Information Theoretic Metrics for Combining Qualitative and Quantitative Data from Consumer Surveys

Vamsi Salaka () and Vittal Prabhu
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Vamsi Salaka: Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802
Vittal Prabhu: Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802

Service Science, 2011, vol. 3, issue 4, 338-348

Abstract: Surveys are a critical channel of information from various stake holders in most service processes. Design of surveys and analysis of data collected from them is a well-established discipline within social sciences, and the associated analysis methods can be broadly categorized into quantitative and qualitative techniques. We propose information theoretic metrics to measure the value of combining qualitative and quantitative data from surveys. Specifically, we use the concepts of information entropy to estimate uncertainty, the concepts of information gain, mutual information, and conditional entropy to measure triangulation, complementation or paradox. Finally, we apply the proposed metrics in a case study to illustrate the practical application of our work in analysis of a consumer survey from a service industry. The proposed information theoretic approach offers a scientific basis for automating survey data analysis in service processes, especially when such surveys contain quantitative and qualitative responses. [ Service Science , ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]

Keywords: information theory; qualitative data; service system; consumer survey; survey design (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:3:y:2011:i:4:p:338-348

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