Which multilevel software should I choose? I have used packages in R thus far for MLM, but now I need to do MLM with complex survey data, and as I understand it, none of the MLM packages in R can cope with the complex weighting needed to analyze this type of data.  (I have done regular regression/logistic regression with complex survey data in R using the survey package.)
So now I am looking for a different software package to purchase this month that would allow me to do this kind of analysis.  I have been reading information online about the various programs, but I haven't been able to find, for example, videos of the kind of analysis that I am interested in in the different programs, that would give me an idea of how difficult it will be for me to figure out the different interfaces.  
Here are the main considerations in my choice of software:


*

*I need to be able to do MLM with complex survey data, as explained above.

*I am also interested in doing multiple imputation and propensity score matching.  I have done both of these things in R already, but I'd like to explore them using MLMs (with and without the complex survey data) as well.  (I know that R can theoretically do multiple imputation with MLMs, but I have never personally been able to get it to work with MLMs in R.)

*I'm not a great programmer.  I have a math background but no formal programming experience--everything I know about programming (or R for example), I have taught myself using books/the internet.  I'm still not that great at using new packages in R, since I often have to educate myself about basic programming topics like data structures, etc.  I also often have trouble linking how the programming commands or errors link at all to the underlying math.  (I do like to be able to have some control over what I am doing, but that doesn't do me much good when I can't figure out how to program it correctly. :))  So the upshot is, I'm looking for something that will be straightforward (and hopefully not hugely time-consuming) to learn.  

*The research I am currently doing is in the social sciences, in education, in case that helps.  

*I also do a lot of logistic regression, in case that is relevant to choosing a package.  


If you have any experience in this area and wouldn't mind sharing your advice on the matter, I'd be grateful!
 A: gllamm.org for Stata is the most flexible package to support multilevel weights. Per se, gllamm is only an estimation engine. You would need to provide your own steps in data transformation, including weight scaling. Of course, Stata has all of the multiple imputation, complex survey data analysis, matching. gllamm is written by a researcher from a school of education who published on using this package in complex survey data context, if that helps :). It will be time consuming to learn though, especially given that you need to learn a whole package whose paradigm is different from R.
A: I would recommend consulting the "Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis" which was published in 2014 for three relevant chapters and the references therein. Chapter 17 by Laura Stapleton is called "Incorporating Sampling Weights into Single- and Multilevel Analyses", Chapter 18, by Kim, Anderson, and Keller is called "Multilevel Analysis of Assessment Data", and Chapter 21, by Anderson, Kim, and Keller is called "Multilevel Modeling of Categorical Response Variables".
All three chapters give examples of how to incorporate design weights into multilevel modeling and address software capabilities. Chapters 21 and 18 also give example strategies for multiple imputation with multilevel data with sampling weights.
Since you do a lot of logistic regression (and will likely be doing more if you are interested in estimating propensity scores), chapter 21 will be of particular relevance. The key is to look for a program that uses adaptive quadrature to estimate model parameters; psuedolikelihood approaches tend to perform poorly with categorical outcomes and weights.
AFAIK, software options for multilevel logistic regression with design weights that use adaptive quadrature and report robust sandwich standard errors include Mplus, SAS NLMIXED, and gllamm (see StasK's answer).
