The partial least squares (PLS) is a popular modeling technique commonly used in social sciences. The traditional PLS algorithm deals with variables measured on interval scales while data are often collected on ordinal scales: a reformulation of the algorithm, named ordinal PLS (OPLS), is introduced, which properly deals with ordinal variables. An application to customer satisfaction data and some simulations are also presented. The technique seems to perform better than the traditional PLS when the number of categories of the items in the questionnaire is small (4 or 5) which is typical in the most common practical situations.
Cantaluppi, G., A Partial Least Squares Algorithm Handling Ordinal Variables also in Presence of a Small Number of Categories, <<Quaderno di Dipartimento N. 14, Serie E.P. N° 144, Dipartimento di Scienze statistiche>>, 2012; (14): 1-33 [http://hdl.handle.net/10807/50765]
A Partial Least Squares Algorithm Handling Ordinal Variables also in Presence of a Small Number of Categories
Cantaluppi, Gabriele
2012
Abstract
The partial least squares (PLS) is a popular modeling technique commonly used in social sciences. The traditional PLS algorithm deals with variables measured on interval scales while data are often collected on ordinal scales: a reformulation of the algorithm, named ordinal PLS (OPLS), is introduced, which properly deals with ordinal variables. An application to customer satisfaction data and some simulations are also presented. The technique seems to perform better than the traditional PLS when the number of categories of the items in the questionnaire is small (4 or 5) which is typical in the most common practical situations.
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