For linear mixed models I'd recommend R. It is free, open source and more flexible than SPSS. The package is standard these days lme4 for most common multilevel models with additional flexibility from packages such as ordinal (for ordinal models) or brms (for Bayesian models including multilevel ones).

SAS, SPSS ???

Just any software ( except R scripts) which can perform this operation.

I recommend SPSS

Thank you!

For linear mixed models I'd recommend R. It is free, open source and more flexible than SPSS. The package is standard these days lme4 for most common multilevel models with additional flexibility from packages such as ordinal (for ordinal models) or brms (for Bayesian models including multilevel ones).

Thank you, Thom!

Hello Olga,

I recommend R for mixed models, but you can also use STATA 15, very easy to use and also that version has a module to make them under the Bayesian approach. Greetings.

Thank you for the response, Alejandro!

In general terms you may want to look at

http://www.bristol.ac.uk/cmm/software/mlwin/mlwin-resources.html#mlmbkgrd

MLwiN is designed to be very fast and to estimate complex models:

there is a free trial version

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

I should also add that you can use MLwiN from inside Stata and R if you have it on your machine.

http://www.bristol.ac.uk/cmm/software/mlwin/mlwin-resources.html#interop

Thank you, Kelvyn!

I recommend SPSS

R without any doubt. There are many packages you can use. Also, there are many youtube tutorial on r packages for LMM analysis.

https://www.r-bloggers.com/linear-mixed-models-in-r/

https://www.youtube.com/watch?v=slEWKWOh990

There is a real problem (when I last looked) with the SPSS implementation of mixed models in that it does not provide a ready means of estimating the higher-level random effects - the so called shrunken residuals. For many research problems this is vital.

However, Chris Charlton has now completed the conversion of some of our online Lemma training materials into SPSS. Two Modules have been completed.

• Module 3 on using Multiple regression this can be used as a pre-cursor to the multilel part of the course

• Module 5: Introduction to Multilevel Modelling.

Importantly the latter show you how to use SPSS syntax to calculate higher- level residuals. The material separates out concepts from implementation.

You can freely use these materials to teach yourself, and/or use them in courses you are teaching.

The course already has material for MLwiN, Stata and R.

http://www.bristol.ac.uk/cmm/news/57.htmlhttp://www.bristol.ac.uk/cmm/news/57.html

http://www.bristol.ac.uk/cmm/learning/online-course/index.html

MLwiN can estimate via likelihood procedures, MCMC and iterated bootstrap.

I would also recommend R, except from lme4 check out lmerTest as well.

Thank you, Marzena!

Just to be clear about my previous post, you can run MLwiN from within R

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

The reasons for doing so is the ability to fit large and complex models very rapidly, see

Zhengzheng Zhang, Richard M. A. Parker, Christopher M. J. Charlton, George Leckie, William J. Browne (2016). R2MLwiN: A Package to Run MLwiN from within R. Journal of Statistical Software, 72(10), 1-4

https://www.jstatsoft.org/article/view/v072i10

I would also recommend R Packages.

You can use STATISTICA software

Dear Olga

I recommend EES, Its academic version is free.

www.fchart.com

please see this article. I think it's a very useful article.

I agree with Thom S Baguley . Please see the attached document and following links:

https://www.r-bloggers.com/linear-mixed-models-in-r/