SD=Standard error * sqrt(N);
QC: I checked the algorithm using SAS. The result was consistent with the algorithm (i.e., SD=standard error*sqrt(N)).
proc means data=sashelp.class mean std stderr n;
Stadard Error 1.1762317
input Gender $ NumYes Total;
Response="Yes"; Count=NumYes; output;
Response="No "; Count=Total-NumYes; output;
Men 30 100
Women 45 100
proc print noobs;
var Gender Response Count;
proc freq order=data;
table Gender * Response / chisq riskdiff;
Check the number of cases within each group (i.e. treatment and control group).
Check if the number of treatment school and control school is balanced within block.
The UCLA site explains Cronbach's alpha as the average internal correlation among survey items. It also says that it is not a measure of unidimensionality. Rather, it is a measurement of internal consistency (though just intuitively I feel what is coherent tends to be also uni-dimensional... I think the point is that the measure is most optimal by design for the assessment of internal correlation, not dimentionality.
Standardized versus Raw
This SAS website says one should use the standardized version of the measure (as opposed to raw).
It says: "Because the variances of some variables vary widely, you should use the standardized score to estimate reliability."
A note to myself: Does this mean if I standardized all items before the analysis, I get the same value for raw and standardized? I can experiment this.
I wrote this Excel program to calculate sample size for surveys.
www.nippondream.com/file/sample size calculation 11 30 2015.xlsx
Phil of SAS helped me identify this function. Thank you.
T-test conducted in PROC GLIMMIX (or most likely other regression procedures) is expressed in Excel function as:
=2*(1-T.DIST( T_VALUE , DEG_OF_FREEDOM ,TRUE))
where T_value must be an absolute value of the original t-value (e.g., if -2 then 2).
This expresses CDF (cumulative distribution function), not PDF (probability density function). I will explicitly discuss what these are in the near future.
I wanted to know how much of statistical results (off PROC GLIMMIX in this case) comes from SAS's internal computation (i.e., I can't replicate results outside SAS) and how much of it can be done in Excel given what I get from SAS output.