Table 31.1 Differential person functioning DPF pairwise

Table 31 supports person bias, Differential Person Functioning (DPF), i.e., interactions between individual persons and classifications of items. This is useful for estimating sub-test, domain and strand measures for individuals in the context of an overall measure.

 

Tables:

31.2 DPF report (measure list: item class within person)

31.3 DPF report (measure list: person within item class)

31.4 DPF report (person by item-class chi-squares)

31.5 Within-class fit report (item class within person)

31.6 Within-class fit report person class within item)

31.7 Person measure profiles for classes of items

DPF plots

DPF Scatterplots

 

Table 31.1 reports a probability and a size for DPF statistics. Usually we want:

1. probability so small that it is unlikely that the DPF effect is merely a random accident

2. size so large that the DPF effect has a substantive impact on scores/measures on the test

 

Specify DPF= for classifying indicators in item labels. Use difficulty stratification to look for non-uniform DPF using the selection rules.

 

From the Output Tables menu, the DPF dialog is displayed.

 

Table 30 supports the investigation of item bias, Differential Item Functioning (DIF), i.e., interactions between individual items and types of persons.

 

Table 33 reports bias or interactions between classifications of items and classifications of persons.

 

In these analyses, persons and items with extreme scores are excluded, because they do not exhibit differential ability across items. For background discussion, see DIF and DPF concepts.

 

Example output:

 

Table 31.1

 

DPF class specification is: DPF=$S1W1

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

| TAP   Obs-Exp   DPF   DPF   TAP   Obs-Exp   DPF   DPF      DPF    JOINT  Rasch-Welch      KID           |

| CLASS Average MEASURE S.E.  CLASS Average MEASURE S.E.  CONTRAST  S.E.   t  d.f. Prob. Number  Name     |

|---------------------------------------------------------------------------------------------------------|

| 1        -.05  -3.52  1.05  2         .04  -2.80  1.65      -.73  1.95  -.37   2 .7459      1 Adam    M1|

| 1        -.05  -3.52  1.05  3         .39  -2.78> 2.07      -.74  2.32  -.32   0 .0000      1 Adam    M1|

| 1        -.05  -3.52  1.05  4         .00  -2.94E  .00      -.58   .00   .00   0 1.000      1 Adam    M1|

 

DPF Specification defines the columns used to identify Differential Person Function classifications, using the selection rules.

 

TAP CLASS is the item class

Obs-Exp Average is the average difference between the observed and expected responses for the Class by the person. When this is positive, the Class is easier than expected or the person has higher ability than expected.

DPF MEASURE is the ability of the person for this item class, with all else held constant. This is output in the Excel file for the DPF plots.
DPF MEASURE is the same doing a full analysis of the data, outputting IFILE=if.txt and SFILE=sf.txt, then doing another analysis with  IAFILE=if.txt and SAFILE=sf.txt and ISELECT=@DPF=code. ">" and "<" indicate that the scores for the group are extreme.

DPF S.E. is the standard error of the measure

DPF CONTRAST is the difference in the person ability measures, i.e., size of the DPF, for the two classifications of items.

JOINT S.E. is the standard error of the DPF CONTRAST

 

DPF estimates with the  the iterative-logit (Rasch-Welch) method:

t gives the DPF significance as a Student's t-statistic test. The t-test is a two-sided test for the difference between two means (i.e., the estimates) based on the standard error of the means (i.e., the standard error of the estimates). The null hypothesis is that the two estimates are the same, except for measurement error.

d.f. is the joint degrees of freedom by the Welch method.

Prob. is the two-sided probability of Student's t. See t-statistics.

 

-5.24> reports that this measure corresponds to an extreme maximum score. EXTRSCORE= controls extreme score estimate.

5.30< reports that this measure corresponds to an extreme minimum score. EXTRSCORE= controls extreme score estimate.


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


 

 
Coming Rasch-related Events: Winsteps and Facets
Oct 21 - 22 2024, Mon.-Tues. In person workshop: Facets and Winsteps in expert judgement test validity - UNAM (México) y Universidad Católica de Colombia. capardo@ucatolica.edu.co, benildegar@gmail.com
Oct. 4 - Nov. 8, 2024, Fri.-Fri. On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
Jan. 17 - Feb. 21, 2025, Fri.-Fri. On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
May 16 - June 20, 2025, Fri.-Fri. On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
June 20 - July 18, 2025, Fri.-Fri. On-line workshop: Rasch Measurement - Further Topics (E. Smith, Facets), www.statistics.com
Oct. 3 - Nov. 7, 2025, Fri.-Fri. On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com

 

 

Our current URL is www.winsteps.com

Winsteps® is a registered trademark