10.1007/s10198-012-0409-4. Epub 2012 Jul 20. PMID: 22815096.
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Eur J Health Econ. 2013 Aug;14(4):639-53. doi: 10.1007/s10198-012-0409-4. Epub 2012 Jul 20.

Measuring worksite health promotion programs: an application of structural equation modeling with ordinal data.

The European journal of health economics : HEPAC : health economics in prevention and care

Fredrik Odegaard, Pontus Roos

Affiliations

  1. Richard Ivey School of Business, Western University, 1150 Richmond Street, London, ON, N5Y 4B3, Canada. [email protected]

PMID: 22815096 DOI: 10.1007/s10198-012-0409-4

Abstract

This paper presents a model for measuring the outcome of Worksite Health Promotion Programs through an application of Structural Equation Modeling with ordinal data. We model the function "being healthy" as a vector comprised of three latent or unobservable variables: Health Status, Lifestyle and Stress. Each variable can be measured only indirectly through a set of manifest or observable ordinal indicators. The objective is to derive and analyze the distributions, and changes in distributions over time, of the latent variables on an individual level. The model is analyzed empirically on data from three large Swedish manufacturing firms.

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