User:Jtneill/Presentations/More complex summary statistics
More complex summary statistics: Effect sizes
Statistics Networking Day, 6 August, 2015, UC
Statistics in psychology
|1st||Introduction to Psychological Research||Research design, univariate descriptive statistics, bivariate linear correlations, t-tests||~200-250|
|2nd||Experimental Psychology||Experimental design, ANOVAs, non-parametric tests of differences||~150|
|3rd||Survey Research and Design in Psychology||Survey design, correlations, exploratory factor analysis, multiple linear regression||~140|
|4th||Research Methods and Professional Ethics||Advanced multivariate statistics (ANOVA and MLR)||~25-30|
What's your favourite complex summary statistic?
- What would you choose?
- For me the choice is easy - although I wasn't taught how to use effect sizes in undergraduate psychology, it is the statistic that I use the most and find most useful.
My favourite: Effect sizes
- Social sciences have tended to over-emphasise null hypothesis significance testing and under-emphasise use of effect sizes:
"I believe that the almost universal reliance on merely refuting the null hypothesis as the standard method for corroborating substantive theories in the soft areas is a terrible mistake, is basically unsound, poor scientific strategy, and one of the worst things that ever happened in the history of psychology"
(Meehl, 1978, p. 817)
- Statistical significance is a function of effect size, sample size, and probability level - but most often, people want to know about the effect size (i.e., "how strong is the relationship or how big is the difference?).
- Effect sizes can be re-expressed as other effect sizes or other common language formats such as percentages.
Types of effect sizes
Graphing effect sizes
- Accompanying ESs with CIs offers the best of both worlds - i.e., indicates the size of an observed effect and uncertainty
- In applied social science research, people generally want to know about the strength of relationship or the size of the difference - so, report effect sizes. Don't rely solely on inferential statistical testing.
- Graph effect sizes and include confidence intervals.
- Hattie, J., Marsh, H. W., Neill, J. T., & Richards, G. E. (1997). Adventure education and Outward Bound: Out-of-class experiences that make a lasting difference. Review of Educational Research, 67, 43-87.
- Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46, 806-834.
- Neill, J. T. (2008). Enhancing life effectiveness: The impacts of outdoor education programs. Unpublished doctoral dissertation, Faculty of Education, University of Western Sydney, NSW, Australia.