In the past, I did a lot of multi-level modelling with MLwiN 2.02, which I quickly learned to loath. Back in the late 1990s, MLwiN was perhaps the first ML software that had a somewhat intuitive interface, i.e. it allowed one to build a model by pointing and clicking. Moreover, it printed updated estimates on the screen while cycling merrily through the parameter space. That was sort of cool, as it could take minutes to reach convergence, and without the updating, one would never have been sure that the program had not crashed yet. Which it did quite often, even for simple models.

Worse than the bugs was the lack of proper scriptability. Pointing and clicking  loses its appeal when you need to run the same model on 12 different datasets, or when you are looking at three variants of the same model and 10 recodes of the same variable. Throw in the desire semi-automatically re-compile the findings from these exercises into two nice tables for inclusion in again and again after finding yet another problem with a model, and you will agree that any  piece of software that is not scriptable is pretty useless for scientists.

Continue reading “Running MLwiN from within Stata” »

MLwiN is one of the granddaddies of multi-level modelling software (the other being HLM).  Essentially, it is a 1990s-ish looking and sometimes quirky GUI wrapped around  an old DOS program (MLn). The one feature that set MLwiN apart in the late 1990s is point-and-click interface that allows you to build the equations for a multi-level in a stepwise fashion. The underlying command language is still slightly confusing and less than well documented, and some of the modern features (such as modelling categorical dependent variables) are implemented as external macros, which does not need to concern you unless something goes horribly wrong, which happens occassionally.

That said, MLwiN is reasonably fast, does now incorporate modern MCMC estimators, has an interface with WINBUGS and can be convinced to do most things you would possibly want to do with it.  I bought version 1.10 ca. 1998, received free upgrades to 2.02 and good support well until 2004/2005 or so.  These days, Stata, R and MPlus can all estimate multi-level models, but working with MLwiN may still be worthwhile for you (by the way, you can download the free stata2mlwin addon from UCLA academic technology to export your variables from Stata to MLwiN).

Continue reading “MLwiN 2.10: free for British Academics” »

Our project on social (citation and collaboration) networks in British and German political science involves networks with hundreds and thousands of nodes (scientists and articles). At the moment, our data come from the Social Science Citation Index (part of the ISI web of knowledge), and we use a bundle of rather eclectic (erratic?) scripts written in Perl to convert the ISI records into something that programs like Pajek or Stata can read. Some canned solutions (Wos2pajek, network workbench, bibexcel) are available for free, but I was not aware of them when I started this project, did not manage to install them properly, or was not happy with the results. Perl is the Swiss Army Chainsaw (TM) for data pre-processing, incredibly powerful (my scripts are typically less than 50 lines, and I am not an efficient programmer), and every time I want to do something in a slightly different way (i.e. I spot a bug), all I have to do is to change a few lines in the scripts.
After trying a lot of other programs available on the internet, we have chosen Pajek for doing the analyses and producing those intriguing graphs of cliques and inner circles in Political Science. Pajek is closed source but free for non-commercial use and runs on Windows or (via wine) Linux. It is very fast, can (unlike many other programs) easily handle very large networks, produces decent graphs and does many standard analyses. Its user interface may be slightly less than straightforward but I got used to it rather quickly, and it even has basic scripting capacities.

The Missing Manual

Continue reading “Software for Social Network Analysis: Pajek and Friends” »