I have data from a pre-assessment and a post-assessment. Each question/statement was coded as dichotomous (yes/no). I took the sum of the questions for both pre (shown below) and post using an array:
array xst_itp [12] xst_1 xst_2 xst_3 xst_4 xst_5 xst_6 xst_7 xst_8 xst_9 xst_10 xst_11 xst_12;
pre_tot = sum(of xst_itp[*]);
pre_miss_n = cmiss(of xst_itp[*]);
if st_miss_n = 12 then st_complete = 0;
if st_miss_n < 12 then st_complete = 1;
I then ran a simple paired ttest:
proc ttest data = all_data;
paired pre_tot*post_tot;
run;
My question is, is it okay to treat this data as continuous data? Do I need to treat it as count/discrete instead? If so, what analysis would you suggest in SAS?
My other question is, do I need to check the normality of both pre_tot and post_tot? And if one is not normal, do I need to use a Wilcoxon signed-rank test instead?
@lboyd wrote:
My question is, is it okay to treat this data as continuous data? Do I need to treat it as count/discrete instead? If so, what analysis would you suggest in SAS?
That's more of a subject specific question in my opinion.
It depends on how you coded things and such. It's more counts really than a continuous distribution.
My other question is, do I need to check the normality of both pre_tot and post_tot? And if one is not normal, do I need to use a Wilcoxon signed-rank test instead?
You should check the normality, but I think the assumption still holds if the distributions are the same.
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