# Week Learning Objectives

By the end of this module, you will be able to

- Describe the problems of using a regular multilevel model for a
binary outcome variable
- Write model equations for multilevel logistic regression
- Estimate intraclass correlations for binary outcomes
- Plot model predictions in probability unit

## Task Lists

- Review the resources (lecture videos and slides)
- Complete the assigned readings
- Snijders & Bosker ch 17.1–17.4

- Attend the Thursday session and participate in the class
exercise
- Complete peer review for two of your peers’ postings (due Monday
November 15, end of day)
- Complete Homework 9

# Lecture

## Slides

Note that in some of the videos below the
Bayeisan analyses were used; however, for the class this year we will
stay with frequentist analyses. The results and interpretations are
basically; just note some differences in the terminology.

You can view and download the slides here: PDF

## Logistic MLM

Check your learning: Which of the following is a binary variable?

### Example Data

Check your learning: Which of the following is **not** a
reason to use a logistic model?

## Elements of Logistic MLM

Check your learning: A logistic model assumes that the outcome
follows

Check your learning: A log odds of 0.5 corresponds to a probability
of

If you have trouble understanding what “odds” and “log odds” are, you
are not alone. This video may give you a better idea: https://www.youtube.com/watch?v=ARfXDSkQf1Y

## Unconditional Model

### Model equations

Check your learning: In a logistic model predicting whether a person
reported a daily stressor, the coefficient of age was -0.5. The
interpretation is that

## Adding Predictors

## Generalized Linear Mixed
Model (GLMM)

Check your learning: A researcher wants to compare the proportion of
minority hires in the past year across departments and schools. The
total number of hires for each department is known. Which model is the
most appropriate?