# 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
• 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.

• 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.