Using CATA and Machine Learning to Operationalize Old Constructs in New Ways: An Illustration Using US Governors' COVID-19 Press Briefings
ORGANIZATIONAL RESEARCH METHODS(2023)
摘要
Increased computing power and greater access to online data have led to rapid growth in the use of computer-aided text analysis (CATA) and machine learning methods. Using "big data", researchers have not only advanced new streams of research, but also new research methodologies. Noting this trend and simultaneously recognizing the value of traditional research methods, we lay out a methodology that bridges the gap between old and new approaches to operationalize old constructs in new ways. With a combination of web scraping, CATA, and supervised machine learning, using labeled ground truth data (i.e., data with known inputs and outputs), we train a model to predict CIP (Charismatic-Ideological-Pragmatic) leadership styles from running text. To illustrate this method, we apply the model to classify U.S. state governors' COVID-19 press briefings according to their CIP leadership style. In addition, we demonstrate content and convergent validity of the method.
更多查看译文
关键词
text analysis,CATA,web scraping,machine learning,leadership,COVID-19
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要