Archive:Advanced ANOVA/One-way ANOVA: Difference between revisions
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* Use any dataset | * Use any dataset | ||
* Conduct a one-way ANOVA and graphically present the means and confidence intervals using an Error Bar Graph - is this error bar chart consistent with the statistical results? Why? Why not? | * Conduct a one-way ANOVA and graphically present the means and confidence intervals using an Error Bar Graph - is this error bar chart consistent with the statistical results? Why? Why not? | ||
==Data== | |||
# [http://www.pearsoned.com.au/wpsBridge/francis5/files/AQUES.sav AQUES.sav] | |||
# [http://www.pearsoned.com.au/wpsBridge/francis5/files/motiv.sav Motiv.sav] | |||
==See also== | ==See also== | ||
* [[Analysis of variance/Data analysis tutorial]] | * [[Analysis of variance/Data analysis tutorial]] (3rd year tutorial) | ||
==External links== | ==External links== | ||
Revision as of 21:30, 6 August 2008
| File:Wikademia.logo.png | Resource type: this resource contains a tutorial or tutorial notes. |
Template:50%done This tutorial focuses on one-way ANOVA - testing mean differences between more than two independent groups.
Introduction
- Unit outline (updated - to upload/link)
- [[../Assessment/]]
General steps
- Establish hypothesis/hypotheses
- Make these as explicit and clear as possible
- Break complicated hypotheses down into sub-hypotheses
- Each hypothesis should be able to be answered as "yes" or "no"
- It should be clear from the hypotheses what the predicted relation is between an IV and a DV
- Identify IV (categorical; between-subjects) and DV (at least interval)
- Examine assumptions:
- The data in each cell is normally distributed
- Homogeneity of variance (the variance in each cell is similar)
- Cells are independent
- Examine descriptive statistics, particularly the four moments (M, SD, Skewness, Kurtosis) overall, and also for each group
- Examine graphs, e.g.,:
- Histograms
- Normal probability plot
- Error-bar graph
- Conduct inferential test (ANOVA) and interpret significance of F
- Conduct follow-up tests (planned contrasts or post-hoc tests) if F is significant
- Calculate and interpret effect sizes
- Eta-square (omnibus - equivalent to R2)
- Standardised mean effect size (difference b/w two means)
Visual ANOVA
- Understanding ANOVA Visually (may require viewing with Internet Explorer)
- Under what conditions would F be the smallest?
- Under what conditions would F be the largest?
- Now explore the same ideas with this more advanced Visualisation Tool for One-way and Two-way ANOVA Applet
Error bar graphs
- Use any dataset
- Conduct a one-way ANOVA and graphically present the means and confidence intervals using an Error Bar Graph - is this error bar chart consistent with the statistical results? Why? Why not?
Data
See also
- Analysis of variance/Data analysis tutorial (3rd year tutorial)
External links
- ANOVA (ucspace)