Dark Brightness

SOme cyclists are green zealots. Others are risk takers who prefer to be fit. It seems this chap is one of the latter (you have to have a certain enjoyment of danger to cycle in Auckland). He also has a neat punishment for nongs who don’t see him.

That said, I don’t bike _because_ it’s good for the environment. I’m suspicious that the extra showers and increased carbo-intake has a low carbon footprint.

I bike because, well, I like biking. And in Auckland there’s the added thrill of the danger. You aquire some interesting scar tissue…

Albeit I am of the firm belief that young men driving fast cars playing tag with a cyclist, should be dragged up the length of Queen Street by their goolies.

via Cactus Kate: Cycle Action Auckland – Inside The Lycra.

Real men shoot, run, fish, hunt, drive and cycle because they think it is fun. Not to “save” a planet that is bigger than any of us.

Which means work.

At present I’m trying to work out how to do a meta analysis of the correlations or odds ratios in a nested confirmatory factor analysis. Probably not needed, as there are descriptive differences and I simply cannot compare apples with gorillas. Problem is there are two papers with almost identical results. But not quite.

WIll need to look at more than OVID for this — there were only 17 hits with the first search so I’ll change words and look again (I was hitting multiple databases at the same time though, so only five papers that looked useful). May not be much else

In the meantime, CRAN let me find this:

Authors: Ken Kelley
(2445)Confidence Intervals for Standardized Effect Sizes: Theory, Application, and Implementation
Reference: Vol. 20, Issue 8, Feb 2007
Submitted 2006-10-01, Accepted 2007-07-30
Type: Article
Abstract:

The behavioral, educational, and social sciences are undergoing a paradigmatic shift in methodology, from disciplines that focus on the dichotomous outcome of null hypothesis significance tests to disciplines that report and interpret effect sizes and their corresponding confidence intervals. Due to the arbitrariness of many measurement instruments used in the behavioral, educational, and social sciences, some of the most widely reported effect sizes are standardized. Although forming confidence intervals for standardized effect sizes can be very beneficial, such confidence interval procedures are generally difficult to implement because they depend on noncentral t, F, and x2 distributions. At present, no main-stream statistical package provides exact confidence intervals for standardized effects without the use of specialized programming scripts. Methods for the Behavioral, Educational, and Social Sciences (MBESS) is an R package that has routines for calculating confidence intervals for noncentral t, F, and x2 distributions, which are then used in the calculation of exact confidence intervals for standardized effect sizes by using the confidence interval transformation and inversion principles. The present article discusses the way in which confidence intervals are formed for standardized effect sizes and illustrates how such confidence intervals can be easily formed using MBESS in R.

I am such a stats geek that this is my bedtime reading. Pathetic.