Learty Letroy Shaw Ph D

Handbook to Simple Research Praxis Learty Letroy Shaw Ph D

Terms such as research, data, evaluation, analysis, and assessment can be overwhelming and approached with hesitation. Such apprehension is usually a result of past experience with tasks such as self-studies, accreditation, program and performance evaluations, and policy analysis. However, data can become the champion of decision-making instead of a laborious checklist when a workflow is created for such research-based tasks. This book is by no means intended to replace the landmark developments of scholars such as Best and Khan, Cooksy, Creswell, Crotty, Fitzpatrick, Lincoln and Guba, Marshall and Rossman, Neuman, Patton, Pawson, Saldaña, Weiss, Warner, and Yin. Neither is it meant to contend with, or replace, the vast body of literature that explains the complex details regarding theory, the intricacies of research design, the differences in research techniques, the numerous methods of analysis, and other complicated details involved in conducting research. Instead, this handbook provides a basic understanding of research, and serves as a simple and practical framework for organizing research. Much of the information presented in this handbook is grounded in the literature of the noteworthy scholars previously mentioned. This handbook is a short synoptic and supplemental piece of literature that supports those existing bodies of work. Nonetheless, it does provide a direct and simple comprehensive praxis to research, evaluation, and analysis by creating a sequential workflow and visual model that guides practitioners, researchers, and decision-makers through the research and analysis process. Users are directed to compose succinct questions aligned with, and targeting, the gap (problem, need, or desired outcomes) based on the status quo, and then select right data (which may be empirically qualitative or quantifiable) aimed at addressing the gap. Finally, users are guided to perform a simple quality check for result relevancy and what I have coined as data disparity.