Saturday, September 19, 2009
Introduction to Journal Article Deconstruction
Friday, September 18, 2009
Variable Importance and Multiple Regression
R Community in Australia
Thursday, September 17, 2009
Comments on "Introduction to Scientific Programming and Simulation Using R"
Tuesday, September 15, 2009
Setting up a Blog on Blogger
The details below set out my setup for my blog account and my blogging statistics. When I set it up originally, I did look into the various options in terms of blogging providers and so on. I make no claim to my choices being optimal for me or other people. But I have found them more than adequate for my purposes. In particular, usage statistics (and comments) are a great form of feedback that is not necessarily available in other forms of academic communication. For further discussion of the benefits of blogging and related technologies in academic, Gideon Burton provides a great exposition.
Confidence Intervals and Correlations
A researcher recently asked me how to calculate confidence intervals for two correlations that share a common variable (i.e., dependent correlations).
Thursday, September 10, 2009
Pen and Paper
Examples:
Wednesday, September 9, 2009
Experiments with a mixture of repeated measures and between subjects factors
- 5 x 3 Design: 5 levels of task type (repeated measures); and 3 levels of group (between subjects)
- 2 x 2 x 2 Design: 2 levels of order (between subjects); by 2 levels of instructions (between subjects); by 2 levels of task feature (repeated measures)
- UCLA has several examples of how examine such designs using SPSS Repeated Measures ANOVA; It also talks about how to test contrasts and run follow up test more generally.
- Andy Field provides a gentle introduction to repeated measures ANOVA using SPSS.
Tuesday, September 8, 2009
Cluster analysis and single dominant factors
Monday, September 7, 2009
Logistic Regression Resources in SPSS or R
Significance Tests on Correlations
Wednesday, September 2, 2009
Repeated Measures Experiments with Many trials in SPSS (PASW)
Data Format:
Create a long format data file called “trials” where each row is the combination of one participant and one trial. And have a separate data file called “subjects” that contains one row per participant and includes data on participants that is constant throughout the experiment (e.g., gender, age, personality measures, etc.).
Tuesday, August 18, 2009
Social Network Analysis Resources for R
Social Network Analysis is an increasingly popular tool for modelling dependence structures between social actors. In my department researchers are developing new models for representing such dependence structures (MELNET). In 2007 I gave a talk on my consulting experience using social network analysis to provide insights on team dynamics. Since then I have switched to mainly using R for analysing social network datasets.
Monday, August 17, 2009
Selecting University Students: Perspectives from Selection and Recruitment
- What criterion of an effective selection system does the university want to use?
- What measurement system can be put in place to maximise this criterion?
Wednesday, August 5, 2009
My Procedure for Upgrading R: Windows XP with StatET
Friday, June 12, 2009
Saturday, June 6, 2009
Upcoming Conferences
- 11th European Congress on Psychology in Oslo: I'll be presenting a talk: "The effect of warnings on personality test faking in employee selection"
- Directions in Statistical Computing in Copenhagen
Normality and Transformations: A few thoughts
Learning R for Researchers in Psychology
R is a powerful open source environment for statistical computing. This post provides a selective list of resources for getting started with R including thoughts on books, online manuals, blogs, videos, user interfaces, and more. At the end of the post are some R resources specific to researchers in psychology. (UPDATED 4th May 2011)
Friday, May 29, 2009
Pronunciation Guides for Mathematical Notation, Expressions, and Greek Letters
Mathematics Pronunciation Guides
- VÄliaho's guide to Pronunciation of Mathematical Expressions: This is the place to start. It covers many important rules in a 3 page document
- Handbook for Spoken Mathematics: If VÄliaho's guide did not meet your requirements, check out this extensive resource. It covers many major branches of mathematics such as logic and set theory, geometry, statistics, calculus, and linear algebra. It is the most comprehensive guide that I have found with around 100 pages and around 500 symbols with pronunciation. I'd recommend studying all the symbols if mathematical pronunciation is an issue for you. The symbols are distributed over many pages making it a little difficult to look up a single symbol of interest. Also, when a choice exists, the guide often chooses a more verbose and less ambiguous form of pronunciation. For example, it suggests for "x_i", "x sub i" instead of "x i". This emphasis on unambiguous verbal communication is sometimes more than required when verbalising the symbols in your head or when verbalising symbols in a context where the actual symbolic math is also displayed.
- RPI's Saying Mathematics Guide
- Oanca et al's Reading Mathematical Expressions
- Wikipedia guide to mathematical symbols: meaning of common mathematical symbols with links to their meaning.
- Greek letters: Lower and upper case Greek letters with pronunciation
- Tips on displaying formulas can even be useful for some obscure mathematical symbols
Books on mathematical pronunciation
- Lawrence Change (1983). Handbook for Spoken Mathematics: (Larry's Speakeasy).
Related Posts
Thursday, May 28, 2009
Introduction to Statistical Modelling in Psychology: NSS Presentation
Today I gave an introductory talk for the Neuropsychological Students’ Society at the University of Melbourne on the topic of Statistical Modelling in Psychology.
The slides with notes from the talk are available for download at the following link: Introduction to Statistical Modelling in Psychology.
Friday, May 22, 2009
Bootstrapping and the boot package in R
I was recently asked about options for bootstrapping. The following post sets out some applications of bootstrapping and strategies for implementing it in R. I've found bootstrapping useful in several settings:
- where the statistic I'm interested in is a little unusual: the average R-square across five separate regressions; the difference in the average correlation of a set of variables between two groups
- non parametric statistics, such as the median
- when assumptions such as normality of homoscedasticity are not satisfied
Thursday, May 21, 2009
Self-Archiving of journal articles in academia
Wednesday, May 20, 2009
Endnote Collaboration
Friday, May 15, 2009
Statistics for a Psychology Thesis
The audio (17MB) for the talk is available online, as are the Slides, and a PDF version of the content below.


