Missing data codes =

This is for 32-bit Facets 3.87. Here is Help for 64-bit Facets 4

Codes that indicate "missing" whenever they appear in the data may be specified with Missing=. They will then be ignored whenever they appear. "Missing" refers to missing observations. It means "observation unknown or observation not collected, so ignore it".

 

There cannot be "missing element numbers". If the element number is unknown (for instance, you don't know which item was being rated), then the observation cannot be included in the analysis - so omit it from the data or comment it out with ";". If the element number does not apply (for instance, an item that does not require a rater), then the element number is null, usually 0, the Keep as null= value.

 

Facets treats as "missing data", any data value (or space or omitted) outside the specified numerical range for a valid observation.

 

Example: Models=?,?,?,R2

Valid data for R2 are 0,1,2 so anything else is treated as missing data.

 

The first time Facets encounters a value outside the valid range (other than blank or omitted), it issues a warning message, just in case you have a data entry error, but proceeds with the analysis:

Check (2)? Invalid datum location: "___" at or near line: ___. Datum "_" is too big or non-numeric, treated as missing.

 

I recommend being explicit about missing data. Use the same code every time, e.g., ".".

If you do this, then you can specify:

Missing = .

so that Facets will not issue a warning message for ".".

 

So in your file you might have:

Facets=4

Models=?,?,?,?,R6

1,2,3,1-4, 3, 2, ., 4 ; where the response to item 3 is missing

 

Example 1: Missing observations have been entered in the data file with code "9". These are to be ignored and bypassed.

Missing = 9

Facets = 2

Data=

22, 36, 1 ; this observation of "1" is analyzed

23, 37, 9 ; this observation of "9" is ignored as missing data

 

Example 2: Codes "^", "." and "#" are all to be ignored as observation values.

Missing = ^, ., #

Facets = 3

Data=

22, 0, 17, 1 ; this observation of "1" is analyzed. Element "0" means that facet 2 does not apply to this observation.

23, 3, 13, # ; this observation of "#" is ignored as missing data

 

Example 3: To make specific observations into missing data use the missing data model.

Models=

23, 37, M ; all data points with elements 23 and 37 are treated as missing


Help for Facets Rasch Measurement and Rasch Analysis Software: www.winsteps.com Author: John Michael Linacre.
 

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Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn, 2024 George Engelhard, Jr. & Jue Wang Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Statistical Analyses for Language Testers (Facets), Rita Green Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
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Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Rasch Models for Measurement, David Andrich Constructing Measures, Mark Wilson Best Test Design - free, Wright & Stone
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Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias Diseño de Mejores Pruebas - free, Spanish Best Test Design A Course in Rasch Measurement Theory, Andrich, Marais Rasch Models in Health, Christensen, Kreiner, Mesba Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen
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