Let's say I have growth parameters for a population. People who exceed 95th percentile are marked with 1, and people who are otherwise below it are marked as zero. I will then use crosstabs to compare the number of >95th across different regions. My question is, I did not take an equal amount of data from each region. Region 1 could have more >95th because I took more cases from it. Does Chi-Square account for difference in number of cases, I mean does it use the percentage of "1" among ones and zeros in a region or does it just use the 1 and not account for its percentage in that specific region, when outputting the 2-tailed sig.
I also did analysis with BMI, height, weight Z scores that are >1.96 and <-1.96 which is also equivalent to 95th and 5th, and then compared mean Z values of different regions using anova for >2 regions, and 2 sample independent t-tests for dichotomous variables like gender.
Which of the methods is the best way to go in your opinion? I feel that converting my continuous percentile height, bmi and weight data to nominal variables(count the >95th) for the sake of chisquare analysis among regions or gender would yield inferior quality results.
which one of these:
1) Prevalence of tall stature in region 1 vs 2 was 1% vs 2% respectively with a mean z-score/percentile of xx vs xx (P_value from t-test of 2 means of z/percentile scores with NO CHISQUARE p-values)
2) Among those who are >95th, mean z/percentile score was 96% in group 1 vs 98% in group 2(p_value from t-test of 2 means of z-score NO overall prevalence so NO CHISQUARE TOO)
3) 1% tall stature in region 1 vs 2% in region 2(P-value from crosstabs chisquare)
Which do you think is the best method to put it in terms of significance and study strength. If you think of other ways, PLEASE do tell me.