Monthly Archives: March 2007

StocNet – social network analysis

Stocnet is free software designed to address aspects of modeling networks. It offers five different statistical (stochastic) methods to estimate networks, and to calculate some common descriptive network statistics, offer some data transformation and/or graph selection capabilities and explore network simulation possibilities. The five models include: p* models(ERGMs), blockmodeling, p2 models, ultrametric methods for clustering and ZO methods for undirected graphs/networks. Relatedly, the homepage of one of the authors of Stocnet, Tom Snijders, contains useful information about Social Network Analysis.

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CARMA Resources

Tufts University Information Technology(UIT) Academic Technology(AT) group recently acquired a Tufts subscription to the Center for the Advancement of Research Methods and Analysis(CARMA) website. CARMA provides video lectures addressing statistical research methologies widely used in the Social Sciences. The presentations are designed to be tutorial in nature and self-contained. Many topics are presented at the upper undergrad and graduate school level. Presenters are CARMA Fellows from universities throughout the U.S. Most streaming lectures are 60-90 minutes in duration and include downloadable powerpoint slides. Lecture presentations have included topics such as Limited Dependent Variable regression, Structural Equations Models, Meta-Analysis, NonResponse in Surveys, Robust Regression, Item Response Theory, Latent Growth models, Hierarchical Modeling and many more. A schedule for Spring 2007 Webcasts is available on their website along with archieved lectures. Tufts faculty and students are required to register on the CARMA site with a Tufts email address and to obtain a password for access.

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Gllamm: Generalized Linear Latent & Mixed Models

gllamm is a user contributed STATA program to handle a very wide variety of models for addressing multilevel latent and mixed variable models. There are three components to gllamm: estimations tasks(gllamm), post-estimation predictions tasks(gllapred) and simulation(gllasim). Why would one care? In some settings having a unified treatment of estimation for many seemingly unrelated models can help one gain insights into applications and estimation inter-relationships. For example the following models: GLMMs, Multilevel Regressions, Factor models, Item Response, SEM models and Latent Class models are all special cases. If you have access to STATA follow the instructions to download and install gllamm.

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