Quantitative Research Methods Training in Education Leadership and Administration Preparation Programs as Disciplined Inquiry for Building School Improvement Capacity

Abstract

The quantitative research methods course is a staple of graduate programs in education leadership and administration. Historically, these courses serve to train aspiring district and school leaders in fundamental statistical research topics. This article argues for programs to focus as well in these courses on helping aspiring leaders develop skills as practitioner-scholars, including deepening their practice around data analytics, providing opportunities to read and evaluate peer-reviewed research, analyzing data using current methods, and applying findings to facilitate building evidence-based improvement cycles in their schools. Additional data leadership training should be offered for the practicing administrator, educational quantitative analyst, research specialist, and district data scientist.

Get the Most from Your Survey: An Application of Rasch Analysis for Education Leaders

“…This demonstration study, then, is designed for an audience of education practitioner-scholars for whom data-based decision-making and data leadership are priorities (Datnow & Park, 2014). Alex Bowers (2017) posits that graduate training in education leadership prepares candidates for four distinct capacities within what is often a single role: practicing administrator, educational quantitative analyst, research specialist, and data scientist. Courses on quantitative methods within such graduate programs emphasize data literacy and utilization, so training in psychometric techniques is both critical and increasingly common.…”

Data analytics and decision making in education: towards the educational data scientist as a key actor in schools and higher education institutions

“…Such an effort has been traditionally very strong, because of the way that data are stored in the educational institutionsnamely, following administrative rules and procedures, and with little use of technology . Things are changing rapidly, and today many schooling datasets are huge, well-classified, readily available, and much diversified (Behrens & DiCerbo, 2014;Bowers, 2017;Bowers, Shoho, & Barnett, 2014;Koedinger et al, 2010). This evolution has been immediately caught by researchers, and applications of sophisticated data miningstyle analyses to those datasets have generated a rich stream of academic papers, reports, and practical applications (Baker & Inventado, 2014).…”

“…Therefore, as acknowledged by Siemens himself, the EDUCAUSE's Next Generation learning initiative offers a broader and cleaner definition, which is "the use of data and models to predict student progress and performance, and the ability to act on that information" 2 . In the last definition, the explicit prevision of the "use" of (analysed and elaborated) information introduces another crucial feature of LA, which is the managerial and policy implication derived from the analysisthat is to say, school principals and policy makers put actions in practice based on their judgments about the results of the analyses (Bienkowski, et al, 2012;Bowers, 2017;Bowers, et al, 2014;Feng, et al, 2016). In such a perspective, scholars involved in LA tend to have a different approach from academic educational scientists that focus solely on the results of studies (also suggesting implications) as LA researchers tend to show very practical uses of the findings for acting in the real life of school management, teaching practices, or policy decisions to be taken.…”

“…Seen from an institutional perspective, one general characteristic of educational data analytics (and LA in particular) is that the effort traditionally made by individual teachers or specific groups of teachers, i.e. the use of data for improving educational experiences and results, is now made systematic at the organizational level (Bowers, 2017;Bowers, et al, 2016). In other terms, it is no more the single responsibility of individuals in schools to use quantitative evidence for rethinking and redesigning didactic activities and interventions (Cosner, 2014;Farley-Ripple & Buttram, 2015;Schildkamp, Poortman, & Handelzalts, 2016), as the recent research argues that data analytics is also an institution's taskand, of course, the tools that an institution can use for this aim are much more powerful than those in the hands of volunteer teachers and assistants (Bowers, et al, 2014).…”

Training and Development Policy, Corporate Governance, and Firm Performance

“…However, there is skepticism about the link between training and the results criteria. Training is often criticised for being faddish, too expensive, not transferring to the job, and not improving the bottom line (Bowers 2017;Caudron 2002;Kraiger et al 2004;Wright and Geroy 2001).…”