Statistics is, to a large extent, a science of comparison. You are trying to test whether one group is bigger, faster, or smarter than another.
You do this by setting up a null hypothesis that your two groups have equal means or proportions and an alternative hypothesis that one group is “better” than the other. The test has interesting results only when the data you collect ends up rejecting the null hypothesis.
But there are times when the interesting research question you’re asking is not about whether one group is better than the other, but whether the two groups are equivalent.
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Transformations don’t always help, but when they do, they can improve your linear regression model in several ways simultaneously.
They can help you better meet the linear regression assumptions of normality and homoscedascity (i.e., equal variances). They also can help avoid some of the artifacts caused by boundary limits in your dependent variable — and sometimes even remove a difficult-to-interpret interaction.
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Interpreting regression coefficients can be tricky, especially when the model has interactions or categorical predictors (or worse – both).
But there is a secret weapon that can help you make sense of your regression results: marginal means.
They’re not the same as descriptive stats. They aren’t usually included by default in our output. And they sometimes go by the name LS or Least-Square means.
And they’re your new best friend.
So what are these mysterious, helpful creatures?
What do they tell us, really? And how can we use them?
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Ah, logarithms. They were frustrating enough back in high school. (If you even got that far in high school math.)
And they haven’t improved with age, now that you can barely remember what you learned in high school.
And yet… they show up so often in data analysis.
If you don’t quite remember what they are and how they work, they can make the statistical methods that use them seem that much more obtuse.
So we’re going to take away that fog of confusion about exponents and logs and how they work. (more…)
Structural Equation Modelling (SEM) increasingly is a ‘must’ for researchers in the social sciences and business analytics. However, the issue of how consistent the theoretical model is with the 
data, known as model fit, is by no means agreed upon: There is an abundance of fit indices available – and wide disparity in agreement on which indices to report and what the cut-offs for various indices actually are. (more…)
Have you ever experienced befuddlement when you dust off a data analysis that you ran six months ago?
Ever gritted your teeth when your collaborator invalidates all your hard work by telling you that the data set you were working on had “a few minor changes”?
Or panicked when someone running a big meta-analysis asks you to share your data?
If any of these experiences rings true to you, then you need to adopt the philosophy of reproducible research.
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