Residuals / Responses Output File = " "

If a residual/response output filename is specified by Residual file=, a file of residuals from the main analysis is produced. This file is designed for input into other programs with one line per measurable observation. It can be used for calculating other fit statistics and producing specialized diagnostic reports.


Heading lines= control the output of the heading line. CSV= allows tab-delimited and other formats. QM quotation marks, controls whether labels are within quotation marks.


This file can be produced from the Output Files menu by clicking on Residual/Response Output file. This has additional options.


Here is an example of the format with 4 decimal places in the "Select fields" dialog box. The precise format depends on the number of facets in your data:


       Obs       Stp       Exp       Res       Var     StRes        Wt     LProb      Meas     Displ    Status         1         1 Senior sci

         1         1    2.1609   -1.1609    1.6268    -.9102     .3000    -.7140    -.7007    -.7136         1         1     .9859  Avogadro 

         1         1    1.4130    -.4130     .7187    -.4871     .3000    -.2370   -1.3477    -.5746         1         1      9859  Avogadro 

         3         3    1.5490    1.4510     .9248    1.5088     .3000   -1.6711   -1.1816    1.5689         1         1      9859  Avogadro 


The columns are:


Fixed field columns





response as observed in the data file



observed response as renumbered into a count of ordered steps



expected score for this response (decimal places set in selection dialog box)



score residual: (observed Stp - expected Exp)



model variance of observed score around the expected score for this response, the statistical information in this response



standardized residual: residual / sqrt (variance)



weighting (model weight * observation weight * item weight)



natural logarithm of the probability of the observation



sum of the measures of the elements producing the observation (user-scaled: Umean=)



displacement = measure residual = (score residual / variance)*(user-scaling). The measure of element 1 according to this observation is "element measure" for element + "displacement" * (orientation of facet 1)



Status Code


-6 (not used  for estimation)

Repsonse in two multiple-observation ranges, such as 1-4, 2-6,...

-5 (not used)

Responses after end-of-file.

-4 (not used)

Responses only in extreme scores.

-3 (not used)

Responses with invalid elements. Elements for these observations are not defined. See Table 2.

-2 (not used)

Responses in two extreme scores

-1 (not used)

Responses invalid after recounting

A dichotomy or rating scale has less than two categories, so it cannot be estimated.

1 (used for estimation)

Responses used for estimation

2 (used)

Responses in one extreme score


(facet number)

element number for facet 1




(facet number)

element measure for facet 1 from Table 7 (user-scaled)




(facet label)

element label for facet 1





For "Category implies Measure" (C->M) and "Measure implies Category" (M->C) statistics, for each observation in the Facets Residualfile=,

"expected score for this response" - round this to the nearest category number = expected average category

if "expected average category" = "observed response as renumbered into a count of ordered steps" then MC = 1, else, MC = 0.

Compute average of MC for each observed category across all the relevant data for C->M

Compute average of MC for each expected category across all the relevant data for M->C


Example: The "Obs" (observed) is the original data. The "Stp" (step) is the ordinal version of the original data. This version is used for analysis, and is the version on which the "Exp" (expected) and the "Res" (residual) are based. This version may be the same as the original data, or the original data may be transformed either due to explicit instructions by the analyst, or by default operation of Facets.


For instance, suppose that the original data are observations of these three values: 10, 20 and 30. Then, by default, Facets will analyze these observations as the "steps": 10, 11, 12. If the original data are intended to be 10,11,12,13,14,....,28,29,30. Then please specify this is in your Models= statement:


?,?,..., R30K   ; where "K" means "Keep" the original numeration.

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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
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