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Generalized Linear Models for Between Su...

There aren’t many good, easy-to-understand resources on Generalized Linear Models. This is a shame, because they are usually a substantial improvement over more conventional ANOVA analyses, because they can much better account for violations of the normality assumption. Check out some tutorial slides I created here: They only cover between-subjects designs. Maybe some time I’ll also make one for generalized mixed models, which take the best of GLiM and multilevel models and combine them into...
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Basics of SEM Tutorial

Attached are some slides that I’ve used to teach my PSYO 6003 Multivariate Statistics students the basics of structural equation modelling, which may be of some use to people using it for the first time. Check them out here:
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Multicolinearity: Why you should care an...

Multicolinearity: Why you should care and what to do about it Multicolinearity is a problem for statistical analyses. This large, unwieldy word essentially refers to situations where your predictor variables so highly correlated with one another, they become redundant. Generally speaking, this is a problem because it will increase your Type II error rate (i.e., false negatives). In the most severe cases, multicolinearity can produce really bizarre results that defy logic. For example, the direction of relationships can sometimes reverse (e.g., a positive relationship...