Hello to everyone!!!
I am doing animal behaviour and biochemical analysis for my studies, i have four groups control and three different stress groups. For my study i am using both sex of animals male and female, here i have to compare control male with among three different stress groups and also i am comparing male four different groups with female control and three stress groups with male groups, for this study which would be the most appropriate ANOVA test one way ANOVA or two ANOVA test?
What would be the most appropriate post hoc analysis for this study?
It sounds to me like you have 2 factors: stress and sex. Therefore, you should do a 2-way ANOVA if you are interested in how these factors interact. You will have one factor with 2 levels (sex: male and female), and one factor with multiple levels (stress: control and however many stress groups you have). To follow up the 2-way ANOVA, you should always use a post hoc test that includes adjustment for multiple comparisons, but which one you use is often a personal preference. I prefer Tukey's HSD test.
Yes 2-way ANOVA (2 x 4 by my reading). We are assuming that you have randomized the animals to the stress conditions, correct? Although "post hoc" comparison is the common phrase, you probably want to use "planned comparison" instead, which implies that you specified the comparisons in advance based on your research questions, each comparison testing a specific hypothesis. Are the cell (sample) sizes equal in each of the cells of the 2 x 4 factorial design? To adjust for multiple test procedures and control experiment-wise error I would recommend REGWQ or Tukey's if equal cell sizes, or Hochberg's GT2 for unequal cell sizes.
Hello Dr. James,
i have one control group and three stress group, all groups consists of both sex (Male & Female) for the experimental study. what would be the suitable statistics method and post hoc test.
yes sample size is equal in all groups. four groups and both sex
- Badges
- Science method
- Similar topics
- Mathematics
- Statistics
- Statistical Modeling
- Analyses of Variance
- ANOVA