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The promise of open survey questions—The validation of text-based job satisfaction measures

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  • Indy Wijngaards
  • Martijn Burger
  • Job van Exel

Abstract

Recent advances in computer-aided text analysis (CATA) have allowed organizational scientists to construct reliable and convenient measures from open texts. As yet, there is a lack of research into using CATA to analyze responses to open survey questions and constructing text-based measures of psychological constructs. In our study, we demonstrated the potential of CATA methods for the construction of text-based job satisfaction measures based on responses to a completely open and semi-open question. To do this, we employed three sentiment analysis techniques: Linguistic Inquiry and Word Count 2015, SentimentR and SentiStrength, and quantified the forms of measurement error they introduced: specific factor error, algorithm error and transient error. We conducted an initial test of the text-based measures’ validity, assessing their convergence with closed-question job satisfaction measures. We adopted a time-lagged survey design (Nwave 1 = 996; Nwave 2 = 116) to test our hypotheses. In line with our hypotheses, we found that specific factor error is higher in the open question text-based measure than in the semi-open question text-based measure. As expected, algorithm error was substantial for both the open and semi-open question text-based measures. Transient error in the text-based measures was higher than expected, as it generally exceeded the transient error in the human-coded and the closed job satisfaction question measures. Our initial test of convergent and discriminant validity indicated that the semi-open question text-based measure is especially suitable for measuring job satisfaction. Our article ends with a discussion of limitations and an agenda for future research.

Suggested Citation

  • Indy Wijngaards & Martijn Burger & Job van Exel, 2019. "The promise of open survey questions—The validation of text-based job satisfaction measures," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-22, December.
    • Handle: RePEc:plo:pone00:0226408
    DOI: 10.1371/journal.pone.0226408
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    References listed on IDEAS

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    1. Indy Wijngaards & Owen C. King & Martijn J. Burger & Job Exel, 2022. "Worker Well-Being: What it Is, and how it Should Be Measured," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(2), pages 795-832, April.

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