My experience (like that of most people) is limited to a number of software packages. HLM is very easy to use. BUGS and GLLAMM (in Stata) are very flexible and cover the widest range of models, but both are challenged by large and complex data sets which can take a long time to estimate. MLwiN can handle large data sets very efficiently and can estimate models in likelihood and Bayesian (MCMC) mode- this allows goodish likelihood estimates to be used as starting values for the MCMC estimation. The MLwiN software also has lots of post estimation procedures to help interpret the results. MIXREG is very efficient and is very useful for discrete outcomes.

The Centre for Multilevel Modelling at the University of Bristol has recognised the problems with this and has started a large programme of work (sponsored by the UK ESRC) to provide for “inter-operability” – that is the ability to work across software platforms. The fruits of this so far are

1: runmlwin: Runs MLwiN from within Stata

http://www.bristol.ac.uk/cmm/software/runmlwin/

2: R2MLwiN: runs MLwiN from within R

http://www.bristol.ac.uk/cmm/software/r2mlwin/

And much more ambitiously

3: Stat-JR

“is a brand new statistical software system: it constitutes a very different data analysis experience, featuring: an interface with a range of other statistical software packages, circumventing the need to learn software-specific techniques each time functionality of a new package required, but also providing tools to help teach software-specific knowledge to those wishing to learn; its own in-house MCMC based estimation engine (eStat) for complex data modelling (including multilevel models); open source templates allowing users to write their own Stat-JR functions; an eBook interface providing an interactive way of reporting and disseminating science, and an innovative tool for teaching statistics” Put simply you can specify a model in this environment and then ask for it to create syntax in a very large range of different software, as well as estimate with its own MCMC procedures .

This has just been released; see

http://www.bristol.ac.uk/cmm/software/statjr/index.html

I prefer using JAGS in R (rjags) to fit any multi-level models as I'm more comfortable with being able to specify whether the random effects are iid or exchangeable. That's coming from a Bayesian perspective, though. I don't know of any published papers that compare the different estimators.

Dear, I just found a very nice free online course:

http://www.cmm.bris.ac.uk/lemma/

They have practical examples for Stata, MLWin and R. There is also some discussion about the estimation procedures. Of course, it seem hard to say what estimator is best...at least, we know how to make results even more similar.

For R, they use library(lme4), probably Samuel is right. I have a problem with stata because I would like to set matrix as exchangeable (the default is independent)

Thanks, all.

My experience (like that of most people) is limited to a number of software packages. HLM is very easy to use. BUGS and GLLAMM (in Stata) are very flexible and cover the widest range of models, but both are challenged by large and complex data sets which can take a long time to estimate. MLwiN can handle large data sets very efficiently and can estimate models in likelihood and Bayesian (MCMC) mode- this allows goodish likelihood estimates to be used as starting values for the MCMC estimation. The MLwiN software also has lots of post estimation procedures to help interpret the results. MIXREG is very efficient and is very useful for discrete outcomes.

The Centre for Multilevel Modelling at the University of Bristol has recognised the problems with this and has started a large programme of work (sponsored by the UK ESRC) to provide for “inter-operability” – that is the ability to work across software platforms. The fruits of this so far are

1: runmlwin: Runs MLwiN from within Stata

http://www.bristol.ac.uk/cmm/software/runmlwin/

2: R2MLwiN: runs MLwiN from within R

http://www.bristol.ac.uk/cmm/software/r2mlwin/

And much more ambitiously

3: Stat-JR

“is a brand new statistical software system: it constitutes a very different data analysis experience, featuring: an interface with a range of other statistical software packages, circumventing the need to learn software-specific techniques each time functionality of a new package required, but also providing tools to help teach software-specific knowledge to those wishing to learn; its own in-house MCMC based estimation engine (eStat) for complex data modelling (including multilevel models); open source templates allowing users to write their own Stat-JR functions; an eBook interface providing an interactive way of reporting and disseminating science, and an innovative tool for teaching statistics” Put simply you can specify a model in this environment and then ask for it to create syntax in a very large range of different software, as well as estimate with its own MCMC procedures .

This has just been released; see

http://www.bristol.ac.uk/cmm/software/statjr/index.html

I have added two volumes on Stat-JR to research gate

Book: An Advanced User's Guide to Stat-JR version 1.0.0 Programming and Documentation

https://www.researchgate.net/publication/259640684_An_Advanced_User%27s_Guide_to_Stat-JR_version_1.0.0_Programming_and_Documentation_by?ev=prf_pub

and

A Beginner's Guide to Stat-JR's TREE interface version 1.0.0 Programming and Documentation

https://www.researchgate.net/publication/259640463_A_Beginner%27s_Guide_to_Stat-JR%27s_TREE_interface_version_1.0.0_Programming_and_Documentation_by?ev=prf_pub

Book An Advanced User's Guide to Stat-JR version 1.0.0 Programmin...

Book A Beginner's Guide to Stat-JR's TREE interface version 1.0.0...

it is possible to do HLM in SPSS???

Amir

The answer is Yes but there are some things it does not give you

see previous postings to

https://www.researchgate.net/post/Has_anyone_used_SPSS_to_do_multilevel_analysis

I suggest Mplus.