Use of Natural Language Processing (NLP) to Support Assuring the Internal Validity of Qualitative Research

Conducting qualitative research has a subjective nature that can create issues with internal validity, and therefore, it is necessary for qualitative researchers to address such issues by implementing specific strategies. In this regard, using the natural language processing (NLP) techniques can be helpful. This paper provides an example of using these techniques to examine the internal validity of qualitative research in the AEC domain based on a previously conducted and validated comprehensive qualitative research by our research team. In this way, we show how an NLP analysis can be set up and applied for reviewing qualitative data within the AEC domain by providing specific examples from our previously conducted qualitative research. The paper concludes that NLP techniques in their current stage of maturity might not provide the ultimate proof for the truthfulness of the context conveyed in the qualitative research; however, the introduced NLP analyses in this study can be used as a complementary method for assuring the research internal validity during and after a qualitative research is conducted. This research presents a new approach for using NLP techniques as an emerging technology to enhance the internal validity of construction research. It also provides valuable insights for domain researchers on the limitations and uncertainties associated with the current state of NLP technology.