Web resources for multilevel modelling

Compiled by Kelvyn Jones, Myles Gould and SV Subramanian.

Books and related downloads and materials

A selection here, but for a full list, go to useful books

(Back to top)

Reference lists about multilevel modelling

CMM publications

Wolfgang Ludwig-Mayerhofer's annotated references on multilevel modelling: www.lrz-muenchen.de/~wlm/wlmmule.htm

There is a structured list of references (based on different types of model) at the HLM website www.ssicentral.com/hlm/references.html#r7

(Back to top)

Software in general

The CMM maintains reviews of some of the packages available for multilevel modelling. These reviews contain syntax for fitting a range of multilevel models to example datasets.

If you want to see how a particular model can be fitted in particular software, there are the developing resources at UCLA www.ats.ucla.edu/stat/examples/

For those wishing to analyse longitudinal data, software instructions in a wide range of programs is provided by UCLA to accompany the textbook Singer JD, Willett JB, 2003 Applied longitudinal data analysis: modeling change and event occurrence, New York, Oxford University Press, at:
www.ats.ucla.edu/stat/examples/alda/

(Back to top)

Training associated with software packages

A growing amount of web-based (or at least downloadable) training materials are being developed. We have organized this section by the particular software that is being used, and rather arbitrarily separated commercial software from the freeware that follows

(Back to top)

Free software

There are a number of programs that are available at low or nil cost; some of these are general (like R), others are more specific but can have special features that make them particularly attractive; we have tried to identify these special features below. We have also pointed to some appropriate training resources.

(Back to top)

Useful software and macros

(Back to top)

Experts' Websites

Douglas Bates who developed the LME and NLME functions in R and S-plus has a website at www.stat.wisc.edu/~bates/bates.html

Bill Browne (who has made major contributions to the MCMC component of MLwiN) has a large number of downloadable papers at seis.bris.ac.uk/~frwjb/bill.html

David Draper has a lot of material about the Bayesian approach to hierarchical models on his web site: www.cse.ucsc.edu/~draper/

Tony Fielding has useful material on ordered categorical variables, endogeneity and instrumental variables including MLwiN macros on his web site

Andrew Gelman has lots of downloadable papers and presentations on multilevel modelling with a strong Bayesian flavour www.stat.columbia.edu/~gelman/

Harvey Goldstein, who is the instigator of the MLwiN software has a number of downloadable papers at his web site

Don Hedeker who has been behind the MIX set of programs has lecture transparencies and class notes on longitudinal analysis at Don Hedeker's web site

Joop Hox's web site has papers, programs and lectures to download at http://joophox.net

Alastair Leyland has extensively used multilevel modelling in public health

Bengt Muthen who is the developer of Mplus which allows multilevel factor analysis has a site at www.gseis.ucla.edu/faculty/muthen/muthen3.htm

Jason Newsom's multilevel web site has discussion of topics like centering, and how to distinguish between fixed and random effects www.upa.pdx.edu/IOA/newsom/mlrclass/default.htm

(Back to top)

David Rogosa has useful links to his course Education 260 on Popular Methods (including multilevel modeling, and causal inference) and Education 351 on Longitudinal analysis www.stanford.edu/~rag/

Steve Raudenbush's LAMMP website has publications and pre-prints and links to the projects he is currently working on www-personal.umich.edu/~rauden/

Tom Snijder's web site http://stat.gamma.rug.nl/multilevel.htm

Subramanian's research papers on using multilevel models in social epidemiology and health as well training resources related to the concepts and application of multilevel statistical methods can be found at http://www.hsph.harvard.edu/faculty/venkata-sankaranarayanan/.

The Office of Behavioral and Social Sciences Research (OBSSR) have some great interactive Multilevel Modeling Materials.

(Back to top)

Examples of multilevel modelling

For an interesting discussion about what multilevel models can (and cannot do) see the interchange at www.stat.columbia.edu

For the use of multilevel models in social network analysis, see stat.gamma.rug.nl/snijders/socnet.htm

For papers using multilevel modelling, see the Gallery of published examples, searchable by model type.

