Handling missing values in multiple factor analysis

Handling missing values is an unavoidable problem in the practice of statistics. We focus on multiple factor analysis in the sense of Escofier and Pages (2008), a principal component method that simultaneously takes into account several multivariate datasets composed of continuous and/or categorical variables. The suggested strategy to deal with missing values, named regularised iterative MFA, is derived from a method available in principal component analysis which consists in alternating a step of estimation of the axes and components and a step of estimation of the missing values. The pattern of missing values considered can be structured with missing rows in some datasets. Some simulations and real examples that cover several situations in sensory analysis are used to illustrate the methodology. We focus on the important issue of the maximum number of products that can be assessed during an evaluation task. (C) 2013 Elsevier Ltd. All rights reserved.