Measuring and optimizing the validity of Means-End data

Abstract

The validity of means-end data is discussed from a conceptual end empirical perspective. A dynamic programming approach (Markov chain) is then proposed that enables to measure the validity and reliability of any group of means-end data (item, hierachical level, whole data basis), thus enabling to purify it and to have empirical insights on debated topics such as the number of means-end levels to be considered and the validity of the means-end data collection methods.