XFILE= analyzed response file

If XFILE=filename is specified in the control file, a file is output which enables a detailed analysis of individual response anomalies.

 

XFILE=? opens a Browse window

 

This file contains 4 heading lines (unless HLINES=N) followed by one line for each person-by-item response used in the estimation. Each line contains:

 

Field number

Description

Number Format

Abbreviation

1

Person number

7.0

PERSON

2

Item number

7.0

ITEM

3

Response value (after scoring with KEY=, IVALUE=, etc.)

4.0

OBS

4

Response value (after scoring and recounting. This usually only happens for rating scales with unobserved intermediate categories and STKEEP=NO.)

4.0

ORD

5

Expected response value. For dichotomous items, probability of success. This is computed from the measures without correction by STBIAS=. If you want to see the values with STBIAS=YES, then:

1. Perform the analysis with STBIAS=YES

2. Output IFILE=if.txt PFILE=PF.txt SFILE=sf.txt

3. Perform the analysis again with STBIAS=NO IAFILE=if.txt PAFILE=pf.txt SAFILE=sf.txt

7.3

EXPECT

6

Modeled Variance of observed values around the expected value

This is also the statistical information in the observation.

Square root (modeled variance) is the observation's raw-score standard deviation

7.3

VARIAN

7

Standardized residual: (Observed - Expected)/Square root (Variance). This approximates a unit-normal deviate. Values outside ±2 are unexpected.

7.3

ZSCORE

8

Score residual: (Observed - Expected)

7.3

RESIDL

9

Person measure in USCALE= units

7.2*

PERMEA

10

Item measure in USCALE= units

7.2*

ITMMEA

11

Measure difference (Person measure - Item measure) in USCALE= units

7.2*

MEASDF

12

Log-Probability of observed response. These can be summed for the Log-Likelihood Chi-Square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC), etc.

7.3

L-PROB

13

Predicted person measure from this response alone in USCALE= units

7.2*

PPMEAS

14

Predicted item measure from this response alone in USCALE= units

7.2*

PIMEAS

15

Modeled Kurtosis of the observed values around their expectation

7.3

KURT

16

More Probable of the Responses to be observed

4.0

MPR

17

Response weight (person weight x item weight)

7.3

WEIGHT

18

Response status: 0 = Not scored, 1 = Standard, 2 = in Extreme person score, 3 = in Extreme item score, 4 = in Extreme person and item scores

2.0

ST

19

Response code in data file

A

CODE

20

Person label

A

PLABEL

21

Item label

A

ILABEL


7.3 means "7 columns with 3 decimal places".

2* means Decimal Places set by the XFILE= dialog box from the Output Files menu or UDECIM= if not set..

A means alphanumeric character field

 

 

Fields can be selected interactively and the default field selection changed at XFILE= dialog box.

 

If CSV=Y, the values are separated by commas. When CSV=T, the commas are replaced by tab characters. For "non-numeric values in quotation marks", specify QUOTED=Y.

 

This file enables a detailed analysis of individual response anomalies. The response residual can be analyzed in three forms:

1) in response-level score units, from [(observed value - expected value)].

2) in logits, from [(observed value - expected value)/variance].

3) in standard units, [(observed value - expected value)/(square root of variance)].

 

The log-probabilities can be summed to construct log-likelihood and chi-square tests. Asymptotically, "chi-square = -2*log-likelihood".

 

Predicted person measure:  Imagine that this observation was the only observation made for the person ... this value is the measure we would predict for that person given the item measure.

Predicted item measure: Imagine that this observation is the only observation made for this item ... this value is the measure we would predict for that item given the person measure.

The formulas are the same as for a response string of more than 1 observation. For dichotomies, see www.rasch.org/rmt/rmt102t.htm and for polytomies www.rasch.org/rmt/rmt122q.htm

 

Example 1: You wish to write a file on disk called "MYDATA.XF" containing response-level information for use in examining particularly response patterns:

  XFILE=MYDATA.XF

 

Example 2: You wish to compute differential item functioning, DIF, for a specific classification group of people:
If Table 30 is not suitable, here is a simple approximation:
Since one item does not have enough information to measure a person, for item bias we have to do it on the basis of a classification group of people.

From the XFILE,

add the "score residuals" (not standardized) for everyone in classification "A" on a particular item.

Add the "modeled variance" for everyone in the classification.

Divide the residual sum by the variance sum. This gives an estimate of the DIF for classification "A" relative to the grand mean measure.

Do the same for classification "B" on the same item.

To contrast classification "A" with classification "B" then

DIF size "AB" =DIF estimate for "A" - DIF estimate for "B"

A significance t-test is t =DIF size "AB" / square root ( 1/variance sum for classification A + 1/variance sum for classification B))

 

Example 3: You want to convert lucky guesses into missing data for some items.

Click on Output Files menu

Click on XFILE=

In the XFILE= Fields dialog box, type the Item numbers you want.

Click on OK

Output to Excel (Temporary file)

Sort the Excel file on Residual

The largest positive residuals are the lucky guesses

Rectangular- Copy the person entry numbers and the item entry numbers into your Winsteps control file:

EDFILE=*

person  item  .    ; the . is to indicate "make this response missing data"

person  item  .    ; the . is to indicate "make this response missing data"

person  item  .    ; the . is to indicate "make this response missing data"

*

 

Save your Winsteps control file.

In the next analysis these unexpected correct responses will be scored as missing. The raw scores will also change.


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Rasch Publications
Rasch Measurement Transactions (free, online) Rasch Measurement research papers (free, online) Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Applying the Rasch Model 3rd. Ed., Bond & Fox Best Test Design, Wright & Stone
Rating Scale Analysis, Wright & Masters Introduction to Rasch Measurement, E. Smith & R. Smith Introduction to Many-Facet Rasch Measurement, Thomas Eckes Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr. Statistical Analyses for Language Testers, Rita Green
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Journal of Applied Measurement Rasch models for measurement, David Andrich Constructing Measures, Mark Wilson Rasch Analysis in the Human Sciences, Boone, Stave, Yale
in Spanish: Análisis de Rasch para todos, Agustín Tristán Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez
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Coming Rasch-related Events
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April 29, 2017, Sat., 16:35 to 18:05. NCME Presidents Invitational Symposium: a new book commemorating Ben Wright's life and career, 16:35 to 18:05, San Antonio, TX, www.ncme.org
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Jan. 17-19, 2018, Wed.-Fri. Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website
May 25 - June 22, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
June 29 - July 27, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com
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