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Quantitative Data Analysis: I’ve Got Data, How Do I Get Answers? 

Quantitative Data Analysis: I’ve Got Data, How Do I Get Answers?
Chapter:
Quantitative Data Analysis: I’ve Got Data, How Do I Get Answers?
Author(s):

Ian M. MacFarlane

, Patricia McCarthy Veach

, and Bonnie S. LeRoy

DOI:
10.1093/med/9780199359097.003.0007
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date: 25 February 2020

This chapter opens with an overview of common statistics terms (e.g., degrees of freedom, effect size) and a review of levels of measurement. Cleaning a dataset for analysis is discussed in detail, with an emphasis on how to handle missing data. Descriptive statistics (e.g., mean, standard deviation, correlation) are reviewed, with tables to help readers know which statistics are appropriate. A flow chart walks readers through the most common inferential statistics tests to facilitate proper analysis of results. Common analyses (e.g., t-test, ANOVA, multiple regression) are outlined, including when to use them, assumptions required by the test, effect size statistics, and how to write up the results. More advanced (e.g., structural equation modeling, multilevel modeling, multivariate) and non-parametric (e.g., chi-square, Kruskal-Wallace) analyses are introduced so that readers can identify when they might be useful. The chapter concludes with a discussion of when and how to contact a statistical consultant.

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