Tuesday, September 29, 2009
A difference score is a variable that has been formed by subtracting one variable from another.
DIFFSCORE = VAR1 - VAR2.
Some researchers have heard that difference scores are 'bad'. This post discusses some of the issues, provides some additional references, and discusses calculating reliability of difference scores.
Monday, September 28, 2009
It is an interesting case study in how to integrate R into a psychology quantitative methods course at the undergraduate level. It's also a cool example of integrating web resources.
Saturday, September 26, 2009
Friday, September 25, 2009
"A SPANK on the bottom, long used by parents to discipline a naughty child, could cause more than tears... It's now thought the age-old disciplinary method may also lower a child's IQ, with those spanked up to three times a week having a lower IQ due to psychological stress."
Thursday, September 24, 2009
Wednesday, September 23, 2009
- Formatting correlation matrices into psychological format using SPSS, Excel and Word
- One-group observational study: Basic set of analyses with links to SPSS resources
- Longitudinal data: Basic data management and analyses
- dyadic data: Basic data analysis and how to merge data in SPSS in dyadic data situations.
- Carry-over effects in repeated measures designs: what to think about and a case study from my own research
- Issues of causal inference in mediation analysis and basic resources for performing mediation and moderation analyses.
Tuesday, September 22, 2009
Monday, September 21, 2009
Something to aspire to:
- Raw data is shared (ethics, copyright, and other considerations permitting).
- Code is shared that shows how the data was imported, transformed, and analysed. This code is well written, commented, and documented.
- The report is shared as opposed to requiring a paid subscription.
- Report output including tables, figures, and some text is linked directly to the analyses in code.
While the aspirations transcend R, I like the prospect of having analyses in R integrated with a final report. The inclusion of tables and figures , at least conceptually is a straightforward idea. However, the inclusion of text in a results section is a little fuzzier. Surely, text in a results section (I'll call it "results text" for short) varies in how it relates to actual analyses. Thus, I had the following questions: 1) What is the unit of results text? 2) How does results text vary and what should be automatically supplied by R?; 3) For results text that should not be supplied by R, how should it be integrated into an analysis process?
Initial thoughts: After a little reflection I had the following thoughts:
Saturday, September 19, 2009
Friday, September 18, 2009
Thursday, September 17, 2009
Tuesday, September 15, 2009
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.
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
Wednesday, September 9, 2009
- 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)
Tuesday, September 8, 2009
Monday, September 7, 2009
Wednesday, September 2, 2009
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.).