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.
Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately.
Unfortunately, many of the graphs we see today confuse, mislead, or deceive the reader.
How do you know your variables are measuring what you think they are? And how do you know they’re doing it well?
The last, and sometimes hardest, step for running any statistical model is writing up results.
As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.
Any good statistical report includes enough information that someone else could replicate your results with your data.
Effect size statistics are required by most journals and committees these days — for good reason.
They communicate just how big the effects are in your statistical results — something p-values can’t do.
But they’re only useful if you can choose the most appropriate one and if you can interpret it.
This can be hard in even simple statistical tests. But once you get into complicated models, it’s a whole new story. (more…)
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…)