Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?
I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.
We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.
Our analysis of linear regression focuses on parameter estimates, z-scores, p-values and confidence levels. Rarely in regression do we see a discussion of the estimates and F statistics given in the ANOVA table above the coefficients and p-values.
And yet, they tell you a lot about your model and your data. Understanding the parts of the table and what they tell you is important for anyone running any regression or ANOVA model.
Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.
The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements. (more…)
What is a Confounder?
Confounder (also called confounding variable) is one of those statistical terms that confuses a lot of people. Not because it represents a confusing concept, but because of how it’s used.
(Well, it’s a bit of a confusing concept, but that’s not the worst part).
It has slightly different meanings to different types of researchers. The definition is essentially the same, but the research context can have specific implications for how that definition plays out.
If the person you’re talking to has a different understanding of what it means, you’re going to have a confusing conversation.
Let’s take a look at some examples to unpack this.
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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The expression “can’t see the forest for the trees” often comes to mind when reviewing a statistical analysis. We get so involved in reporting “statistically significant” and p-values that we fail to explore the grand picture of our results.
It’s understandable that this can happen. We have a hypothesis to test. We go through a multi-step process to create the best model fit possible. Too often the next and last step is to report which predictors are statistically significant and include their effect sizes.