In this article, we describe how a latent variable modeling approach to the specification of measurement error unifies and benefits traditional methods of examining reliability in psychology and medicine. The models presented include classical reliability and generalizability theory to account for measurement error, latent class analysis to assess sensitivity and specificity, and item response theory to improve questionnaire development. We also illustrate how working with latent variables, in addition to addressing measurement error, may help deal with some instances of missing data. Throughout the article, analyses and results from examples and published articles are presented to illustrate the advantage of working with latent variables.
机构:
Kobe Univ, Grad Sch Business Adm, Kobe, Hyogo, Japan
Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, W Yorkshire, EnglandKobe Univ, Grad Sch Business Adm, Kobe, Hyogo, Japan
Sanko, Nobuhiro
Hess, Stephane
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Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, W Yorkshire, EnglandKobe Univ, Grad Sch Business Adm, Kobe, Hyogo, Japan
Hess, Stephane
Dumont, Jeffrey
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Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, W Yorkshire, England
RSG, Charlotte, NC USAKobe Univ, Grad Sch Business Adm, Kobe, Hyogo, Japan
机构:
Hong Kong Univ Sci & Technol, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R ChinaHong Kong Univ Sci & Technol, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China
Zhang, NL
Nielsen, TD
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机构:Hong Kong Univ Sci & Technol, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China
Nielsen, TD
Jensen, FV
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机构:Hong Kong Univ Sci & Technol, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China