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Estimation of Nonlinear Models in the Presence of Measurement Error *
Author(s) -
Higgins Lexis F.,
Judd Charles M.
Publication year - 1990
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1990.tb01247.x
Subject(s) - multiplicative function , observational error , errors in variables models , computer science , nonlinear system , estimation , data mining , latent variable , multilevel model , econometrics , statistics , algorithm , mathematics , machine learning , engineering , mathematical analysis , physics , systems engineering , quantum mechanics
Techniques used in decision sciences and business research to estimate interactions between latent variables are limited in controlling for measurement error. This article uses a latent structure modeling approach that substantially controls for measurement error in nonlinear relationships. The results of this technique are compared to the results obtained applying hierarchical regression analysis and the impact of measurement error is assessed. The paper provides a unique assessment of the validity of the multi‐attribute attitude model. The validity of the multiplicative rule in the model is supported.