data extro;
input i1-i10;
array items i1-i10;
do over items;
if items eq . then delete;
end;
if _N_ le 101;
cards;
4	5	5	5	5	5	5	4	4	4
3	5	4	3	5	5	4	3	4	3
2	3	4	3	4	4	4	3	2	4
1	4	2	1	2	4	3	1	1	1
4	5	3	4	1	5	5	3	5	3
3	3	4	3	1	3	3	2	4	3
4	2	3	3	3	3	2	4	4	2
2	4	4	4	4	4	3	2	2	2
1	2	3	2	2	3	1	2	2	2
3	2	4	3	4	2	4	2	4	2
4	5	5	5	5	5	5	4	5	4
3	5	3	4	3	4	4	4	4	1
5	4	4	4	4	4	4	3	5	2
4	5	4	4	5	5	3	5	5	4
2	2	4	2	3	4	2	2	4	2
2	1	2	3	3	2	3	1	2	1
4	2	4	2	4	5	2	1	2	2
2	5	5	4	3	4	5	1	1	4
1	3	5	5	5	5	5	3	5	5
3	5	3	3	3	5	2	3	3	3
2	1	4	2	4	4	2	2	2	2
3	4	4	4	4	5	4	3	3	4
3	3	4	3	5	4	3	3	3	3
3	4	4	3	3	4	4	2	3	3
4	5	4	3	5	5	5	2	5	2
1	5	4	3	4	5	2	4	4	1
2	3	3	3	3	3	2	2	2	1
2	3	2	3	3	3	2	2	2	3
3	5	4	3	4	5	3	1	2	4
3	4	4	3	4	4	2	2	2	2
4	5	4	4	4	4	2	3	3	4
4	5	4	4	5	4	4	2	3	2
2	2	3	2	2	2	2	2	3	2
3	4	4	4	3	4	3	2	3	2
3	4	4	4	4	5	3	3	4	4
1	4	4	4	4	5	2	2	3	2
3	3	4	4	4	4	3	4	5	4
3	4	3	2	1	3	3	3	3	1
2	3	3	2	4	3	4	3	2	4
4	4	3	4	4	3	4	2	4	2
4	4	5	4	5	4	4	3	4	4
4	4	3	4	5	4	4	4	5	3
4	5	4	5	2	2	3	4	5	3
3	5	4	3	5	5	4	3	4	3
1	4	2	3	5	3	2	4	2	4
3	5	5	5	5	5	5	1	4	4
3	2	4	4	5	5	2	4	5	4
4	5	4	4	4	5	4	3	5	3
4	5	4	4	5	5	5	1	.	1
3	3	4	3	4	2	4	3	4	3
3	4	2	3	3	3	3	2	2	3
4	4	5	4	4	5	4	3	4	4
4	5	4	4	4	4	1	2	2	2
3	5	5	4	5	4	3	2	3	2
3	4	4	3	3	4	3	1	4	3
4	5	4	5	4	5	4	3	3	5
4	5	4	5	4	5	5	5	5	4
3	5	5	5	5	4	5	2	4	3
4	5	5	4	4	4	5	3	4	4
3	3	4	3	3	4	2	2	3	2
2	3	4	3	4	4	4	1	1	4
1	1	3	2	3	1	2	3	2	2
3	4	5	3	4	5	5	4	2	2
4	5	5	4	5	5	5	3	5	4
4	5	5	5	5	4	5	4	5	5
5	5	5	5	5	4	5	5	5	3
3	4	4	4	4	5	3	2	4	4
5	5	4	4	4	5	4	3	5	5
4	5	5	5	4	5	4	5	5	4
4	5	5	5	5	5	5	2	5	5
1	3	3	2	2	4	1	2	1	1
4	5	2	5	5	5	1	5	5	2
1	3	2	1	4	5	1	3	4	3
3	4	4	3	3	4	3	3	4	3
3	4	5	4	4	5	5	2	4	3
5	3	3	5	4	5	4	3	5	4
5	5	1	4	5	5	5	4	5	5
1	2	1	1	1	1	1	1	1	1
2	3	4	3	5	3	4	2	3	3
2	2	4	2	4	4	3	2	2	3
1	1	3	2	2	2	2	1	1	1
3	2	4	3	3	3	3	2	3	2
3	2	3	3	2	3	2	2	2	2
4	5	4	5	4	5	4	4	4	4
4	5	5	5	5	5	4	4	5	4
3	5	4	5	5	5	4	3	5	4
