Hypothesis Testing: Factorial ANOVA

In summary, using a two-way ANOVA is a good choice for your research design. To interpret the F-values, you can use tests such as the Tukey HSD Test or the Bonferroni Test and also consider the adjusted R-squared value to determine the validity of the model.
  • #1
phoebz
19
0
Hello!

So I had to propose a research design as an assignment and I chose to look at cognitive function in an exercise group vs. a control group over the period of 10 years.

-subjects are tested at the beginning and end of each year

The groups were not randomized on perfectly (I used two Grade 3 classes, one as control, one as exercise) controlled so I chose a two-way ANOVA 2 (pre/post) x 2(exercise/control).

The issue now is that I do not know how I would organize/ interpret all these F-values and its become very confusing.

Any advice?
 
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  • #2
The two-way ANOVA is a great approach for your research design, as it allows you to compare different factors (two in this case) and how they affect the outcome. To interpret the F-values, you can use a few different tests such as the Tukey HSD Test or the Bonferroni Test. These tests are used to compare the differences between the mean values of each group, and can help you analyze the data to determine if there is a significant difference between the groups. Additionally, you can also look at the adjusted R-squared value of the model, which gives you an indication of how much of the variation in the data can be explained by the model. It can also help you decide if the model is valid or if more factors need to be considered.
 

Related to Hypothesis Testing: Factorial ANOVA

1. What is the purpose of Hypothesis Testing: Factorial ANOVA?

Hypothesis Testing: Factorial ANOVA is a statistical method used to analyze the effects of two or more independent variables on a single dependent variable. It allows researchers to determine if there is a significant difference between groups and to identify which independent variables have the greatest impact on the dependent variable.

2. How is Factorial ANOVA different from One-Way ANOVA?

Factorial ANOVA is an extension of One-Way ANOVA, which only allows for the comparison of one independent variable. Factorial ANOVA allows for the analysis of multiple independent variables and their interaction on the dependent variable. This allows researchers to understand the combined effects of multiple variables on the outcome.

3. What are the assumptions of Factorial ANOVA?

The assumptions of Factorial ANOVA include normality, homogeneity of variances, independence of observations, and the absence of multicollinearity. Normality assumes that the data is normally distributed, homogeneity of variances assumes that the variances of the groups being compared are equal, independence of observations assumes that each observation is not influenced by other observations, and the absence of multicollinearity assumes that there is no strong correlation between the independent variables.

4. How is the significance level determined in Factorial ANOVA?

The significance level, also known as alpha, is typically set at 0.05 in Factorial ANOVA. This means that there is a 5% chance that the results of the analysis could have occurred by chance. If the p-value is less than 0.05, the results are considered statistically significant and the null hypothesis can be rejected.

5. Can Factorial ANOVA be used for non-parametric data?

No, Factorial ANOVA is a parametric test and requires the data to be normally distributed. If the data does not meet this assumption, a non-parametric test such as the Kruskal-Wallis test should be used instead.

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