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 -