This permits the replacement of data values in your data file with other values, without altering the data file. Data values are in the original data file format, specified in CODES=. If specified as decimals, they are rounded to the nearest integers. Lines can be in EDFILE= in any order, and item numbers and person numbers can be skipped.Additional persons can be entered beyond those in DATA= or after END LABELS. Values can be in any order, but are more speedily processed when sorted in person-entry order. Person labels can be inserted or changed using PLFILE=.

 

The first line of the EDFILE= is shown in the Analysis window so that it can be checked:

 

 Processing EDFILE= data edit from: C:\Users\ADMINI~1\AppData\Local\Temp\ZED967WS.TXT

 Processing first active EDFILE= observation: 1-35         19       0

 Processing as: 1 (Adam    M) 19 (00019) 0

.................................................. 50000  (each dot is 1,000 observations)

.................................................. 150000  (each dot is 2,000 observations)

.................................................. 300000  (each dot is 3,000 observations, and so on)

 

with totals at the end:

 OBSERVATIONS IN EDFILE=: 123

    OBSERVATIONS UPDATED: 103

  OBSERVATIONS UNCHANGED: 20

 

 

 

 

Example 0. You want to merge, "rack", the data from two tests:  To "stack" the data, use DATA=file1.txt+file2.txt

Use EDFILE= format, and combine file1.txt and file2.txt into one EDFILE= file. Person entry numbers must match but can be in any order. If items have the same number, then the second occurrence overwrites the first.

 

For instance:

file data in EDFILE= format , or input file1.txt as a standard DATA= file:

23    1-17 0100100100100111  ; person 23, items 1-17, responses: Note: no spaces in 1-17. 1 - 17 fails.

.....

file2 data in the same or only EDFILE=

23  18-30 1010110001101  ; person 23, items 18-30, responses

.....

 

For person labels not in DATA=, use PLFILE=

 

Example 1: In your MCQ test, you wish to correct a data-entry error. Person 23 responded to item 17 with a D and item 18 with an A, not whatever is in the data file.

 

EDFILE=*

23 17 D     ; person 23, item 17, data value of D

23 18 A     ; person 23, item 18, data value of A

*

 

or

 

EDFILE=*

23 17-18 DA     ; person 23, item 17 and 18, data values of D and A

*

 

Example 2: Person 43 failed to read the attitude survey instructions correctly for items 32-56. Mark these missing.

 

EDFILE=* 

43 32-56 " " ; person 43, items 32 to 56, blanks are missing data.

*

 

Example 3: Persons 47-84 are to be given a rating of 4 on item 16.

 

EDFILE=* 

47-84 16 4 ; persons 47 to 84, item 16, data value of 4

*

 

Example 4: Items 1-10 are all to be assigned a datum of 1 for the control sub-sample, persons 345-682.

 

EDFILE=* 

345-682 1-10 1 ; persons 345-682, items 1 to 10, data value 1.

*

 

Example 5: Missing data values are to be imputed with the values nearest to their expectations.

 

a. Produce PFILE=, IFILE= and SFILE= from the original data (with missing).

b. Use those as PAFILE=, IAFILE=, SAFILE= anchor files with a data set in which all the original non-missing data are made missing, and vice-versa - it doesn't matter what non-missing value is used.

c. Produce XFILE= to obtain a list of the expected values of the originally missing data.

d. Use the EDFILE= command to impute those values back into the data file. It will round expected values to the nearest integer, for us as a category value.

 

EDFILE=*

17 6 2.6 ; persons 17, item 6, expected value 2.6, imputed as category "3".

*

 

Example 6: All responses to item 6 for males "M" in column 6 of person label are to be coded as "missing", character ".":

 

EDFILE=*

"?????M" 6 .

*

 

Example 7: We want to do Examples 1, 2, 3, 4, 5, 6 all at once to our dataset:

 

EDFILE=*

23 17 D     ; person 23, item 17, data value of D

43 32-56 " " ; person 43, items 32 to 56, blanks are missing data.

47-84 16 4 ; persons 47 to 84, item 16, data value of 4

345-682 1-10 1 ; persons 345-682, items 1 to 10, data value 1.

17 6 2.6 ; persons 17, item 6, expected value 2.6, imputed as category "3".

"?????M" 6 .

*

 

Example 8: We want person group X (in column 4 of the person label) except for subgroups 11, 24 (in columns 6, 7 of the person label):

PSELECT = "???X?{12}{14}"  ; this selects X and 11, 14, 21, 24

EDFILE=*

"?????14" "?" . ; convert 14 to missing data

"?????21" "?" . ; convert 21 to missing data

*

 

Example 9: Exceedingly unexpected responses are to be coded "missing". (It is easier, but not as exact, to use CUTLO= and CUTHI= to trim the observations).

 

Either
Extract into Excel the list of unexpected observations from Table 6.6 or Table 10.6.

Or
Output the XFILE= to Excel
Sort by unexpectedness (standardized residual)
Delete all rows except for the responses you want to code missing

 

Then
Rearrange the columns: Person Item
In the third column put the desired missing data code.
Copy-and-paste the three columns into a text file.
In your Winsteps control file:
EDFILE = (text file name)
Rerun the analysis

 

Example 9: All data in a separate EDFILE= data file.

 

 

Example 10: Item bank recalibration for computer-adaptive tests (CAT) or similar.

Here is a method is that maintains the accuracy of previously-reported person measures as much as possible:

1.collect up all the relevant data and format the data into a rectangular dataset or equivalent. EDFILE= is useful for this.

2.anchor all the persons at their report measures

3.anchor all items at their item-bank difficulties, and rating-scale structures (if polytomies) SAFILE=

4.analyze the dataset

5.the item displacements tell us which items have drifted by how much.

6.items with displacements of more than 0.5 logits, that are also bigger than the item S.E.s, are candidates for recalibration.

7.unanchor all the displaced items. Keep everything else anchored

8.reanalyze the dataset. The displaced items will now have revised difficulties in the context of the anchored persons.