Abstract
Data from customer satisfaction surveys are multivariate—there are several questions resulting in as many endpoints. Furthermore, survey data typically don’t fit into simple parametric models. Indeed, the endpoints or response variables may be measured on different types of scales (metric, ordinal, binary). For these two reasons, one requires multivariate inference methods, and specifically methods that can deal with a mix of response variable types. Additionally, it would be advantageous if the procedures also performed well for small to moderate numbers of respondents, as not every survey can afford to obtain responses from hundreds of participants.
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Arboretti, R. et al. (2018). Analyzing Survey Data Using Multivariate Rank-Based Inference. In: Parametric and Nonparametric Statistics for Sample Surveys and Customer Satisfaction Data. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-91740-5_5
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