Two-way ANOVA: Difference between revisions
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== | '''Two-way ANOVA''', short for two-way analysis of variances and also called two-factorial ANOVA, is a statistical method which is applied to investigate the effect of two categorial independent variables on one dependent variable. Those independent variables are also called factors.<ref>Harring & Johnson 2018, p. 1734</ref> In the course of the analysis, three different effects are of interest:<ref>Denis 2021, p. 147</ref> | ||
* '''Main effect''' of factor A on the dependent variable | |||
* '''Main effect''' of factor B on the dependent variable | |||
* '''Interaction effect''' of factor A and B | |||
==Differences compared to one-way ANOVA== | |||
Just like the one-way ANOVA, a two-way ANOVA investigates differences of group means.<ref>Harring & Johnson 2018, p.1736</ref> The main difference lies in the number of independent variables or factors used. While the effect of only one factor on the dependent variable is analyzed for the one-way ANOVA, two-way ANOVA investigates the effects and interaction of two factors.<ref>Harring & Johnson 2018 p. 1734</ref> Thus, both row mean differences and column mean differences are investigated. However, the main question of interest is equal for that of the one-way ANOVA:<ref>Denis 2021, p. 148</ref> | |||
* '''“Why does any given score in our data deviate from the mean of all the data?”''' | |||
==Design== | |||
For conducting a two-factorial ANOVA one can use either a balanced or unbalanced design. An identical number of observations for each group would be called balanced, while a different number of subjects per group is called unbalanced. However, the main problem of unbalanced designs is that the estimates of main effects might be distorted and therefore might have to be adjusted.<ref>Harring & Johnson 2018, p. 1736</ref> | |||
With a levels of factor A and b levels of factor B, the resulting data layout has a number of a x b cells or treatments. The following table shows a general data layout for two-way ANOVA analyses:<ref>Aczel-Sounderpandian 2009, pp. 381, 392</ref> | |||
Revision as of 11:25, 3 November 2022
Two-way ANOVA, short for two-way analysis of variances and also called two-factorial ANOVA, is a statistical method which is applied to investigate the effect of two categorial independent variables on one dependent variable. Those independent variables are also called factors.[1] In the course of the analysis, three different effects are of interest:[2]
- Main effect of factor A on the dependent variable
- Main effect of factor B on the dependent variable
- Interaction effect of factor A and B
Differences compared to one-way ANOVA
Just like the one-way ANOVA, a two-way ANOVA investigates differences of group means.[3] The main difference lies in the number of independent variables or factors used. While the effect of only one factor on the dependent variable is analyzed for the one-way ANOVA, two-way ANOVA investigates the effects and interaction of two factors.[4] Thus, both row mean differences and column mean differences are investigated. However, the main question of interest is equal for that of the one-way ANOVA:[5]
- “Why does any given score in our data deviate from the mean of all the data?”
Design
For conducting a two-factorial ANOVA one can use either a balanced or unbalanced design. An identical number of observations for each group would be called balanced, while a different number of subjects per group is called unbalanced. However, the main problem of unbalanced designs is that the estimates of main effects might be distorted and therefore might have to be adjusted.[6]
With a levels of factor A and b levels of factor B, the resulting data layout has a number of a x b cells or treatments. The following table shows a general data layout for two-way ANOVA analyses:[7]
