﻿ Table 33.3, 33.4 Differential group functioning DGF list

# Table 33.3, 33.4 Differential group functioning DGF list

Table 33 supports the investigation of item bias, Differential Group Functioning (DGF), i.e., interactions between classes of items and types of persons. Specify DIF= for person classifying indicators in person labels, and DPF= for item classifying indicators in the item labels.

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 33.1 is best for pairwise comparisons, e.g., Females vs. Males. Use Table 33.1 if you have two classes of persons, and Table 33.2 if you have two classes of items.

Table 33.3 or Table 33.4 are best for multiple comparisons, e.g., regions against the national average. Table 33.3 sorts by item class then person class. Table 33.4 sorts by person class then item class.

Table 33.3

DGF CLASS-LEVEL BIAS/INTERACTIONS FOR DIF=@GENDER AND DPF=\$S1W1

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

| PERSON     OBSERVATIONS    BASELINE       DGF     DGF   DGF   DGF         ITEM  |

| CLASS     COUNT AVERAGE EXPECT           SCORE    SIZE  S.E.   t   Prob.  CLASS |

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

| F           234     .46    .46             .00     .06   .28  -.22 .8294  1     |

| F            54     .85    .84             .01    -.18   .50   .36 .7224  2     |

| F            18     .89    .85             .04    -.50   .83   .60 .5541  3     |

| F            18     .00    .01            -.01     .00< 3.00   .00 1.000  4     |

| M           221     .51    .50             .01    -.16   .27   .59 .5552  1     |

| M            51     .86    .86             .00     .00   .61   .00 1.000  2     |

| M            17     .82    .87            -.04     .76  1.04  -.74 .4734  3     |

| M            17     .00    .01            -.01     .00< 2.40   .00 1.000  4     |

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

Table 33.4

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

| ITEM      OBSERVATIONS    BASELINE       DGF     DGF   DGF   DGF         PERSON |

| CLASS    COUNT AVERAGE EXPECT           SCORE    SIZE  S.E.   t   Prob.  CLASS  |

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

| 1          234     .46    .46             .00     .06   .28  -.22 .8294  F      |

| 1          221     .51    .50             .01    -.16   .27   .59 .5552  M      |

| 2           54     .85    .84             .01    -.18   .50   .36 .7224  F      |

| 2           51     .86    .86             .00     .00   .61   .00 1.000  M      |

| 3           18     .89    .85             .04    -.50   .83   .60 .5541  F      |

| 3           17     .82    .87            -.04     .76  1.04  -.74 .4734  M      |

| 4           18     .00    .01            -.01     .00< 3.00   .00 1.000  F      |

| 4           17     .00    .01            -.01     .00< 2.40   .00 1.000  M      |

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

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

DGF class specification identifies the person-label columns containing DIF classifications, with DIF= set to @GENDER using the selection rules. The item-label columns for item classes are specified by DPF=.

Table 33.3. The DGF effects are shown ordered by Person CLASS within item class.

Table 33.4. The DGF effects are shown ordered by Person CLASS within Item CLASS.

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 F persons responded to TAP item 1.

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

BASELINE is the prediction without DGF

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 DGF.

DGF: Differential Group Functioning

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

DGF SIZE is the relative difficulty for this class, e.g., person CLASS F has a relative difficulty of .07 for item CLASS 1-. ">" (maximum score), "<" (minimum score) indicate measures corresponding to extreme scores.

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

DGF t is an approximate Student's t-statistic test, estimated as DGF SIZE divided by the DGF S.E. with a little less than (COUNT-2) degrees of freedom.

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

ITEM CLASS identifies the CLASS of items.

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

For more information, contact info@winsteps.com or use the Contact Form

 Forum Rasch Measurement Forum to discuss any Rasch-related topic

Click here to ask a question or make a suggestion about Winsteps and Facets software.

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 with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments, George Engelhard, Jr. & Stefanie Wind 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
April 10-12, 2018, Tues.-Thurs. Rasch Conference: IOMW, New York, NY, www.iomw.org
April 13-17, 2018, Fri.-Tues. AERA, New York, NY, www.aera.net
May 22 - 24, 2018, Tues.-Thur. EALTA 2018 pre-conference workshop (Introduction to Rasch measurement using WINSTEPS and FACETS, Thomas Eckes & Frank Weiss-Motz), https://ealta2018.testdaf.de
May 25 - June 22, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
June 27 - 29, 2018, Wed.-Fri. Measurement at the Crossroads: History, philosophy and sociology of measurement, Paris, France., https://measurement2018.sciencesconf.org
June 29 - July 27, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com
July 25 - July 27, 2018, Wed.-Fri. Pacific-Rim Objective Measurement Symposium (PROMS), (Preconference workshops July 23-24, 2018) Fudan University, Shanghai, China "Applying Rasch Measurement in Language Assessment and across the Human Sciences" www.promsociety.org
Aug. 10 - Sept. 7, 2018, Fri.-Fri. On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Sept. 3 - 6, 2018, Mon.-Thurs. IMEKO World Congress, Belfast, Northern Ireland www.imeko2018.org
Oct. 12 - Nov. 9, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com

Our current URL is www.winsteps.com

Winsteps® is a registered trademark

 John "Mike" Linacre, author of Winsteps, and Jenny use and recommend eco-friendly, safe and effective wellness and beauty products such as skincare