Monthly Archives: September 2009

Survey weights and new ANES suggestions

Many large surveys are structured as complex sample designs that reflect various stratification considerations. Statistics calculated from such designs must be weighted to reflect the general population of interest. A clear discussion and set of recommendations by four prominent researchers for the calculation and implementation of weights using ANES datasets can be found in the Sept. 2009 Technical Report, nes012427, Computing Weights for American National Election Study Survey Data. The report can be found in the Reference Library section of the ANES
website.
Single panel cross-sectional, two-wave panel and multi-wave panel recommendations are considered along with nonresponse and poststratification weighting. The generality of discussion applies to other large studies such as Census data, and similar surveys.

Posted in Uncategorized | Leave a comment

Areal and point source spatial data models

Researchers using spatial data are often faced with a mix of data obtained from several levels of scale, aggregation and point reference data. Classical geospatial regressions do not deal with this mix very well, and standard ordinary regressions even worst. A unified treatment is the topic of a recent article, “Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models” in Volume 18, Number 2 of JCGS. In short, the authors cleverly reparameterized and recast the problem so as to allow efficient MCMC samplers to address the Bayesian estimation task. Their article’s supplemental materials provide the R and OpenBugs codes to address the efficient estimation tasks outlined.

Posted in Uncategorized | Leave a comment

Spss resources

Spss software has an extensive tutorial built into its product and most first time users will benefit from using it. Additional Spss resources can be found here.

Posted in Uncategorized | Leave a comment

R available on Tufts Linux Cluster

Elsewhere on this Blog I mention various bits and pieces of R software. Now that the fall semester is upon us, we have added many new R BioInformatic packages to the baseline R installation on our research linux cluster. This option provides a scalable solution to those needing additional computing power.

Posted in Uncategorized | Leave a comment