# Basic and Advanced Multilevel Modeling with Stata

**Prerequisites (knowledge of topic)**

A graduate statistics course, at an introductory level, with exposure to regression analysis.

**Hardware**

Provided by host institution, however it is highly recommended to bring your own laptop

**Software**

Provided by host institution

- Stata, v. 15 (experience with Stata is not required, but will be helpful)

**Course content**

Day 1 (Morning Session): A brief introduction to Stata

- What is Stata?
- Resources for working with Stata
- Why use Stata?
- A data set to illustrate some data management capabilities of Stata
- The Stata working windows
- Exploring a data set
- Examining variables
- Putting order into a data file
- Assigning labels and variable names
- Dealing with missing values – a first essential step
- Modifying existing and creating new variables
- Transforming variables
- A general approach to variable transformation
- Getting help.

Day 1 (afternoon session): Fitting single-level regression models using Stata

- Data set and research question
- Preliminary analyses
- Single-level regression analysis with Stata
- Plotting residuals against predictors
- Plotting residuals against fitted (predicted) values
- Plotting standardized residuals.

Day 2 (morning session): Why do we need multilevel and mixed models?

- What is multilevel modeling, why can’t we do without it, and how come aggregation and disaggregation do not do the job?
- Examples of nested data and the hallmark of

multilevel modeling - Another important instance of multilevel modeling
- Aggregation and disaggregation of variable scores
- Analytic benefits of multilevel modeling.
- The beginnings of multilevel modeling – why what we already know about regression analysis will be so useful
- A brief review of regression analysis
- Multilevel models as sets of regression equations
- An illustrative example of multilevel modeling.

Day 2 (afternoon session): The intra-class correlation coefficient and its estimation

- The fully unconditional two-level model and definition of the intraclass

correlation coefficient (ICC) - Point and interval estimation of the ICC using Stata

Day 3 (morning session): How many levels? – Proportion ofthird level variance and its evaluation

- Proportion third level variance
- The fully unconditional three-level model
- Point and interval estimation of proportion third level variance using Stata.

Day 3 (afternoon session): Robust modeling of lower-level variable relationships in the

presence of clustering effect

- What is robust modeling in the presence of nesting effects?
- Robust modeling of hierarchical data using Stata.

Day 4 (morning session): Mixed effects models (mixed models)

- What are mixed models, what are they made of, and why are they useful?

- An illustration of the difference between fixed

and random effects - Examples of mixed modeling frameworks
- Mixed models with continuous response variables.
- Random intercept models

- Fitting a random intercept model with Stata
- Model adequacy evaluation
- Between- and within-estimators and when to use which
- Random regression models

- An instructive example and the restricted

maximum likelihood (REML) method - Random intercept and slope model
- Multiple random slopes
- Fixed effects, random effects, and total effects
- Numerical issues
- Nested levels – conditional three-level mixed models

Day 4 (afternoon session): Mixed models with discrete responses

- Why do we need these models?
- A few important statistical facts
- The generalized linear model (GLIM)
- Random intercept models with discrete outcomes
- Random regression models with discrete outcomes
- Model choice
- Appendix – Cross-classification and crossed effects multilevel models.

Day 5 (morning session): Longitudinal multilevel modeling

- Introduction
- Multilevel modeling of longitudinal data
- Using Stata to fit unconditional and conditional growth curve models (cross-sectional time series).

Day 5 (afternoon session): Extensions, Limitations, Conclusion and Outlook.

- What we could not cover in this course – your next steps.
- Extensions of multilevel models
- Limitations of multilevel modeling
- Conclusion and outlook.

**Literature**

Mandatory:

- Snijders, T. A. B., & Bosker, R. J. (2013).
*Multilevel analysis. An introduction to basic and advanced multilevel modeling.*Thousand Oaks, CA: Sage.

Supplementary / voluntary:

- Rabe-Hesketh, S., & Skrondal, A. (2012).
*Multilevel and longitudinal modeling with Stata.*College Station, TX: Stata Press.

Mandatory readings before course start:

- Raykov, T. (2019).
*A course in multilevel modeling*. Lecture notes. Michigan State University, East Lansing, Michigan, USA.

** **

**Examination part**

Take home assignment, to be submitted within 3 weeks upon course completion.

Participants are allowed any literature they can find, incl. the lecture notes volume to be provided in pdf form to them before course commences.

**Supplementary aids**

Course participants are allowed to use any literature they can access, incl. the lecture notes.

**Examination content**

Multilevel modeling with missing data, violations of missing at random, and accounting for clustering effects.

**Literature**

Raykov, T. (2019). *A course in multilevel modeling. *Lecture notes. Michigan State University, East Lansing, Michigan, USA.