I tried to find through the forum to get some help and it was helpful to get some information but still have some confusion so I am posting the question here.

So my major is Soil science and I am looking at the effects of a product (fertilizer) on soil microorganisms and soil enzymes. I am using SPSS for my analysis.

Treatments= A, B, C, D

Levels= 3 (samples were collected from all treatments at three different times)

Number of replicate= 3

Total number of samples = 60

I know that I have to run Repeated measures One Way Anova.

Question: What other statistical analysis should I look for (other than Repeated measures one way ANOVA)? And I have seen many people have performed Principal component analysis but that cannot be done here as far as I know because of small number of samples. Can someone please suggest what additional analysis should I perform?

Any help/suggestion would be appreciated.

Thanks ]]>

I am having some conceptual difficulties in understanding and interpreting interaction terms (between a dummy and a continuous variable) in OLS regressions. I was hoping someone could help me out.

Let’s say we have an equation where an individual’s years of schooling is the outcome, and we have the individual’s parental income (a continuous variable) and the individual’s gender (=1 if male) as controls. And we want to see whether parental income has a differential effect by gender. So the equation would be as follows:

YearsofSchool = β0 + β1 ParentIncome+ β2 Male+ β3 ParentIncome * Male + ε

I understand that β1 denotes the effect of parental income on schooling for females, and β1+ β3 for males. So, for example, if β1 is significant, we can say that parent’s income is significantly associated with schooling of girls. Similarly, we test for the significance of β1+ β3, and conclude whether or not income has a significant association with schooling for boys.

However, what I don’t understand is how to interpret the coefficient on the interaction term, β3. What does β3 denote in this case and how should we interpret it? E.g., if we find that parental income is not significantly associated with schooling for either boys or girls, but the interaction term is significant, what does that mean? Or if, parental income is significantly associated with schooling for girls only, but the interaction term is insignificant, what would that mean?

I look forward to your help. ]]>

Let X = number of people wearing headphones ]]>