I’m just returning from the ECM 2012 conference that was held in Santiago de Compostela.
As was the case for to the preceding one, and in contrast to the annual conference from the Psychometric Society, this conference is generally more heavily oriented toward psychological applications, and less place is devoted to psychometric ‘hard’ methods, except for Friday’s morning sessions. There was a workshop on applied IRT modeling with R, but I completely missed it.
Few days ago, I came across Oliver Kirchkamp’s Workflow of statistical data analysis which provides essential tips and guidelines for managing not only data but the whole analysis flow (from getting raw data to publishing papers).
For R users This is a very comprehensive textbook, with illustrations in R. I already dropped some words two years ago in How to efficiently manage a statistical analysis project. People with little knowledge of R can skip chapter 2 and go directly to chapter 3 which goes to the heart of the problem: How to create and manage a statistical project in R?
Back from the IV EAM conference that was held in Postdam, near Berlin. The next one is planned in two years in Spain. In the mean time, I expect great publications coming up from some of the presenters.
There was a lot of interesting talks although five parallel sessions inevitably led to tedious alternative forced choice decisions (unless one’s willing to run from one room to another in less than 2 minutes, but this is not my case).
I just read Practical Psychiatric Epidemiology, from Prince, M., Stewart, R., Ford, T., and Hotopf, M. (Eds.) (Oxford, 2003).
There is already a review in The Bristish Journal of Psychiatry, 186: 268 (2005). Hereafter, I would like to quote some of the main ideas of this nice textbook on research and methodological aspects of psychiatric epidemiology. Although this textbook is mostly dedicated to students in epidemiology or psychiatry (which I am not, of course), it contains a huge amount of useful references and advices to whom may be concerned with the analysis of comparative studies in the biomedical domain.
This book is about regression techniques commonly used when modelling continuous and/or binary outcomes in biomedical studies. Starting from the most basic techniques (but too often neglected, to my opinion) of exploratory and descriptive techniques (Chap. 2, graphical and numerical summaries), the authors devote an entire chapter (Chap. 3) to give the reader a clear overview of classical multivariate techniques used to characterize association between categorical and continuous variable (including censored data).