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Abstract
A quantitative research study collects numerical data that must be analyzed to help draw the study’s conclusions. Teaching quantitative data analysis is not teaching number crunching, but teaching a way of critical thinking for how to analyze the data. The goal of data analysis is to reveal the underlying patterns, trends, and relationships of a study’s contextual situation. Learning data analysis is not learning how to use statistical tests to crunch numbers but is, instead, how to use those statistical tests as a tool to draw valid conclusions from the data. Three major pedagogical goals that must be taught as part of learning quantitative data analysis are the following: (a) determining what questions to ask during all phases of a data analysis, (b) recognizing how to judge the relevance of potential questions, and (c) deciding how to understand the deep-level relationships within the data.
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Author Biography
Michael J. Albers is an STC Fellow and a professor at East Carolina University. He is the founder and chair of the annual Symposium on Communicating Complex Information (SCCI). His primary teaching areas are editing and information design. Before earning his PhD, he worked for 10 years as a technical communicator and performing interface design. His research interests include communication of complex information and human-information interaction.
