User:Jtneill/Presentations/More complex summary statistics
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More complex summary statistics: Effect sizes
Dr. James Neill, Assistant Professor, Centre for Applied Psychology, Faculty of Health, University of Canberra
Contents
Statistics in psychology[edit]
Year | Unit | Topics | Enrolment |
---|---|---|---|
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?[edit]
- 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[edit]
- 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[edit]
Type | Correlations | Means |
---|---|---|
Bivariate | r | d |
Multivariate | R |
Graphing effect sizes[edit]
Confidence intervals[edit]
- Accompanying ESs with CIs offers the best of both worlds - i.e., indicates the size of an observed effect and uncertainty
Take-home message[edit]
- 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.
References[edit]
- 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.