Tag: statistical training

Chris Daman 0
When is a Multilevel Model not appropriate?

I recently received this interesting question regarding Multilevel Models after one of my last blog posts: Question: Can you tell me when a multilevel-model is not appropriate? I have data that by design is clustered but the random intercept in the null model is not significant. I have seen advice

Chris Daman 0
Multilevel Models Part 2: What is a Multilevel Model?

Multilevel models (also called hierarchical linear models) are used to analyze clustered or grouped data, as well as longitudinal or repeated measures data. Consider the simple scenario shown below, where Y is continuous and is shown as a function of a continuous predictor variable, X (which has been standardized). If

Chris Daman 5
Multilevel Models Part 1: Do I Need a Multilevel Model?

If you have data where the observations are not independent due to nesting or clustering, you may need a multilevel model. Another scenario that would require a multilevel model is if you have data where observations have been gathered multiple times on the same subject (a.k.a., longitudinal data or repeated

Chris Daman 0
ESTIMATE Statements - the final installment

FINALLY…the simplest ESTIMATE statements to write are for continuous variables not involved in interactions or higher order terms. Consider a data set containing the 2004 SAT scores for each of the 50 states. The file includes the combined math and verbal SAT scores (TOTAL), the state (STATE) and the percent

Chris Daman 0
"Easy button" for ESTIMATE statements

My previous blog demonstrated the most difficult type of ESTIMATE statement to write—a two-way (or higher) ANOVA with interactions. An "easy button" for ESTIMATE statement comes by having a simpler model. Models with only main effects and no interactions make writing ESTIMATE statements straightforward.  Consider first a one-way ANOVA. A

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