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

Abbreviation

Description

1-10

Obs

response as observed in the data file

11-20

Stp

observed response as renumbered into a count of ordered steps

21-30

Exp

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

31-40

Res

score residual: (observed Stp - expected Exp)

41-50

Var

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

51-60

StRes

standardized residual: residual / sqrt (variance)

61-70

Wt

weighting (model weight * observation weight * item weight)

71-80

LProb

natural logarithm of the probability of the observation

81-90

Meas

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

91-100

Disp

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)

101-110

Status

Status Code

Meaning

-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

111-120

(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:

Models=

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


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

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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
Winsteps Tutorials Facets Tutorials Rasch Discussion Groups

 

Coming Rasch-related Events
July 31 - Aug. 3, 2017, Mon.-Thurs. Joint IMEKO TC1-TC7-TC13 Symposium 2017: Measurement Science challenges in Natural and Social Sciences, Rio de Janeiro, Brazil, imeko-tc7-rio.org.br
Aug. 7-9, 2017, Mon-Wed. In-person workshop and research coloquium: Effect size of family and school indexes in writing competence using TERCE data (C. Pardo, A. Atorressi, Winsteps), Bariloche Argentina. Carlos Pardo, Universidad Catòlica de Colombia
Aug. 7-9, 2017, Mon-Wed. PROMS 2017: Pacific Rim Objective Measurement Symposium, Sabah, Borneo, Malaysia, proms.promsociety.org/2017/
Aug. 10, 2017, Thurs. In-person Winsteps Training Workshop (M. Linacre, Winsteps), Sydney, Australia. www.winsteps.com/sydneyws.htm
Aug. 11 - Sept. 8, 2017, Fri.-Fri. On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Aug. 18-21, 2017, Fri.-Mon. IACAT 2017: International Association for Computerized Adaptive Testing, Niigata, Japan, iacat.org
Sept. 15-16, 2017, Fri.-Sat. IOMC 2017: International Outcome Measurement Conference, Chicago, jampress.org/iomc2017.htm
Oct. 13 - Nov. 10, 2017, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
Jan. 5 - Feb. 2, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
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
Jan. 17-19, 2018, Wed.-Fri. Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website
April 13-17, 2018, Fri.-Tues. AERA, New York, NY, www.aera.net
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
Aug. 10 - Sept. 7, 2018, Fri.-Fri. On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Oct. 12 - Nov. 9, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
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New and on-going: Masterchef Australia 2017: Rasch Measurement of Cooks

 

 
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