Abstract
Bootstrap is a resampling technique proposed by Efron (The Annals of Statistics 7:1–26, 1979). It has been used in many fields, but in case of missing data studies one can find only a few references.
Most studies in marketing research are based on questionnaires, that, for several reasons present missing responses. The missing data problem is a common issue in market research. Here, a customer satisfaction model following the ACSI barometer from Fornell (Journal of Marketing 60(4):7–18, 1996; The American customer satisfaction index: methodology report. Michigan: University of Michigan Business School, 1998) will be considered. Sometimes not all customers experience all services or products. Therefore, we may have to deal with missing data, taking the risk of reaching non-significant impacts of these drivers on Customer Satisfaction and resulting in inaccurate inferences. To estimate the main drivers of Customer Satisfaction, Structural Equation Models methodology is applied (Peters and Enders, Journal of Targeting Measurement and Analysis for Marketing 11(1):81–95, 2002).
For a case study in mobile telecommunications several missing data imputation techniques were reviewed and used to complete the data set. Bootstrap methodology was also considered jointly with imputation techniques to complete the data set. Finally, using Partial Least Squares (PLS) algorithm we could compare the above procedures. It suggests that bootstrapping before imputation can be a promising idea.
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Cordeiro, C., Machás, A., Neves, M.M. (2010). A Case Study of a Customer Satisfaction Problem: Bootstrap and Imputation Techniques. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_13
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DOI: https://doi.org/10.1007/978-3-540-32827-8_13
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