Quantitative Data Analysis—In the Graduate Curriculum
| Autor: | Michael J. Albers |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Communication
05 social sciences 050301 education 02 engineering and technology Crunch Education Critical thinking Quantitative analysis (finance) 020204 information systems Quantitative research Pedagogy Curriculum mapping 0202 electrical engineering electronic engineering information engineering Mathematics education Relevance (information retrieval) Psychology 0503 education Curriculum Statistical hypothesis testing |
| Zdroj: | Journal of Technical Writing and Communication. 47:215-233 |
| ISSN: | 1541-3780 0047-2816 |
| DOI: | 10.1177/0047281617692067 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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