Quantifying cognitive bias in educational researchers. Issue 4 (8th August 2021)

• Record Type: Journal Article
• Authors: Bierema, Andrea; Hoskinson, Anne-Marie; Moscarella, Rosa; Lyford, Alex; Haudek, Kevin; Merrill, John; Urban-Lurain, Mark
• Is Part Of: International journal of research & method in education. Volume 44:Issue 4(2021)
• Page Start: 395
• Page End: 413
• Publication Date: 2021-08-08
• Subjects: Coding -- computer model calibration -- cognitive bias -- confirmation bias -- experimenter bias -- logistic regression -- machine learning; Education -- Research -- Periodicals; Education -- Periodicals; 370.705
• DOI: 10.1080/1743727X.2020.1804541
• Languages: English
• ISSNs: 1743-727X

Abstract

ABSTRACT: As we take advantage of new technologies that allow us to streamline the coding process of large qualitative datasets, we must consider whether human cognitive bias may introduce statistical bias in the process. Our research group analyzes large sets of student responses by developing computer models that are trained using human-coded responses and a suite of machine-learning techniques. Once a model is initially trained, it may be insufficiently accurate. Increasing the number of human-coded responses typically enhances these models to an acceptable level of accuracy. Alternatively, instead of human coding responses, we can rapidly increase the number of coded responses by verifying computer-predicted codes for each response. However, having access to this information may bias human coders. We designed the present study to test for differences in level of agreement with computer-predicted codes in terms of magnitude and direction during computer model calibration if information about computer-predicted codes is available. Our results indicate human coding bias despite being disciplinary experts who were aware of the possibility of cognitive bias creating statistical bias and that magnitude and direction of that bias varies across experts.