Dataset for Worst Case Resistance Testing: A Nonresponse Bias Solution for Today’s Survey Research Realities
Principal Investigator(s): View help for Principal Investigator(s) Stephen L. France, Mississippi State University; Frank Adams, Mississippi State University; Myles Landers, Mississippi State University
Version: View help for Version V2
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Project Citation:
France, Stephen L., Adams, Frank, and Landers, Myles. Dataset for Worst Case Resistance Testing: A Nonresponse Bias Solution for Today’s Survey Research Realities. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-05-22. https://doi.org/10.3886/E203261V2
Project Description
Summary:
View help for Summary
The dataset contains data gathered from a Qualtrics
panel sample involving a previous retail shopping experience. The dataset was
gathered to help test and validate methods for dealing with nonresponse bias in
the following paper:
France, S. L., Adams, F. G., Landers, V. M. (2024). Worst Case Resistance Testing: A Nonresponse Bias Solution for Today’s Survey Research Realities, forthcoming in Survey Research Methods.
The rationale for the use of this sample was to gather response data from a well known and validated set of instruments. The questionnaire adapts instruments previous developed in a previous paper. As noted in the paper “as Szymanski & Henard (2001) have over 3,000 citations, and pose relatively simple questions, they were judged as liable to provide stable results, and unlikely to represent confounding factors due to their complexity.”
Szymanski, D. M., & Henard, D. H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16-35.
France, S. L., Adams, F. G., Landers, V. M. (2024). Worst Case Resistance Testing: A Nonresponse Bias Solution for Today’s Survey Research Realities, forthcoming in Survey Research Methods.
The rationale for the use of this sample was to gather response data from a well known and validated set of instruments. The questionnaire adapts instruments previous developed in a previous paper. As noted in the paper “as Szymanski & Henard (2001) have over 3,000 citations, and pose relatively simple questions, they were judged as liable to provide stable results, and unlikely to represent confounding factors due to their complexity.”
Szymanski, D. M., & Henard, D. H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16-35.
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