TY - UNPB
T1 - A Partial Least Squares Algorithm Handling Ordinal Variables also in Presence of a Small Number of Categories
AU - Cantaluppi, Gabriele
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Ordinal Variables
KW - Partial Least Squares
KW - Path Analysis
KW - Polychoric Correlation Matrix
KW - Structural Equation Models with Latent Variables
KW - Ordinal Variables
KW - Partial Least Squares
KW - Path Analysis
KW - Polychoric Correlation Matrix
KW - Structural Equation Models with Latent Variables
UR - http://hdl.handle.net/10807/50765
UR - http://arxiv.org/pdf/1212.5049v1
M3 - Working paper
BT - A Partial Least Squares Algorithm Handling Ordinal Variables also in Presence of a Small Number of Categories
ER -