2	4	4	2	4	4	2	2	2	2
4	4	5	4	4	4	4	3	4	4
3	2	3	2	3	4	3	2	4	2
2	5	3	4	2	5	2	3	4	1
3	2	2	2	2	4	2	1	3	1
2	3	5	4	3	3	2	2	4	2
3	5	5	5	4	5	4	1	3	2
5	5	5	5	5	5	5	1	5	1
3	2	3	2	4	3	3	2	4	3
4	4	5	4	5	5	5	2	4	5
1	4	5	3	5	5	5	1	3	2
3	2	3	2	4	4	2	2	3	2
4	2	1	2	2	2	1	1	3	1
2	4	4	3	4	5	3	3	3	4
1	3	4	5	2	2	2	4	4	2
3	2	4	3	3	2	3	3	4	2
3	2	2	1	1	2	4	1	1	1
2	2	3	2	3	4	2	2	2	2
1	4	4	2	4	1	1	1	2	1
3	4	4	4	4	4	4	3	3	3
2	4	4	3	4	4	3	2	3	2
4	4	5	4	5	5	5	3	4	3
4	4	4	4	4	4	4	3	4	4
2	3	3	4	3	3	4	2	2	2
1	4	4	1	4	4	3	2	1	3
5	5	4	5	5	5	5	4	4	5
4	3	2	3	3	4	3	1	2	1
3	2	4	3	1	3	2	1	2	1
2	3	3	5	4	3	3	4	3	4
3	5	4	4	5	4	3	4	4	4
1	4	2	4	2	3	2	2	2	2
1	1	3	1	3	3	1	1	1	1
4	5	4	5	4	5	3	4	5	4
4	5	4	5	5	4	4	4	5	5
3	4	5	5	1	1	4	1	1	5
3	4	4	4	4	4	3	3	4	1
1	2	2	2	2	3	1	5	2	2
4	5	1	2	3	4	1	1	1	1
3	5	4	3	4	5	3	3	4	1
3	2	4	3	2	2	2	2	3	2
3	5	4	4	4	5	3	2	3	3
4	5	4	5	5	5	3	4	5	3
4	5	5	4	4	5	4	3	4	3
3	2	4	3	3	3	3	3	3	2
4	5	4	4	4	3	4	3	4	3
3	4	5	5	5	5	4	4	5	2
4	4	4	4	4	4	3	4	5	2
3	3	4	3	3	4	4	3	3	3
4	5	1	5	5	5	5	5	5	5
3	3	3	3	3	4	3	3	4	2
2	4	2	3	2	3	4	5	4	4
4	4	4	4	3	4	4	4	5	2
3	4	4	4	3	3	3	3	3	2
4	4	4	2	2	4	2	3	4	1
4	5	4	4	3	3	2	3	2	2
2	3	2	1	2	4	2	4	2	1
3	3	3	3	3	3	3	2	2	3
5	3	4	4	3	5	2	4	5	4
5	5	4	1	4	5	4	5	5	1
2	4	3	4	3	4	2	4	2	4
1	3	4	1	4	1	5	1	1	1
1	4	2	2	2	2	1	4	2	1
4	5	5	4	5	4	5	4	5	4
3	3	3	4	2	4	5	4	3	4
2	4	3	2	4	5	2	3	2	2
4	5	3	4	4	5	3	4	4	4
3	3	3	3	4	2	3	1	4	4
3	5	3	4	4	5	3	3	4	3
3	4	4	4	4	3	2	3	4	4
3	4	4	3	4	4	2	3	4	3
1	2	3	2	2	3	1	3	3	1
4	3	4	4	3	4	4	3	4	3
4	5	5	5	5	5	5	5	5	2
4	5	5	5	5	5	5	4	5	5
3	3	3	3	4	3	3	3	3	3
3	5	4	3	4	4	3	3	4	4
4	5	5	5	5	5	5	4	5	5
3	4	4	5	4	4	5	2	3	4
2	5	5	5	5	5	4	4	4	5
4	5	5	5	5	5	5	4	5	4
1	4	2	3	2	2	2	1	1	2
3	4	4	4	4	4	4	4	5	4
2	5	5	4	5	5	2	3	5	4
4	5	5	2	5	5	5	2	5	4
3	1	2	1	4	3	1	1	2	1
1	3	2	1	2	4	1	1	1	1
1	2	1	1	1	2	1	1	1	1
3	1	4	1	2	4	1	1	5	1
3	2	4	2	4	4	4	3	3	2
2	2	2	2	2	2	2	1	2	2
3	4	2	3	4	4	3	4	4	3
3	4	4	4	4	.	.	.	.	.
