Table 30.2, 30.3 DIF bias/interaction = Item measures for person classes

Table 30 supports the investigation of item bias, Differential Item Functioning (DIF), i.e., interactions between individual items and types of persons. Specify DIF= for person classifying indicators in person labels. Item bias and DIF are the same thing.  The item measures by person class are plotted in the DIF Plot.

 

In Table 30.1 - the hypothesis is "this item has the same difficulty for two groups"
In Table 30.2, 30.3 - the hypothesis is "this item has the same difficulty as its average difficulty for all groups" - this is the best estimate when the data fit the model

In Table 30.4 - the hypothesis is "this item has no overall DIF across all groups"

 

Example output:

You want to examine item bias (DIF) between Females and Males in Exam1.txt. You need a column in your Winsteps person label that has two (or more) demographic codes, say "F" for female and "M" for male (or "0" and "1" if you like dummy variables) in column 9.

 

Table 30.1 is best for pairwise comparisons, e.g., Females vs. Males. Use Table 30.1 if you have two classes.

 

Table 30.2 or Table 30.3 are best for multiple comparisons, e.g., regions against the national average. Table 30.2 sorts by item then person class then item. Table 30.3 sorts by person class then item.

 

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

| KID         OBSERVATIONS        BASELINE       DIF     DIF     DIF   DIF   DIF              TAP          |

| CLASS    COUNT  SCORE AVERAGE EXPECT MEASURE   SCORE MEASURE   SIZE  S.E.   t    d.f. Prob. Number  Name |

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

| F           18      18   1.00   1.00   -6.59     .00   -6.59    .00   .00   .00     1 1.000      1 1-4   |

| M           17      17   1.00   1.00   -6.59     .00   -6.59    .00   .00   .00     1 1.000      1 1-4   |

| F           18      16    .89    .92   -4.40    -.03   -3.93    .48   .89   .54    16 .5998      4 1-3-4 |

| M           17      16    .94    .91   -4.40     .03   -5.15>  -.75  1.91  -.39    14 .7020      4 1-3-4 |

| F           18      15    .83    .88   -3.83    -.05   -3.22    .61   .79   .77    16 .4504      5 2-1-4 |

| M           17      16    .94    .89   -3.83     .05   -5.14> -1.30  1.90  -.69    14 .5036      5 2-1-4 |

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

 

This displays a list of the local difficulty/ability estimates underlying the paired DIF analysis. These can be plotted directly from the Plots menu.

 

DIF class specification identifies the columns containing DIF classifications, with DIF= set to @GENDER using the selection rules.

 

The DIF effects are shown ordered by CLASS within item (column of the data matrix).

 

KID CLASS identifies the CLASS of persons. KID is specified with PERSON=, e.g., the first CLASS is "F"

OBSERVATIONS are what are seen in the data

COUNT is the number of observations of the classification used for DIF estimation, e.g., 18 non-extreme F persons responded to TAP item 1.

AVERAGE is the average observation on the classification, e.g., 0.89 is the p-value, proportion-correct-value, of item 4 for F persons.
COUNT * AVERAGE = total score of person class on the item

BASELINE is the prediction without DIF

EXPECT is the expected value of the average observation when there is no DIF, e.g., 0.92 is the expected proportion-correct-value for F without DIF.

MEASURE is the what the overall measure would be without DIF, e.g., -4.40 is the overall item difficulty of item 4 as reported in Table 14.

DIF: Differential Item Functioning

DIF SCORE is the difference between the observed and the expected average observations, e.g., 0.92 - 0.89= -0.03

DIF MEASURE is the item difficulty for this class, e.g., item 4 has a local difficulty of -3.93 for CLASS F.

The average of DIF measures across CLASS for an item is not the BASELINE MEASURE because score-to-measure conversion is non-linear. ">" (maximum score), "<" (minimum score) indicate measures corresponding to extreme scores for this CLASS. "E" indicates an extreme score on all items.

DIF SIZE is the difference between the DIF MEASURE for this class and the BASELINE DIFFICULTY, i.e., -3.93 - -4.40 = .48. Item 4 is .48 logits more difficult for class F than expected.

DIF S.E. is the approximate standard error of the difference, e.g., 0.89 logits

DIF t is an approximate Student's t-statistic test, estimated as DIF SIZE divided by the DIF S.E.

d.f. t has approximately (COUNT-2) degrees of freedom excluding observations of extreme persons.

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

 

These numbers are plotted in the DIF plot. Here item 4 is shown. The y-axis is the "DIF Measure".

 

Table 31.1 Differential person functioning DPF pairwise

 

Example 1: Where do I extract appropriate difficulties for my classes for both items that exhibit DIF and those that don't?

 

The DIF-sensitive difficulties are shown as "DIF Measure" in Table 30.1. They are more conveniently listed in Table 30.2. The "DIF Size" in Table 30.2 or Table 30.3 shows the size of the DIF relative to the overall measure in the IFILE=.

 

To apply the DIF measures as item difficulties, you would need to produce a list of item difficulties for each group, then analyze that group (e.g., with PSELECT=) using the specified list of item difficulties as an anchor file (IAFILE=).

 

My approach would be to copy Table 30.3 into an Excel spreadsheet, then use "Data", "Text to Columns" to put each Table column into a separate Excel column. The anchor file would have the item number in the first column, and either the overall "baseline measure" or the group "DIF measure" in the second column. Then copy and paste these two columns into a .txt anchor file.

 

Example 2: What is the impact of DIF on person measures?

Please look at Winsteps Table 30.2 or 30.3. The average effect of the DIF on person measures of the person group is (DIF Size for the item for the person group)/(total number of items). DIF has raised the person measures if the observed average of the scored responses is greater than expected, and vice-versa.


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