1. Practical Introduction to PARSCALE
Paul K. Crane, MD MPH
Internal Medicine
University of Washington

2. Outline Introductory comments Getting a dataset prepared for PARSCALE
Creating PARSCALE code
PARSCALE output
Reading theta estimates from PARSCALE
Final comments

3. Introductory comments
PARSCALE is not user friendly
Available from SSI for $250 plus $40 for the text (checked August 23, 2004)
Flexible; does many IRT applications well
Technical support (Leo Stam) is very good
This talk is not approved, sanctioned, or anything else by SSI – I have no financial relationship with SSI other than I own a copy of PARSCALE

4. Pre-PARSCALE PARSCALE requires an ASCII formatted dataset
Responses to particular items need to be in specified columns
Need to deal with missing data and with 2 digit answers
PARSCALE can ignore commas or any other text

5. Pre-PARSCALE, slide 2
PARSCALE is VERY unhappy with 0 categories (it doesn’t even try)
PARSCALE is pretty unhappy with small response categories (it may not converge on a solution)
Our rule of thumb is to combine categories until there are at least 20 observations in each category; this has always worked for us (where other things have not)

6. Ensure data will be in the same column locations
Make sure all id numbers have the same number of digits. Add a really big number (1,000,000) to all so that they are all 9 digits, for example
Make missing values an X. In STATA: change missing values to .x, then change .x to X in Word
.mvencode .=
.mvdecode =.x

7. Item screening
For each item, we need to make sure there are no empty categories, and plan ahead for future category merging
In STATA, tabulate each item, one at a time
If the item is highly skewed, don’t export it at all (All but <20 is “highly skewed”)
Category
Number
If the item needs recombining, make a note of that and go ahead and export it
Number

8. First step from STATA to PARSCALE
.mvdecode .=
.mvencode =.x
.gen newid=id
.outfile newid item01 item02 item03 … using “D:/work/…/cogtest1.txt”, wide comma
Obviously omit items with too little variance from the item list

9. Second step from STATA to PARSCALE
Open the file in Word
Replace .x with X
Replace ,10 with ,A
,11 with ,B
Etc.
The text file should now look square to your eye

10. Third and final step from STATA to PARSCALE
Missing data: go to the first line of your text file, and hit return.
First line should read:
NPKEY X,X,X,X,X,X,X,X,X,X,X,X,X,X…
Have to specify to PARSCALE that this file is where to look for the missing value code
The X’s should look like your other lines – same number of X’s as items (1 per block)
Save the dataset as a text only file with linebreaks in your PARSCALE directory

11. PARSCALE code – slide 1
Open up PARSCALE and open a new document (or, better, open an old document and modify)
TITLE: need a title, needs to be 2 lines.
2nd line of title. No need for semicolon.
>COMMENT if you want. Needs semicolon;
>FILES DFNAME=‘cogtest1.txt', NFNAME=‘cogtest1.txt', SAVE;
This tells PARSCALE where the data file is and where the missing data key is. The SAVE command tells it you want to save some output

12. PARSCALE code – slide 2
>SAVE PARM=‘cogtest1.par', FIT=‘cogtest1.fit', SCORE=‘cogtest1.sco';
This tells PARSCALE to save the parameters in a file (which I recommend), as well as a separate file with the theta scores (which I also recommend).
Fit statistics are less helpful so far.

13. PARSCALE code – slide 3 >INPut NTEst=1, LENGTH=109, NID=5, NTO=109;
(5A1,37(1X,1A1)/40(1A1,1X)/32(1A1,1X))
First line tells PARSCALE that we have 1 test, 109 items, 5 characters for ID, and 109 items in total
Second line tells PARSCALE what the data look like exactly: 5 alphanumeric characters on the first line, followed by 37 repetitions of (skip a column, read an alphanumeric character), then a line break, 40 (skip 1-read 1), line break, 32 skip 1-read 1
REALLY picky about syntax; note the semicolons and parentheses

14. Advanced comments regarding line length
PARSCALE apparently wants to read up to 5 lines of data for each person
If there are more data than that, you can increase the number of characters in a line; can certainly handle 80
So 80 characters is 40 data points (remember the commas), * 5 lines is 200 data points
Will become an issue when we are equating and/or doing intensive DIF analyses

15. PARSCALE code – slide 4
>TEST1 TNAme=‘3MS_001', ITEMS=(1(1)109), NBL=109, SLOPE;
This tells PARSCALE that there is 1 test, whose name is the 3MS_001, the items are numbered by 1’s, there are 109 blocks, and start from a value of 1.0 for the slope parameters

16. PARSCALE code – slide 5
>BLOCK001 BNAME=(`byr’), ORI=(0,1,2), MOD=(1,1,2), NIT=1, NCAT=3;
The first block’s name is byr, it was originally coded 0,1, 2; recode the categories into 1,1, 2; there is 1 item; and there are 3 categories
Need to put longer names (>8 characters) in single quotes
Space these commands so they look pretty (first thing Leo Stam does)
Write a separate block for each item

17. PARSCALE code – slide 6 >BLOCK002 …>BLOCK 109 …;
>CALIB GRADED, LOGISTIC, SCALE=1.7, NQPT=11, CYCLES=2000, CRIT=.001;
This tells PARSCALE to calibrate the data using the graded response model and the logistic formulation; use a value of 1.7 for the D parameter, use 11 quadrature points; iterate 2000 times; and stop when nothing changes cycle-to-cycle by more than 0.001

18. PARSCALE code – slide 7 >SCORE EAP;
This tells PARSCALE to use expected a posteriori scoring
Another option is SCORE MLE; which uses maximum likelihood scoring
Use EAP if you have people with perfect scores; otherwise, those people will have missing scores
If no one has a perfect score, MLE is theoretically better
That’s actually it! Not so hard, was it??

19. PARSCALE output
Phase 0: typing errors, syntax errors, file specification errors. Reads in 1st 2 data lines.
Phase 1: item preparation: are there empty cells? Calculations for initial slope (suppressed) and location values
Phase 2: E-M cycles, calibrating location and slopes; displays a summary of the parameters for all the items; chi squared item fit statistics
Phase 3: Scores for each person

20. From PARSCALE back to STATA – slide 1
Recall we used the SAVE command to save the scores of people:
>SAVE PARM=‘cogtest1.par', FIT=‘cogtest1.fit', SCORE=‘cogtest1.sco';
Within an empty session of STATA, try to open up that file (it won’t let you)
.infile newid _skip(11) theta setheta using …/cogtest1.sco
Copy the whole file name from your error statement and paste it into the command line

21. From PARSCALE back to STATA – slide 2
Enter the data editor in STATA, and copy newid001, theta001, and setheta001
Paste these variables into the data editor of your master file with your covariates
Make sure the newid’s are the same
.corr newid newid001
.drop newid001
Standardize theta
.gen cogscore001 = int((15*theta001)+100)
Standardize setheta001
.gen cogerror001 = (15*setheta001)

22. Final comments It looks more obscure than it is There are other tricks
I think these tricks and tools are foolproof
At least several colleagues have been able to code in PARSCALE using these outlines
There are other tricks
Can use saved parameters to score new people
Can use other IRT models, including Rasch / Rating Scale or Partial Credit models
Customer support is good, if all else fails
I hesitate to do this: