Enhancing Mixed Methods Research with your generative AI assistant – A Tutorial and Evaluation for Scholars

Abstract

Scholars from all disciplines can benefit from learning how to use generative Artificial Intelligence (GAI or AI) for data analysis. The current article used real data to demonstrate the analysis and synthesis of mixed methods research (MMR) data with generative AI. I also explore how reliable and valid data outputs are and how to improve the use of generative AI for research. The demonstration data used emanated from a study done in South Africa, with a quantitative sample size of 969 first-year engineering students and, for the qualitative part, 14 second-year students. In the current article, I compare my original analysis to ChatGPT results. Generative AI is a mind tool that is ideal when utilised with human insight to check the cohesion, consistency, and accuracy of the data analysis. The current content is geared towards enhancing methodological application regardless of field or discipline and includes access to a prompt library and examples of using outputs. For the qualitative analysis, I found that ChatGPT could detect similar themes but missed some, and its write-up was shallower than our human version. The quantitative analysis was accurate for the descriptive statistics, but the researcher had to use best judgment to select the correct inferential analysis. A quantitative and qualitative analysis should be conducted separately in generative AI before asking the bot for help with mixed methods research. I give guidelines and a tutorial on how to use chatbots in an ethically responsible and scientifically sound manner for research in social and human sciences.