I have multiple characteristics of a population available (gender, age, place), and I have the answer for each individual for a particular yes-no question available done previously. For example, here is a sample population.
pop = data.frame(y=c(1,1,0,1,0,1,0,1,0,0,0), gender=c(1,1,0,1,0,0,1,1,0,0,0), age=c(21,20,45,32,20,19,33,20,34,35,22), place=c(1,3,2,4,1,3,5,2,1,5,2))
I want to ask the same question to the same population again. The answer may have changed, but there is a reasonable expectation it may not have. What is the way to decide which of these characteristics (single or in combination) is best to use as a strata in stratified sampling to reduce the error? More importantly, how do I justify my choice? (Can I base it on some statistics of the previous)?
I understand from this question answers that running an
anova for each strata might help.
> anova(lm(y~place+gender+age, data=pop)) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) place 1 0.00147 0.00147 0.0086 0.92852 gender 1 1.17581 1.17581 6.9329 0.03376 * age 1 0.36281 0.36281 2.1392 0.18697 Residuals 7 1.18719 0.16960 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
So I should probably use
gender alone as the strata here. Others are not of much use. Is this the correct way?