I am having trouble understanding a paper I read. The paper used both univariate repeated measures analysis of variance for within subjects effects and repeated measures of analysis of variance for between subject effects. The research paper I have read is looking at the effects of incubation temperature and egg size on hatchling size and growth in turtles. These turtles lay different size eggs and the researcher is proposing that the larger eggs incubated at a higher temperature grow faster (over 3 years) than large eggs incubated at a lower temperature. Therefore there are two temperatures. Basically they collected eggs from four females and randomly assigned eggs from each of the females to one of the 2 temperatures.

-From what I understand the researcher eventually needs to determine if clutch of origin had any effect because these are not actually individual replicates because they came from the same mother? Is this correct thinking? If so one would need to used an Ancova to do so?

Now when the author plots (ln of egg mass) on the x axis and (ln of mass at 3 years) on the y axis, he finds a significant effect for those placed in the higher temperature (r^2=0.59, p<0.01), but not for those placed in a lower temperature (p=0.12). I understand that this means that 59% of the time ln egg mass has an effect of the ln of the mass at age 3 (in high temperature), and that this number is a good correlation (considering this is nature?).

However, the author puts one table the following:

Results of the univariate repeated measures analysis of variance for within subject effects. The dependent variable is the residual of natural log-transformed hatchling mass and juvenille mass. With the following sources:

1. Age Class

2. Age Class*Temperature

3. Age Class*Clutch

4. Age Class*Temperature*Clutch

5. Error

He gives degrees of freedom, type III S, mean square, F value, and P values for each.

My question is if the p value is significant (or not), what does this mean for each source given?

I have a question also about the between subject effects in which the table looks similiar, but maybe I will wait to hear about this one before I ask.

Thanks,

Claire