4	5	5	4	5	4	5	4	4	4
3	4	5	3	5	3	3	2	3	3
2	3	5	3	3	4	3	3	4	3
3	3	4	5	3	5	2	4	5	4
3	2	3	3	2	3	3	4	4	1
4	5	4	4	5	5	5	2	4	2
4	3	4	2	4	3	4	1	3	1
4	5	4	4	4	4	5	2	3	4
1	3	2	2	2	4	3	2	1	1
4	4	5	4	5	5	4	2	4	5
5	3	4	5	1	3	3	4	5	4
1	4	3	2	2	3	1	1	1	2
2	1	1	2	2	4	1	3	4	1
1	2	3	1	1	4	1	1	1	2
2	1	3	2	1	3	1	3	2	1
3	3	3	2	3	3	2	2	3	2
3	4	4	4	4	2	4	3	3	2
1	2	3	2	2	3	1	1	1	1
4	5	4	3	4	5	4	3	4	4
proc iml;
*********************************************************************
*  The algorithm in this program is due to 
*  Michael Brannick.  It is a bootstrap estimation procedure.
*********************************************************************;
use extro var{i1 i2 i3 i4 i5 i6 i7 i8 i9 i10};
**********************************************************************;
* the dataset name that you want to analyze follows the word 'use'.
* The name of the variables in the dataset are named after 'var'.
**********************************************************************;
read all var{i1 i2 i3 i4 i5 i6 i7 i8 i9 i10} into x;
*x contains the raw data - it has items as columns and people as rows;
***********************************************************************
*** Set up bootstrap estimates
***********************************************************************;
numbitem = ncol(x); * find the number of items in the scale;
numbsubj = nrow(x); * find the number of people, the sample size;

iter = 100000;
out1 = j(iter,1,-9);    * placeholder for alpha estimates;
do boot = 1 to iter;    * main loop to compute number of estimates;
sample = j(numbsubj,numbitem,-9);   * placeholder for raw data;
******************************************************************;
  do sam = 1 to numbsubj; * pick a sample with replacement;
    row = uniform(0)*100;
    row = round(row);
    if row < 1 then row = 1;
    if row > 100 then row =100;
	sample[sam,] = x[row,];
  end;                    * sample should fill with data;
*print sample;             * check on the data to see if it's legit;
*******************************************************************;
*  Compute Alpha
*******************************************************************;
bigone = j(numbsubj, 1);
means = ((bigone`)*sample)/numbsubj;
xd = sample-(bigone*means);
itemcov = (1/(numbsubj-1))*((xd`)*xd);
**************************************;
*itemcov is the item covariance matrix;
**************************************;
one = j(numbitem,1);
jtphij = (one`)*itemcov*one;
myalpha = 1 - ((trace(itemcov))/jtphij);
myalpha = (numbitem/(numbitem-1))*myalpha;
******************************************************************;
out1[boot,] = myalpha;  * Save alpha for the Nth sample;
end;
*******************************************************************;
*Output the results;
Create out1 from out1;
append from out1;
data d2; set out1;
proc univariate plot normal;
quit;
run;