Quantitative Data Analysis—In the Graduate Curriculum. (April 2017)

• Record Type: Journal Article
• Authors: Albers, Michael J.
• Other Names: Cargile Cook Kelli guest-editor.; Rickly Rebecca guest-editor.
• Is Part Of: Journal of technical writing & communication. Volume 47:Number 2(2017)
• Page Start: 215
• Page End: 233
• Publication Date: 2017-04
• Subjects: research methods -- quantitative analysis -- statistics data analysis; Technical writing -- Periodicals; 808.0666
• DOI: 10.1177/0047281617692067
• Languages: English
• ISSNs: 0047-2816

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.