Same SE for all coefficients of a linear model I am trying to find shellfish densities for a field-based aquaculture program and wanted to understand the impact of reef structure and site on the response.  This experiment only has a sample size of 36. I looked at the distribution and log-transformed it for normality before running a linear model:
model<-lm(data$logged ~ data$reef + data$site)

My output looks like this:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  10.2593     0.1599  64.179  < 2e-16 ***
data$reef2   -1.2147     0.1958  -6.204 1.06e-06 ***
data$reef3   -1.4936     0.1958  -7.629 2.61e-08 ***
data$reef4   -2.6405     0.1958 -13.487 9.03e-14 ***
data$reef5   -1.0867     0.1958  -5.550 6.18e-06 ***
data$reef6   -0.8922     0.1958  -4.557 9.30e-05 ***
data$siteB   -0.6314     0.1384  -4.561 9.21e-05 ***
data$siteC   0.2567     0.1384   1.854   0.0743 .  

Residual standard error: 0.3391 on 28 degrees of freedom
Multiple R-squared:  0.8943,    Adjusted R-squared:  0.8679 
F-statistic: 33.85 on 7 and 28 DF,  p-value: 4.937e-12

What I don't understand is why my SE value are the same for all of my coefficients. Can someone help me understand how to fix this issue?
Thanks.

It doesn't look like there's an issue.
Your standard errors are equal because you presumably have a balanced data set, i.e. two observations for each of the 18 possible combinations of reef and site.
