Predicting gender differences as latent variables: summed scores, and individual item responses: a methods case study

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Creator
  • Pietrobon, Ricardo
    • Other Affiliation: Duke University Medical Center
  • Taylor, Marcus
    • Other Affiliation: Duke University Medical Center
  • Guller, Ulrich
    • Other Affiliation: University Hospital Basel
  • Higgins, Laurence D
    • Other Affiliation: Duke University Medical Center
  • Jacobs, Danny O
    • Other Affiliation: Duke University Medical Center
  • Carey, Timothy
    • Affiliation: School of Medicine, Department of Medicine, Division of General Medicine and Clinical Epidemiology
Abstract
  • Background Modeling latent variables such as physical disability is challenging since its measurement is performed through proxies. This poses significant methodological challenges. The objective of this article is to present three different methods to predict latent variables based on classical summed scores, individual item responses, and latent variable models. Methods This is a review of the literature and data analysis using "layers of information". Data was collected from the North Carolina Back Pain Project, using a modified version of the Roland Questionnaire. Results The three models are compared in relation to their goals and underlying concepts, previous clinical applications, data requirements, statistical theory, and practical applications. Initial linear regression models demonstrated a difference in disability between genders of 1.32 points (95% CI 0.65, 2.00) on a scale from 0–23. Subsequent item analysis found contradictory results across items, with no clear pattern. Finally, IRT models demonstrated three items were demonstrated to present differential item functioning. After these items were removed, the difference between genders was reduced to 0.78 points (95% CI, -0.99, 1.23). These results were shown to be robust with re-sampling methods. Conclusions Purported differences in the levels of a latent variable should be tested using different models to verify whether these differences are real or simply distorted by model assumptions.
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Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Ricardo Pietrobon et al.; licensee BioMed Central Ltd.
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Journal title
  • Health and Quality of Life Outcomes
Journal volume
  • 2
Journal issue
  • 1
Page start
  • 59
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1477-7525
Bibliographic citation
  • Health and Quality of Life Outcomes. 2004 Oct 25;2(1):59
Publisher
  • BioMed Central Ltd
Access right
  • Open Access
Date uploaded
  • November 6, 2015

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