Monday, May 24, 2010
New York Times reports on an interesting UCLA study that involved video taping 32 Los Angeles families over the course of a week. The study generated rich data for analysis. It's great to see researchers moving beyond self-report measures towards real-world well-coded behavioural observations. However, great measurement does not overcome issues of a small sample size.
Thursday, May 20, 2010
I was recently talking to a researcher who had conducted a cognitive experiment that involved experimentally manipulating a variable
x, a continuous property of a stimulus, looking at the effect on a variable
p, the probability of giving a response. The function p(x) was assumed to be a logistic function. The researcher wanted to know how to calculate the point on
xat which the fitted logistic regression function equalled 0.5.
This post briefly discusses how to run a nonlinear regression in SPSS. Specifically, it discusses the scenario where you have a a set of
kobservations for each of
nparticipants, and where your aim is to fit a nonlinear function to the data of each participant in order to save the parameter estimates for subsequent analysis. This is a relatively common task in psychology. You have multiple participants measured on a numeric repeated measures variable and you want to see how a dependent variable is related to this repeated measures variable. And you want to do this separately for each participant. For example, you might be modelling performance as a function of practice or accuracy as a function of stimulus intensity.
Sunday, May 16, 2010
In this post I present a 34-minute video on using R. The video is based on an analysis of 1924 to 2006 Winter Olympic Medals that I presented previously in text form. The video aims to to show what an interactive session in R might look like using StatET and Eclipse.
Monday, May 10, 2010
The R programming language includes many abbreviations. Abbreviations exist in function names, argument names, and allowed values for arguments. This post expands on over 150 R abbreviations with the aim of making it easier for users new to R who are trying to memorise R commands.
Wednesday, May 5, 2010
G*Power 3 is an excellent piece of software for performing statistical power analysis. It is particularly useful for applied researchers who need to perform a power analysis as part of their research. The software is free, runs on Windows, and provides a user friendly GUI. G*Power 3 can be downloaded here This post discusses the features of G*Power 3 and provides examples of some of the useful plots that can be generated.
If you want to learn about R through videos, there are now a large number of options. This post provides links to many of these video under the headings of: (a) What is R? (b) Introductory R, and (c) Intermediate and Advanced R.