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Efficient Methods for Sampling Responses from Large-Scale Qualitative Data

Author

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  • Surendra N. Singh

    (School of Business, University of Kansas, Lawrence, Kansas 66045)

  • Steve Hillmer

    (School of Business, University of Kansas, Lawrence, Kansas 66045)

  • Ze Wang

    (College of Business Administration, University of Central Florida, Orlando, Florida 32816)

Abstract

The World Wide Web contains a vast corpus of consumer-generated content that holds invaluable insights for improving the product and service offerings of firms. Yet the typical method for extracting diagnostic information from online content--text mining--has limitations. As a starting point, we propose analyzing a sample of comments before initiating text mining. Using a combination of real data and simulations, we demonstrate that a sampling procedure that selects respondents whose comments contain a large amount of information is superior to the two most popular sampling methods--simple random sampling and stratified random sampling---in gaining insights from the data. In addition, we derive a method that determines the probability of observing diagnostic information repeated a specific number of times in the population, which will enable managers to base sample size decisions on the trade-off between obtaining additional diagnostic information and the added expense of a larger sample. We provide an illustration of one of the methods using a real data set from a website containing qualitative comments about staying at a hotel and demonstrate how sampling qualitative comments can be a useful first step in text mining.

Suggested Citation

  • Surendra N. Singh & Steve Hillmer & Ze Wang, 2011. "Efficient Methods for Sampling Responses from Large-Scale Qualitative Data," Marketing Science, INFORMS, vol. 30(3), pages 532-549, 05-06.
    • Handle: RePEc:inm:ormksc:v:30:y:2011:i:3:p:532-549
    DOI: 10.1287/mksc.1100.0632
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    References listed on IDEAS

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    1. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
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    2. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    3. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2019. "Technology in the 21st century: New challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 321-335.
    4. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.

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