A Three-GenAI Consensus Model for Deductive Analysis of Response Data

In deductive content analysis, text data is categorized into previously developed themes. We present a three-GenAI consensus model for deductive analysis of text data. In the model, the same prompt and data are provided to three GenAIs, and their agreement with each other is used to cluster data to facilitate deductive coding by researchers. First, data is split into trial and corpus sets. Trial data is used to iteratively revise the prompt provided to the three GenAIs. Next, the revised prompt is provided to the three GenAIs to deductively code the larger corpus data. Based on the responses of the three GenAIs, data is clustered into three groups: all three GenAIs agree, only 2 GenAIs agree, and all three GenAIs disagree. The data in the three clusters is separately coded by researchers, with the GenAI coding in each cluster used to narrow coding choices for the data in that cluster. To illustrate the use of the three-GenAI consensus model, we used it to deductively analyze 496 student responses to a self-directedness survey that had been previously coded by researchers. We calculated that the researchers would have saved nearly 70\% of coding time if they had used the three-GenAI consensus model first. Deductive analysis of text data is labor-intensive, involving multiple researchers and multiple iterations. The three-GenAI consensus model enables researchers to redistribute time from exhaustively analyzing all data to closely reviewing only the data that does not clearly fit any of the themes of deductive analysis.