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Total number of experiment participants, N: this will be determined by type of factor for each.Number of subjects per condition, n: how many participants are in each level/group/treatment.
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Total number of condition in the experiment: this is identified by multiplying out the number of levels for each factor.Response variable: this is the dependent variable/outcome variable/measurement taken.Levels of each factor: how many conditions/groups/treatments a factor has.Factors: the independent variables/predictors.Does the effect of age of teen (Factor A) on the number of phone calls made to the opposite sex (response variable) depend on the sex of the teen (Factor B)?īefore we begin the process of calculating a 2-Factor ANOVA we need to review several key elements of the study:.Is there a significant effect of sex of the teen (Factor B) on number of phone calls made to the opposite sex (response variable).Is there a significant effect of age of teen (Factor A) on number of phone calls made to the opposite sex (response variable).A 2-Factor ANOVA allows a researcher to assess the main effects (the independent variables) and the interaction yielding three outcomes (3 Fs), a F for factor 1, a F for factor 2 and an interaction between factor 1 and 2.An interaction is the result of the two independent variables combining to produce a result that is different from a result that is produced by either variable alone. Collectively where the collective influence of the factors is referred to as an interaction.Individually for each factor, reporting out a F for each.Factorial designs like the 2-Factor ANOVA allow a researcher to examine more than one independent variable on the dependent variable.When the data is organized in a matrix it is very easy to see the factors, as well as the separate levels of the factors. Two Factor ANOVA data is commonly organized like the table above and is referred to a matrix. For a between-subjects design, there are 4 different samples. So among the 4 total conditions/levels/groups between the 2 factors, an individual is only in 1 of the samples. An individual can only be in 1 condition for gender and 1 condition for age. In this case, we have a between-subjects design. Remember that there are different types of ANOVAs based on design.