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Big Data and the Challenge of Construct Validity

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  • Braun, Michael T.
  • Kuljanin, Goran

Abstract

One important issue not highlighted by Guzzo, Fink, King, Tonidandel, and Landis (2015) is that simply establishing construct validity will be significantly more challenging with big data than ever before. One needs to only look as far as the other social sciences analyzing big data (e.g., communications, economics, industrial engineering) to observe the difficulty of making valid claims as to what measured variables substantively “mean.” This presents a significant hurdle in the application of big data to organizational research questions because of the critical importance of demonstrating validity in the organizational sciences as highlighted by Guzzo et al.

Suggested Citation

  • Braun, Michael T. & Kuljanin, Goran, 2015. "Big Data and the Challenge of Construct Validity," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(4), pages 521-527, December.
    • Handle: RePEc:cup:inorps:v:8:y:2015:i:04:p:521-527_00
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    Cited by:

    1. Calvard, Thomas Stephen & Jeske, Debora, 2018. "Developing human resource data risk management in the age of big data," International Journal of Information Management, Elsevier, vol. 43(C), pages 159-164.
    2. Camilla Salvatore, 2023. "Inference with non-probability samples and survey data integration: a science mapping study," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 83-107, April.

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