Scoring table construction

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

 

Facets and Winsteps or Ministep (easier):

 

1.Output Files menu: Output a Winsteps file from Facets with anchor values. Specify the "item" facet as the Winsteps item facet and any other facet as the person facet.

2.In the Winsteps file, remove PAFILE= and leave IAFILE= and SAFILE=.

3. Run the Winsteps analysis (Ministep is good for 25 or fewer items).

4.Ignore everything but produce Table 20.

 

              TABLE OF MEASURES ON TEST OF 13 ITEM

----------------------------------------------------------------------------

| SCORE  MEASURE    S.E. | SCORE  MEASURE    S.E. | SCORE  MEASURE    S.E. |

|------------------------+------------------------+------------------------|

|    13    -6.34E   1.82 |    40    -1.35     .28 |    67     1.33     .35 |

 


 

Facets only (much more difficult): Constructing a score-to-measure table for a many-faceted situation can be challenging. Here is a procedure:

 

1. Do an analysis of your data. Let's assume it has 3 facets: 1. Examinees. 2. Raters. 3. Items. We want a score-to-measure table for the items with a standard rater.

 

2. If "disconnected" subsets are reported, then resolve that through group-anchoring or direct anchoring. We need to have the set of definitive measures.

 

3. Write out an anchorfile= with everything anchored.

 

4. Delete the current data= reference. We will construct a new data file

 

5. Replace the raters with a "standard" rater in the rater facet, with measure 0 (or whatever leniency you want your standard rater to have)

 

2, Rater, A ; assuming facet 2 is the rater facet

10, standard rater, 0 ; this is to be the standard rater for the score table

*

 

6. Identify the items that are to be part of the standard test. They must have anchor values. For convenience, let's assume there are 5 items. Renumber them with element numbers 1 to 5. The previous element numbers won't be needed here. Let's say they are on a rating scale 0-4. So the possible scores range from 0 to 5*4 = 20.

 

3, Item, A ; assume facet 3 is items

1, (whatever its name is), (whatever its anchor measure value is)

....

5, (whatever its name is), (whatever its anchor measure value is)

*

 

7. delete all current examinee element labels. Replace them with artificial examinee labels. One for every possible score on the test, e.g.,

 

1, Examinee ; these aren't anchored

1000 = Examinee who scored 0

1001-1020 ; I am assuming that 20 is the maximum score on a 5 item test.

*

 

8. set up a new data file, giving the corresponding scores. There are 5 items in my example, numbered 1-5, and the possible scores are 0 to 20 assigned to examinees 1000 to 1020. Rater 10 is the standard rater. So this is the data file:

 

data=

1000, 10, 1-5, 0, 0, 0, 0, 0 ; raw score of 0

1001, 10, 1-5, 1 ,0, 0, 0, 0 ; it does not matter which item is item 1. It is only the raw score that matters.

1002, 10, 1-5, 2 ,0, 0, 0, 0

1003, 10, 1-5, 3 ,0, 0, 0, 0

1004, 10, 1-5, 4 ,0, 0, 0, 0

1005, 10, 1-5, 4, 1, 0, 0, 0

1006, 10, 1-5, 4, 2, 0, 0, 0

1007, 10, 1-5, 4, 3, 0, 0, 0

1008, 10, 1-5, 4, 4, 0, 0, 0

1009, 10, 1-5, 4, 4, 1, 0, 0

1010, 10, 1-5, 4, 4, 2, 0, 0

1011, 10, 1-5, 4, 4, 3, 0, 0

1012, 10, 1-5, 4, 4, 4, 0, 0

1013, 10, 1-5, 4, 4, 4, 1, 0

1014, 10, 1-5, 4, 4, 4, 2, 0

1015, 10, 1-5, 4, 4, 4, 3, 0

1016, 10, 1-5, 4, 4, 4, 4, 0

1017, 10, 1-5, 4, 4, 4, 4, 1

1018, 10, 1-5, 4, 4, 4, 4, 2

1019, 10, 1-5, 4, 4, 4, 4, 3

1020, 10, 1-5, 4, 4, 4, 4, 4 ; raw score of 20

 

9. Perform this analysis. Items, raters and the rating scale are anchored. Examinees are not. Examinees 1000 to 1020 will have scores 0 to 20. And so their measures correspond to 0 to 20. Ignore all other statistics.

 

10. The examinee scores and measures provide your score-to-measure table. Use the Output Files menu, Scorefile= to write the scores and measures to Excel for easy import wherever they are needed.

 

11. If you want the measure range to match the original raw score range, then

USCALE= (max possible score - minimum possible score) / (measure for max score - measure for min score)

UIMEAN =  ( minimum possible score) ) - ( measure for min score * USCALE )

and, if you want output to look neat

UDECIM = 0

 


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

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Rasch Books and Publications: Winsteps and Facets
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 Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Rasch Models for Solving Measurement Problems (Facets), George Engelhard, Jr. & Jue Wang 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
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
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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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