In market research, such as for the measure of the customer satisfaction, data are collected through questionnaires. Responses are often classified into ordered categories, so observed variables are ordinal and the rate of missing data may be very high. In this paper, a method for the analysis of a categorical and incomplete data matrix is proposed. Our methodology is applied to data collected by a market survey of Fiat Auto in order to show the latent dimensions underlying the customer satisfaction with car dealers. After multiple imputation of missing values the polychoric correlation matrix, measuring the manifest variables correlations, is computed and used as a proper input to factor analysis. Two factors underlying the several judgement items are thus obtained and their weights on the global judgement ordinal variable are then estimated by ordered probit regression.

A statistical analysis of the customer satisfaction with car dealers

POLLICE, Alessio
2004

Abstract

In market research, such as for the measure of the customer satisfaction, data are collected through questionnaires. Responses are often classified into ordered categories, so observed variables are ordinal and the rate of missing data may be very high. In this paper, a method for the analysis of a categorical and incomplete data matrix is proposed. Our methodology is applied to data collected by a market survey of Fiat Auto in order to show the latent dimensions underlying the customer satisfaction with car dealers. After multiple imputation of missing values the polychoric correlation matrix, measuring the manifest variables correlations, is computed and used as a proper input to factor analysis. Two factors underlying the several judgement items are thus obtained and their weights on the global judgement ordinal variable are then estimated by ordered probit regression.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/8293
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