(Back to top)

Tutorials in MCMC estimation

MCMC estimation is increasingly being used to estimate complex models; there are number of sites with really helpful resources to get you started:

Simon Jackman's Estimation and Inference via Markov chain Monte Carlo: a resource for social scientists tamarama.stanford.edu/mcmc/

Jeff Gill's web site is a mine of information in this area, it includes some down-loadable chapters from his 2002 book Bayesian Methods for the Social and Behavioral Sciences which is to be thoroughly recommended http://jgill.wustl.edu/Site/Homepage.html

There is a useful website for David Spiegelhalter, Keith Abrams and Jonathan Myles (2003) Bayesian approaches to clinical trials and health-care evaluation, Wiley; it contains downloads for the examples that use WinBugs and Excel worksheets that allow simple analysis of odds-ratio and hazard ratio models on the basis of normal priors and likelihoods www.mrc-bsu.cam.ac.uk/bayeseval/

Sujjit Sahu's tutorial on MCMC www.maths.soton.ac.uk/staff/Sahu/utrecht/

Harold Lehmann Bayesian Communication prototypes Bayesian analysis on-line www.hopkinsmedicine.org/Bayes/PrimaryPages/Index.cfm

A Brief Introduction to Graphical Models and Bayesian Networks is to be found at http://www.cs.ubc.ca/~murphyk/Bayes/bayes.html

For software to determine sample-size requirements using prior opinion see Lawrence Joseph's Bayesian software site www.medicine.mcgill.ca/epidemiology/Joseph/

To keep up to date in this area, you can visit the MCMC preprint service www.statslab.cam.ac.uk/~mcmc/

(Back to top)

Causal analysis

Introductory sites

Christopher Winship's Counterfactual Causal Analysis in Sociology website provides a good introduction to developments in this area www.wjh.harvard.edu/~cwinship/cfa.html

Harvard School of Public Health PROGRAM ON CAUSAL INFERENCE in Epidemiology and Allied Sciences www.hsph.harvard.edu/causal/index.htm

(Back to top)

Experts on causal analysis

Judea Pearl has a large number of downloads of lectures and papers ayes.cs.ucla.edu/jp_home.html

Guido Imbens - ideas.repec.org/e/pim4.html

David Harding - www-personal.umich.edu/~dharding/

(Back to top)

Software for causal analysis with observational data

for R-based matching software which uses a wide range of techniques see Gary King's site sekhon.berkeley.edu/matching/

there is a SPPS syntax file for propensity scoring available at John Painter's site www.unc.edu/~painter/SPSSsyntax/propen.txt

facilities in R for Multivariate and Propensity Score Matching Software written by Jasjeet Sekhon sekhon.berkeley.edu/matching/

and Stata programs for ATT estimation based on propensity score matching www.sobecker.de/pscore.html

(Back to top)

Multilevel modelling and causal analysis

The "Columbia group on Bayesian statistics, multilevel modelling, causal inference, and social networks" have a site at www.stat.columbia.edu/~sam/MultilevelModeling/

There are pre-prints and publications on Steve Raudenbush's site - search for 'causal'.

Tony Fielding has material on endogeneity and instrumental variables including MLwiN macros, at Tony Fielding

(Back to top)

Missing data

Missing data are a persistent problem in social and other datasets. A standard technique for handling missing values efficiently is known as multiple imputation and the software REALCOM-IMPUTE is unique in that it has been designed to implement this procedure for 2-level data. Apart from being able to deal with 2-level data it can also handle properly categorical data, whether in the response or predictor variables in a model. An interface is provided with MLwiN that allows users to carry out the full procedure and fit their final model semi-automatically.

Further details about multiple imputation can be found at http://missingdata.lshtm.ac.uk. To download the software, which is freely available, go to Realcom - Imputation

Note: some of the documents on this page are in PDF format. In order to view a PDF you will need Adobe Acrobat Reader

(Back to top)