Archive:Advanced ANOVA/One-way ANOVA: Difference between revisions
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==General steps== | ==General steps== | ||
# Establish hypothesis/hypotheses | # 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) | # Identify IV (categorical; between-subjects) and DV (at least interval) | ||
# Examine assumptions: | # Examine assumptions: | ||
| Line 12: | Line 16: | ||
#* Homogeneity of variance (the variance in each cell is similar) | #* Homogeneity of variance (the variance in each cell is similar) | ||
#* Cells are independent | #* 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 | #* Error-bar graph | ||
# Conduct inferential test and interpret significance | # Conduct inferential test (ANOVA) and interpret significance of ''F'' | ||
# Conduct planned contrasts | # Conduct follow-up tests (planned contrasts or post-hoc tests) if ''F'' is significant | ||
# | # Calculate and interpret effect sizes | ||
#* Eta-square (omnibus - equivalent to ''R''<sup>2</sup>) | #* Eta-square (omnibus - equivalent to ''R''<sup>2</sup>) | ||
#* Standardised mean effect size (difference b/w two means) | #* Standardised mean effect size (difference b/w two means) | ||
Revision as of 23:42, 2 August 2008
This tutorial focuses on one-way ANOVA.
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?
See also
External links
- ANOVA (ucspace)