The Eight Steps of Data Analysis: A Graphical Framework to Promote Sound Statistical Analysis

Data analysis is a risky endeavor, particularly among people who are unaware of its dangers. According to some researchers, “statistical conclusions validity” threatens all research subjected to the dark arts of statistical magic. Although traditional statistics classes may advise against certain pr...

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Vydáno v:Perspectives on psychological science Ročník 15; číslo 4; s. 1054 - 1075
Hlavní autor: Fife, Dustin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Los Angeles, CA SAGE Publications 01.07.2020
SAGE PUBLICATIONS, INC
Témata:
Data analysis
Normality
Quantitative analysis
Researcher subject relations
Researchers
Statistical analysis
Statistical Data
Statistics
Warnings
confirmatory data analysis
statistical assumptions
p-hacking
graphical data analysis
fishing
NHST
ISSN:1745-6916, 1745-6924, 1745-6924
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Shrnutí:Data analysis is a risky endeavor, particularly among people who are unaware of its dangers. According to some researchers, “statistical conclusions validity” threatens all research subjected to the dark arts of statistical magic. Although traditional statistics classes may advise against certain practices (e.g., multiple comparisons, small sample sizes, violating normality), they may fail to cover others (e.g., outlier detection and violating linearity). More common, perhaps, is that researchers may fail to remember them. In this article, rather than rehashing old warnings and diatribes against this practice or that, I instead advocate a general statistical-analysis strategy. This graphic-based eight-step strategy promises to resolve the majority of statistical traps researchers may fall into—without having to remember large lists of problematic statistical practices. These steps will assist in preventing both false positives and false negatives and yield critical insights about the data that would have otherwise been missed. I conclude with an applied example that shows how the eight steps reveal interesting insights that would not be detected with standard statistical practices.
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ISSN:1745-6916
1745-6924
1745-6924
DOI:10.1177/1745691620917333