Table 10.3 Item option & distractor frequencies in misfit order

(controlled by Distractors=Y, OSORT=, CFILE=, PTBIS=)

 

ITEM OPTION FREQUENCIES are output if Distractors=Y. These show occurrences of each of the valid data codes in CODES=, and also of MISSCORE= in the input data file. Counts of responses forming part of extreme scores are included. Only items included in the corresponding main table are listed. These statistics are also in DISFILE=, which includes entries even if the code is not observed for the item. See also Distractor Analysis.

 

OSORT= controls the ordering of options within items. The standard is the order of data codes in CODES=.

 

         ITEM CATEGORY/OPTION/DISTRACTOR FREQUENCIES:  ENTRY ORDER                                                   

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

|ENTRY   DATA  SCORE |     DATA   |      ABILITY     S.E.  INFT OUTF PTMA |                       |                  

|NUMBER  CODE  VALUE |  COUNT   % |    MEAN    P.SD  MEAN  MNSQ MNSQ CORR.| ITEM                  |                  

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

|   13   4       *** |     11  16#|     .79     1.41  .45             .08 |M. STAIRS              | 4 75% Independent

|        1         1 |     30  52 |   -1.85      .99  .18   .8  1.0  -.89 |                       | 1 0% Independent 

|        3         3 |      5   9 |    1.07      .80  .40   .6   .4   .10 |                       | 3 50% Independent

|        5         5 |     15  26 |    2.63     1.06  .28  1.7  1.5   .56 |                       | 5 Supervision    

|        6         6 |      7  12 |    3.25      .81  .33  1.2  1.2   .44 |                       | 6 Device         

|        7         7 |      1   2 |    4.63      .00        .7   .6   .23 |                       | 7 Independent    

|        MISSING *** |      1   1#|   -1.99      .00                 -.12 |                       |                  

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

 * Average ability does not ascend with category score                                                               

 # Missing % includes all categories. Scored % only of scored categories                                             

 

ENTRY NUMBER is the item sequence number.
The letter next to the sequence number is used on the fit plots.

 

DATA CODE is the response code in the data file.
MISSING means that the data code is not listed in the CODES= specification.
Codes with no observations are not listed.

 

SCORE VALUE is the value assigned to the data code by means of NEWSCORE=, KEY1=, IVALUEA=, etc.
*** means the data code is missing and so ignored, i.e., regarded as not administered. MISSCORE=1 scores missing data as "1".

 

DATA COUNT is the frequency of the data code in the data file (unweighted) - this includes observations for both non-extreme and extreme persons and items. For counts weighted by PWEIGHT=, see DISFILE=

 

DATA % is the percent of scored data codes. For dichotomies, the % are the proportion-correct-values for the options.
For data with score value "***", the percent is of all data codes, indicated by "#".

 

ABILITY MEAN is the observed, sample-dependent, average measure of persons (relative to each item) in this analysis who responded in this category (adjusted by PWEIGHT=). This is equivalent to a "Mean Criterion Score" (MCS) expressed as a measure. It is a sample-dependent quality-control statistic for this analysis. (It is not the sample-independent value of the category, which is obtained by adding the item measure to the "score at category", in Table 3.2 or higher, for the rating (or partial credit) scale corresponding to this item.) For each observation in category k, there is a person of measure Bn and an item of measure Di. Then: average measure = sum( Bn - Di ) / count of observations in category.
An "*" indicates that the average measure for a higher score value is lower than for a lower score value. This contradicts the hypothesis that "higher score value implies higher measure, and vice-versa".
The "average ability" for missing data is the average measure of all the persons for whom there is no response to this item. This can be useful. For instance, we may expect the "missing" people to be high or low performers, or to be missing random (and so they average measure would be close to the average of the sample).
These values are plotted in Table 2.6.

 

ABILITY P.SD is the population standard deviation of the ABILITY values = √(Σ (ABILITY - (ABILITY MEAN))²/COUNT)

 

S.E. MEAN is the standard error of the mean (average) measure of the sample of persons from a population who responded in this category (adjusted by PWEIGHT=) = √(Σ (ABILITY - (ABILITY MEAN))²/(COUNT*(COUNT-1))

 

INFT MNSQ is the Infit Mean-Square for observed responses in this category (weighted by PWEIGHT=, and omitting responses in extreme person scores). Values greater than 1.0 indicate unmodeled noise. Values less than 1.0 indicate loss of information.

 

OUTF MNSQ is the Outfit Mean-Square for observed responses in this category (weighted by PWEIGHT=, and omitting responses in extreme person scores). Values greater than 1.0 indicate unmodeled noise. Values less than 1.0 indicate loss of information.

 

PTMA CORR is the point-correlation between the data code, scored 1, or non-occurrence, scored 0, of this category or distractor and the person raw scores or measures chosen by PTBISERIAL=. The computation is described in Correlations. Example: for categories 0,1,2, then the correlation is between [1 for the target score (0 , 1, or 2) and 0 for the other scores ( 1 and 2, 0 and 2, or 0 and 1) ] and the person ability measures for the persons producing each score.

 

ITEM (here, ACT) is the name or label of the item.

 

Data codes and Category labels are shown to the right of the box, if CLFILE= or CFILE= is specified.

 

* Average ability does not ascend with category score. The average ability of the persons observed in this category is lower than the average ability of the persons in the next lower category. This contradicts the Rasch-model assumption that "higher categories <-> higher average abilities."

 

# Missing % includes all categories. Scored % only of scored categories. The percentage for the missing category is based on all the COUNTS. The percentages for the SCOREd categores are based only on those category COUNTs.

 

""BETTER FITTING OMIT" appears in fit-ordered Tables, where items better fitting than FITI= are excluded.


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

Facets Rasch measurement software. Buy for $149. & site licenses. Freeware student/evaluation Minifac download
Winsteps Rasch measurement software. Buy for $149. & site licenses. Freeware student/evaluation Ministep download

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
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
Facets Tutorials - free
Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan
Other Rasch-Related Resources: Rasch Measurement YouTube Channel
Rasch Measurement Transactions & Rasch Measurement research papers - free An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse Rasch Measurement Theory Analysis in R, Wind, Hua Applying the Rasch Model in Social Sciences Using R, Lamprianou El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
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
Rating Scale Analysis - free, Wright & Masters
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
As an Amazon Associate I earn from qualifying purchases. This does not change what you pay.

facebook Forum: Rasch Measurement Forum to discuss any Rasch-related topic

To receive News Emails about Winsteps and Facets by subscribing to the Winsteps.com email list,
enter your email address here:

I want to Subscribe: & click below
I want to Unsubscribe: & click below

Please set your SPAM filter to accept emails from Winsteps.com
The Winsteps.com email list is only used to email information about Winsteps, Facets and associated Rasch Measurement activities. Your email address is not shared with third-parties. Every email sent from the list includes the option to unsubscribe.

Questions, Suggestions? Want to update Winsteps or Facets? Please email Mike Linacre, author of Winsteps mike@winsteps.com


State-of-the-art : single-user and site licenses : free student/evaluation versions : download immediately : instructional PDFs : user forum : assistance by email : bugs fixed fast : free update eligibility : backwards compatible : money back if not satisfied
 
Rasch, Winsteps, Facets online Tutorials


 

 

 

Our current URL is www.winsteps.com

Winsteps® is a registered trademark