Averaging models: Parameters estimation with the R-Average procedure
- Autores: G. Vidotto, D. Massidda, S. Noventa
- Localización: Psicológica: Revista de metodología y psicología experimental, ISSN-e 1576-8597, ISSN 0211-2159, Vol. 31, Nº 3, 2010, págs. 461-475
- Idioma: inglés
- Enlaces
- Dialnet Métricas: 4 Citas
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Resumen
The Functional Measurement approach, proposed within the theoretical framework of Information Integration Theory (Anderson, 1981, 1982), can be a useful multi-attribute analysis tool. Compared to the majority of statistical models, the averaging model can account for interaction effects without adding complexity. The R-Average method (Vidotto & Vicentini, 2007) can be used to estimate the parameters of these models. By the use of multiple information criteria in the model selection procedure, R-Average allows for the identification of the best subset of parameters that account for the data. After a review of the general method, we present an implementation of the procedure in the framework of R-project, followed by some experiments using a Monte Carlo method.
