Linear Mixed Effects Models Concepts

This video introduces linear mixed effects models, explaining their components, such as fixed and random effects. It uses a weight loss study to compare linear regression and mixed effects models, highlighting the differences in p-values and the importance of random intercepts. The video also discusses the advantages of mixed effects models over repeated measures ANOVA, including handling missing data and estimating coefficients like slopes.

What is the primary focus of linear mixed effects models?

• To analyze neither fixed nor random effects

• To analyze both fixed and random effects

• To analyze only random effects

• To analyze only fixed effects

What do fixed effects in a model represent?

• Parameters that vary between groups

• Parameters that do not vary

• Parameters that are always one

• Parameters that are always zero

In the diet study example, what was the estimated average weight loss per week using linear regression?

• 5.0 kilos

• 3.1 kilos

• 2.5 kilos

• 4.0 kilos

Why might a linear mixed effects model be preferred over simple linear regression in the diet study?

• It always gives a higher p-value

• It ignores individual differences

• It provides a single estimate for all individuals

• It accounts for variations between individuals

What is a key feature of random intercept models?

• All individuals have the same intercept

• Intercepts are always one

• Each individual has a unique intercept

• Intercepts are always zero

How does a mixed effects model treat subjects compared to a multiple linear regression model?

• As fixed effects

• As random effects

• As neither fixed nor random effects

• As both fixed and random effects

What is an advantage of linear mixed effects models over repeated measures ANOVA?

• They require categorical variables

• They require continuous variables

• They can handle missing data

• They cannot handle missing data

What assumption is shared by both linear regression and linear mixed effects models?

• Explanatory variables should be non-linearly related to the response variable

• Residuals should be uniformly distributed

• Explanatory variables should be categorical

• Residuals should be normally distributed