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- W20683381 abstract "The evolution of causal and predictive analysis by path models is reviewed, with special regard to the merging of two central avenues: the multirelational forecasting models of econometrics, and the modelling in terms of latent variables that is of old standing in psychology and education. The emphasis is on the rationale and broad scope of the NIPALS (Nonlinear Iterative PArtial Least Squares) approach to path models with latent variables. Characteristic of NIPALS modelling is the specification of causal and/or predictive relations in terms of predictors (conditional expectations), followed by least-squares estimation of the unknowns; namely, estimation by OLS (Ordinary Least Squares) regression of predictors where all variables are directly observed, and by iterative OLS when it comes to predictors that involve latent variables. At the gateway of the problem area is Hauser's pioneering model (1968, 1971). Various partings of the ways are examined on the basis of six NIPALS models, all of which are simple modifications of Hauser's model. The concluding remarks focus on the similarities and differences between NIPALS and ML (Maximum Likelihood) modelling." @default.
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- W20683381 date "1975-01-01" @default.
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- W20683381 title "Path Models with Latent Variables: The NIPALS Approach" @default.
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- W20683381 doi "https://doi.org/10.1016/b978-0-12-103950-9.50017-4" @default.
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