Dear all
I want to test whether which factors significantly affect the weight of insects. There are 3 categorical independent variables and each is subdivided into 2 or 3 levels, and 1 continuous dependent variable. Here is an example of my data:
Diet, Conc., Sex, Weight (mg)
A 10% Male 100.2
A 20% Male 105.5
A 30% Female 150.0
B 10% Male 80.1
B 20% Female 130.3
B 30% Female 135.7
C 10% Female 150
C 20% Male 133
Which statistical method should I use?
As Mirko has noted, your percentage variable could be treated as a continuous explanatory variable. But given that there are only 3 possible values, I would be inclined to treat it as categorical. And if all explanatory variables, I would use a factorial ANOVA model. It is equivalent to a regression model on the same data, but the output from ANOVA may be a bit more convenient for what you are trying to do. Are any interactions expected a priori? What software do you use? HTH.
Thank you everyone. Now I got an idea how to deal with my data.
Bruce Weaver I will analyze in R.
I am not a frequent useR, but Salvatore S. Mangiafico is. You may find his resources at the link below helpful.
- http://rcompanion.org/handbook/G_09.html
I Think it depends on your research question
If you want to find any differences among categories
you should do ANOVA Test
If you want to find out what independent factors may cause one independent variable
you should chose regression model
It’s my Limitted Knowledge
What is your sample size? How many individual insects do you intend to examine? As others have mentioned ANOVA would be the best choice, but you will need many more observations in your 3 x 3 x 2 factorial design. The minimun to obtain cell means is n=36, but that is only n=2 in each cell.
If you have all possible combinations of the factors represented in your data set, then ANOVA is the way to go. You will only be able to include interaction terms in the model if your treatment combinations are replicated. If you have an incomplete factorial, you will need to use contrasts to compare groups so you are not confounding any effects.
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- Biological Science
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