Archive:Advanced ANOVA/Factorial ANOVA: Difference between revisions
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==Design== | ==Design== | ||
'''Factorial ANOVA''' involves testing differences between group means based on two or more categorical [[independent variable]]s, with a single, continuous [[dependent variable]] | '''Factorial ANOVA''' involves testing differences between group means based on two or more categorical [[independent variable]]s, with a single, continuous [[dependent variable]]. In other words, a factorial ANOVA could involve: | ||
* | * Two or more between subjects categorical/ordinal IVs | ||
* | * One interval or ratio DV | ||
For example, "What is the effect of Gender (2) and Degree Type (3) on Overall Student Satisfaction?" | |||
We could describe this as a 2 x 3 Factorial ANOVA. | |||
If significant effects are found and more than | For a factorial ANOVA, the results of interest are: | ||
* Main effect for IV1 | |||
* Main effect for IV2 | |||
* Interaction between IV1 and IV2 | |||
If significant effects are found and more than two levels of an IV are involved, then specific contrasts are required, which could be either: | |||
* A priori (planned) contrasts | * A priori (planned) contrasts | ||
* Post-hoc contrasts | * Post-hoc contrasts | ||
(with appropriate control of the family-wise Type I error rate) | |||
[[Effect sizes]] should also be reported. | [[Effect sizes]] should also be reported. | ||
Revision as of 06:17, 4 October 2008
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Design
Factorial ANOVA involves testing differences between group means based on two or more categorical independent variables, with a single, continuous dependent variable. In other words, a factorial ANOVA could involve:
- Two or more between subjects categorical/ordinal IVs
- One interval or ratio DV
For example, "What is the effect of Gender (2) and Degree Type (3) on Overall Student Satisfaction?" We could describe this as a 2 x 3 Factorial ANOVA.
For a factorial ANOVA, the results of interest are:
- Main effect for IV1
- Main effect for IV2
- Interaction between IV1 and IV2
If significant effects are found and more than two levels of an IV are involved, then specific contrasts are required, which could be either:
- A priori (planned) contrasts
- Post-hoc contrasts
(with appropriate control of the family-wise Type I error rate)
Effect sizes should also be reported.
General steps
- Establish hypothesis/hypotheses
- Make sure you have separate hypotheses for:
- Main effect for each IV
- Interactions between IVs
- Planned contrasts (if warranted)
- Make sure you have separate hypotheses for:
- Examine assumptions:
- IVs (categorical; between-subjects) and DV (at least interval)
- 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 cell
- Examine graphs
- Conduct inferential test (ANOVA) and interpret significance of F scores
- Conduct follow-up tests (planned contrasts or post-hoc tests) if F is significant
- Interpret interactions
- Calculate and interpret effect sizes
- Eta-square (omnibus - equivalent to R2)
- Standardised mean effect size (difference b/w two means)
Example SPSS outputs
- Factorial ANOVA (example) - Are there differences in University Student Satisfaction levels between Gender and Age?
- Factorial ANOVA (example) - Are there differences in University Student Satisfaction levels between Gender and Age? - Are there differences in Locus of Control between Gender and Age?
Descriptives
- A table of descriptive statistics (M, SD, Skewness, and Kurtosis) for each cell and for each marginal total, and grand total should be presented when reporting results.
- For a 2-way ANOVA, the descriptives table should breakdown one IV in the columns and the other IV in the rows, such as illustrated in the following basic table design.
- Note that each of the three columns on the right should be further split into five columns to allow reporting of M, SD, Skewness, and Kurtosis, n.
| Age | |||
| Gender | Younger | Older | Total |
| Males | |||
| Females | |||
| Total | |||
Expanded, the descriptive table layout could look like this (note this second table switches the rows and columns from the table above - this is somewhat arbitrary):
| Age | ||||||||||||
| Young | ||||||||||||
| Middle | ||||||||||||
| Older | ||||||||||||
| Overall | ||||||||||||
Here's an example of such an APA style table:
Understanding interactions
- One of the keys to understanding Factorial ANOVA is being able to intepret interactions.
- A recommended experiential exercise for learning about interactions is to fabricate a dataset which can be used to demonstrate factorial ANOVAs in which there are:
- No effects
- Main effect A, no main effect B, no interaction
- Main effect A, no main effect B, interaction
- No main effect A, main effect B, no interaction
- No main effect A, main effect B, interaction
- Main effect A, main effect B, no interaction
- Main effect A, main effect B, interaction
- Interaction, no main effects
Francis exercises
Effect sizes
See also
External links
- Factorial ANOVA (ucspace)
- Factorial ANOVA Notes (Handout)