“…In response to Hallinger's (2014) call for the improvement in the methodology of conducting systematic reviews of research in education leadership, in this study we build on recent innovations in the field of automated text data mining and machine learning to apply probabilistic topic modeling-a suite of automated text mining algorithms that computationally detect latent topic structures from a corpus of documents such as journal articles-to investigate the nature of topics in the educational leadership research literature. As Educational Administration Quarterly (EAQ) has been consistently regarded as the most prestigious research journal in the field (Campbell, 1979;Cherkowski, Currie, & Hilton, 2011;Haas et al, 2007;Murphy et al, 2007;Richardson & McLeod, 2009;Wang & Bowers, 2016), we use probabilistic topic modeling to empirically derive the latent topics discussed by the research literature across the entire history of EAQ starting with Volume 1, Issue 1, in 1965 up through Volume 50, Issue 5, in 2014, as a means to build on the past work of narrative reviews (e.g., Campbell, 1979;Haller, 1968;Murphy et al, 2007). We specifically seek to answer two research questions:…”