Oops—you ran the analysis you planned to run on your data, carefully chosen to answer your research question, but your residuals aren’t normally distributed.
Maybe you’ve tried transforming the outcome variable, or playing around with the independent variables, but still no dice. That’s ok, because you can always turn to a non-parametric analysis, right?
Well, sometimes.
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You probably learned about the four levels of measurement in your very first statistics class: nominal, ordinal, interval, and ratio.
Knowing the level of measurement of a variable is crucial when working out how to analyze the variable. Failing to correctly match the statistical method to a variable’s level of measurement leads either to nonsense or to misleading results.
Choosing statistical software is part of The Fundamentals of Statistical Skill and is necessary to learning a second software (something we recommend to anyone progressing from Stage 2 to Stage 3 and beyond).
There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box?
Ratios are everywhere in statistics—coefficient of variation, hazard ratio, odds ratio, the list goes on. You see them reported in the literature and in your output.
You comment on them in your reports. You even (kinda) understand them. Or, maybe, not quite?
Please join Elaine Eisenbeisz as she presents an overview of the how and why of various ratios we use often in statistical practice.
Meta-analysis is the quantitative pooling of data from multiple studies. Meta-analysis done well has many strengths, including statistical power,
precision in effect size estimates, and providing a summary of individual studies.