This course introduces methods for modelling multilevel data structures, for example pupils nested within schools, or individuals nested within neighbourhoods. Starting with basic concepts in multilevel modelling and the fundamentals of random intercept and random coefficient models, the course will then cover more advanced topics including: nonlinear models for binary responses, repeated measures, multivariate models, cross-classified models, and spatial data structures.The course will use MLwiN.

Keywords

Multilevel, mixed effects, random effects, clustering

Zielgruppe

Participants will find the course useful if they

Are using data with a clustered data structure

Want to know more about the basic principles of multilevel models

Are interested in more advanced topics in multilevel modelling, including MCMC approaches for cross-classified and spatial data structures.

Lernziel

By the end of the course participants will

Be able to understand the ideas behind multilevel modelling and when their use is appropriate.

Be able to fit a range of random intercept and coefficient models to continuous and binary response data

Have a general understanding of MCMC methods and their application to multilevel modelling

Be able to deal with a range of complex data structures including hierarchical, cross-classified, repeated measures, multivariate, and spatial

Voraussetzungen

A basic understanding of statistical concepts

Zeitplan

Zeitplan

Day 1: Introducing multilevel models

08.00-10.00

Registration and Coffee

10.00-11.30

Session 1: Multilevel data structures

11.45-13.00

Computer exercise 1: Introduction to MLwiN

13.00-14.00

Lunch

14.00-15.00

Session 2: Comparing groups using multilevel models

15.15-16.00

Session 3: Random intercept models

16.15-17.45

Computer exercise 2: Random intercept models

Literature

Rasbash, J., Steele, F., Browne, W J., and Goldstein, H. (2017) A user's guide to MLwiN. Version 3.01. Chapter 1-3.

Hox, J J. (2010) Multilevel analysis: Techniques and applications. Second edition. Routledge. Chapter 1-3.

Kreft, I., and De Leeuw, J. (1998) Introducing multilevel modeling. Sage, Chapter 1.

Snijders, T A B., and Bosker, R J. (2012) Multilevel analysis: An introduction to basic and advanced multilevel modeling. Second edition. Sage.Chapter 1-4.

Finch, W H., Bolin, J E., and Kelley, K (2014) Multilevel modeling using R. CRC press. Chapter 1-2.

Session 5: Context effects and cross-level interactions

14.30-15.30

Session 6: Diagnostics, complex level-1 variance

15:45-17:00

Computer exercise 4: Context effects and interactions

Literature

Rasbash, J., Steele, F., Browne, W J., and Goldstein, H. (2017) A user's guide to MLwiN. Version 3.01. Chapter 4, 6-7, 15.

Kreft, I., and De Leeuw, J. (1998) Introducing multilevel modeling. Sage. Chapter 2-3.

Snijders, T A B., and Bosker, R J. (2012) Multilevel analysis: An introduction to basic and advanced multilevel modeling. Second edition. Sage. Chapter 5-7.

Rasbash, J., Steele, F., Browne, W J., and Goldstein, H. (2017) A user's guide to MLwiN. Version 3.01. Chapter 9, 13-14.

Hox, J J. (2010) Multilevel analysis: Techniques and applications. Second edition. Routledge. Chapter 5-6, 10.

Snijders, T A B., and Bosker, R J. (2012) Multilevel analysis: An introduction to basic and advanced multilevel modeling. Second edition. Sage.Chapter 15-17.

Finch, W H., Bolin, J E., and Kelley, K (2014) Multilevel modeling using R. CRC press. Chapter 5, 7.

Day 4: MCMC methods for more complex multilevel structures

09.00-09.15

Recap on day 3

09.15-10.45

Session 9: An introduction to MCMC methods

11.00-12.30

Computer exercise 7: Multilevel models with MCMC approaches

12.30-13.30

Lunch

13.30-15.00

Session 10: Cross-classified and spatial models

15.15-17.00

Computer exercise 8: MCMC methods for X-Class and spatial

Literature

Browne, W J. (2017) MCMC estimation in MLwiN. Version 3.01. Chapter 1-6, 15, 17.

Hox, J J. (2010) Multilevel analysis: Techniques and applications. Second edition. Routledge. Chapter 13.

Snijders, T A B., and Bosker, R J. (2012) Multilevel analysis: An introduction to basic and advanced multilevel modeling. Second edition. Sage. Chapter 12.

Finch, W H., Bolin, J E., and Kelley, K (2014) Multilevel modeling using R. CRC press. Chapter 9.

Day 5: Additional MCMC models

09.15-10.45

Session 11: Missing data and Level 1 variance models

11.00-12.30

Computer exercise 9: Missing data adjustments in MLwiN

12.30-13.30

Lunch

13.30-14.30

Session 8: Wrap up… Q and A/other developments

14.30

Close. Opportunity to discuss particular projects.

Literature

Browne, W J. (2017) MCMC estimation in MLwiN. Version 3.01. Chapter 9, 18.

Hox, J J. (2010) Multilevel analysis: Techniques and applications. Second edition. Routledge. Chapter 12.

Snijders, T A B., and Bosker, R J. (2012) Multilevel analysis: An introduction to basic and advanced multilevel modeling. Second edition. Sage. Chapter 8.

Literaturempfehlungen

Literaturempfehlungen

Rasbash, J., Steele, F., Browne, W J., and Goldstein, H. (2017) A user's guide to MLwiN. Version 3.01.

Hox, J J. (2010) Multilevel analysis: Techniques and applications. Second edition. Routledge.

Snijders, T A B., and Bosker, R J. (2012) Multilevel analysis: An introduction to basic and advanced multilevel modeling. Second edition. Sage.

Goldstein, H. (2011) Multilevel statistical models. 4^{th} Edition. Wiley.

Finch, W H., Bolin, J E., and Kelley, K (2014) Multilevel modeling using R. CRC press.

Kreft, I., and De Leeuw, J. (1998) Introducing multilevel modeling. Sage.

Referenteninformationen - Prof. Ian Brunton-Smith, PhD

Referenteninformationen - Prof. Ian Brunton-Smith, PhD

Ian Brunton-Smith is a professor of Quantitative Criminology at the University of Surrey. His work regularly incorporates multilevel models to explore the impact of clustering on social outcomes, as well as effects of interviewers on survey estimates. He has taught short courses in multilevel modelling for a number of years, including courses for the National Centre for Research Methods.