Multilevel and Mixed models are essentially the same analysis. But they use different vocabulary, different notation, and approach the analysis considerations in different ways.
[Read more…] about Member Training: Translating Between Multilevel and Mixed Models
Stage 3
Member Training: Analyzing Likert Scale Data
Is it really ok to treat Likert items as continuous? 
And can you just decide to combine Likert items to make a scale? Likert-type data is extremely common—and so are questions like these about how to analyze it appropriately. [Read more…] about Member Training: Analyzing Likert Scale Data
Member Training: A Gentle Introduction to Bootstrapping
Bootstrapping is a methodology derived by Bradley Efron in the 1980s that provides a reasonable approximation to the sampling distribution of various “difficult” statistics. Difficult statistics are those where there is no mathematical theory to establish a distribution.
[Read more…] about Member Training: A Gentle Introduction to Bootstrapping
When Linear Models Don’t Fit Your Data, Now What?
When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.
Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.
[Read more…] about When Linear Models Don’t Fit Your Data, Now What?
The Difference Between an Odds Ratio and a Predicted Odds
When interpreting the results of a regression model, the first step is to look at the regression coefficients. Each term in the model has one. And each one describes the average difference in the value of Y for a one-unit difference in the value of the predictor variable, X, that makes up that term. It’s the effect size statistic for that term in the model. [Read more…] about The Difference Between an Odds Ratio and a Predicted Odds
Member Training: Heterogeneity in Meta-analysis
Meta-analysis allows us to synthesize the results of separate studies. The goal is to assess the mean effect size and also heterogeneity – how much the effect size varies across studies. [Read more…] about Member Training: Heterogeneity in Meta-analysis