Simulated data file = ""

A file of simulated data can be constructed from the measures estimated (or anchored) for the main analysis. It will have one simulated observation for each observation in the original data file. Each simulation is unique, so that multiple different simulations can be obtained with the Output Files menu clicking on Simulated Data file.


Simulated data file = filename

The simulated data can be analyzed using Data=filename in the original specification file (or enter at Extra specifications? prompt). Comment out any Dvalues= specifications in the original specification file.


The simulated data file has the basic Facets data format:


; Simulated data matching the empirical data structure

; Ratings of Scientists (edited to illustrate ambiguity in measurement)

; matching: C:\FACETS\examples\subsets.txt

1,2,1,7 ; 9 ; 1,2,1 are facet elements. 7 is simulated. 9 is the original data value.

1,2,2,7 ; 7

1,2,3,4 ; 5

1,2,4,9 ; 8

1,2,5,3 ; 5



Example 1: We need to compute the S.E. for every element including sampling error.

Simulate and analyze 1000 Facets data sets from lfs.txt in one folder.

Save the following as x.bat in c:\Facets\examples, and then double-click on x.bat.

If you are using Minifac, then change \Facets to \Minifac -

The S.D. of the estimates for each element is its total S.E.




echo Loop number %COUNT%

rem do this 1000 times


rem generate simulate data file from lfs.txt

START /WAIT ..\Facets BATCH=YES lfs.txt specfile.out.txt simul=s%COUNT%.txt

rem analyze simulated file

START /WAIT ..\Facets BATCH=YES lfs.txt s%COUNT%.out.txt data=s%COUNT%.txt






Example 2: Simulate data corresponding to various types of rater behavior.

1. We conceptualize the rater effects we want to investigate, for instance "halo effect".

2. We formulate statistical models corresponding to each of the rater effects, for instance "halo effect" = all observations by a rater of a person are the same as the first observation.

3. We propose the parameter values which would correspond to each of those rater effects.

4. We use the statistical models of 2. and the parameter values of 3. to generate the data.


If the models in 2. are Rasch models that can be simulated by Facets, then we can use the parameter values in 3. as anchor values in Facets analyses. Then generate data in 4. using the "simulate data file" option in Facets. In order to make the Facets program run, we give it some data, but it does not matter what the data are, because Facets will use the anchor values, not values estimated from the data, to generate the new data.


If the models in 2. are not models that can be simulated by Facets, then we can formulate the data directly that match what we intend, for instance 3 3 3 3 3 3 could be one rater-person data string for "halo effect", and 4 4 4 4 4 4 could be another data string. Or we can use general-purpose simulation software, such as Simfit,


Example 3: Discover the estimation bias in a set of Facets estimates.

Use the batch file in Example 1, to simulate data matching your dataset. Then compare the standard deviations of the estimates. Here are the numbers from an dataset with 3 facets and 5 simulations:


Population S.D.

Facet 1

Facet 2


Original S.D.




Simulation 1 S.D.




Simulation 2 S.D.




Simulation 3 S.D.




Simulation 4 S.D.




Simulation 5 S.D.




Average of simulation  S.D.s




Estimation bias = Average S.D./Original S.D.





Example 4: The simulated data file is to have a different data pattern than the original data file, but be based on the same element measures.

1. Analyze your original data.

2. Output an anchor file (Anchorfile=) with no data but all elements anchored.

3. Construct a dummy data file of all the same response values, such as "1", with the data pattern you want. You can use Excel to do this.

4. Analyze the anchorfile as your specification file and the dummy data file as your data file.

5. Output the simulated data file. This will now have the data pattern that you want, and match the measures of the original dataset.


Example 5: Simulate a data file with more persons.

1. From your current Facets analysis, output an Anchorfile=

2. In the anchor file, add to the person facet in Labels=, the new person elements and their anchor values. These can be generated using Excel and your desired mean and S.D. of the additional person measures.

3. Add to the data new dummy observations, such as "1", with the new persons, the raters, the items. You can use Excel to do this

4. Analyze the anchorfile with Facets, using the new data file. Everything should be anchored.

5. Ignore the output of the analysis.

6. Output file. Simulate a new simulated data file.

7. Unanchor everything in the anchorfile. Save it as your new specification file.

8. Analyze the unanchored specification file with the simulated data file.

Help for Facets Rasch Measurement Software: Author: John Michael Linacre.

For more information, contact or use the Contact Form

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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 with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments, George Engelhard, Jr. & Stefanie Wind 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
Jan. 5 - Feb. 2, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps),
Jan. 10-16, 2018, Wed.-Tues. In-person workshop: Advanced Course in Rasch Measurement Theory and the application of RUMM2030, Perth, Australia (D. Andrich), Announcement
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April 10-12, 2018, Tues.-Thurs. Rasch Conference: IOMW, New York, NY,
April 13-17, 2018, Fri.-Tues. AERA, New York, NY,
May 22 - 24, 2018, Tues.-Thur. EALTA 2018 pre-conference workshop (Introduction to Rasch measurement using WINSTEPS and FACETS, Thomas Eckes & Frank Weiss-Motz),
May 25 - June 22, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps),
June 27 - 29, 2018, Wed.-Fri. Measurement at the Crossroads: History, philosophy and sociology of measurement, Paris, France.,
June 29 - July 27, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps),
July 25 - July 27, 2018, Wed.-Fri. Pacific-Rim Objective Measurement Symposium (PROMS), (Preconference workshops July 23-24, 2018) Fudan University, Shanghai, China "Applying Rasch Measurement in Language Assessment and across the Human Sciences"
Aug. 10 - Sept. 7, 2018, Fri.-Fri. On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets),
Sept. 3 - 6, 2018, Mon.-Thurs. IMEKO World Congress, Belfast, Northern Ireland
Oct. 12 - Nov. 9, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps),


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