Table 30.4 Differential item functioning DIF fit summary

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"

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

 

Table 30.4 summarizes the CLASS Differential Item Functioning statistics for each item shown in Table 30.2. Table 30.2 shows a t-statistic for each CLASS. These are summarized as chi-square statistics for each item, indicating whether the observed DIF within each item is due to chance alone. The null hypothesis is that the DIF is statistically zero across the classes. Please do not use anchoring  (IAFILE=, PAFILE=, SAFILE=) when investigating this Table.

 

DIF class specification is: DIF=@GENDER

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

| KID        SUMMARY DIF               BETWEEN-CLASS       TAP                   |

| CLASSES    CHI-SQUARE   D.F.  PROB.  UNWTD MNSQ  t=ZSTD  Number Name           |

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

|       2         .8772      1  .3490       .9200   .4132       6 3-4-1          |

|       2        2.2427      1  .1342      2.5792  1.2592       7 1-4-3-2        |

|       2        1.9207      1  .1658      1.9963  1.0211      13 1-4-3-2-4      |

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

 

KID CLASSES is the count of person CLASSES for the item: 2 = Male and Female.

 

SUMMARY DIF CHI-SQUARE is the sum of the t-statistic values from Table 30.2, squared and normalized. See Example 1 below.

D.F. is the degrees of freedom, the count of CLASSES contributing to the chi-square less 1.

PROB. is the probability of the chi-square. Values less than .05 indicate statistical significance.

 

BETWEEN-CLASS are Between-Group Fit Statistics, testing the hypothesis: "The dispersion of the group measures accords with Rasch model expectations."

UNWTD MNSQ is the unweighted mean-square (chi-square divided by its degrees of freedom). It is the size of the misfit (expectation = 1.0, overfit <1.0, underfit >1.0).

t=ZSTD is the significance of the MEAN-SQUARE standardized as a unit-normal deviate (t-statistic with infinite degrees of freedom).

 

TAP is the item. Number is the item entry number. Name is the item label.

 

Item-Trait Chi-Square

The SUMMARY DIF CHI-SQUARE values in Table 30.4 are equivalent to the item-trait chi-square statistics reported by RUMM with DIF=MA3 (or however many strata are chosen in RUMM). In RUMM, the trait CLASSes are obtained by ordering the persons by measure, omitting extreme scores, and dividing the ordered list as equally as possible into equal size classes, including all persons with the same measure in the same class.

 

The Root Mean Square Error of Approximation (RMSEA)

RMSEA is a transformation of the Item-Trait Chi-Square and so of the SUMMARY DIF CHI-SQUARE with DIF=MA3. Its computation is

RMSEA = √ max( [((χ²/df) - 1)/(N - 1)] , 0)

where

χ² = SUMMARY DIF CHI-SQUARE

d.f. = D.F.

N is the person sample size

 

Example 1. SUMMARY DIF CHI-SQUARE.

Let's do an analysis of Exam1.txt with Udecimals=4. Output Table 30 with DIF=@Gender

In Table 30.2, the values for item 13:

CLASS F: COUNT= 18, DIF SIZE= -.7268, DIF S.E.= .6764

CLASS M: COUNT= 16, DIF SIZE= -.7075, DIF S.E.= .7497

So the t-statistics are approximately:

CLASS F: t = -.7268/.6764 with (18-1) d.f. -> -1.0745 with 17 d.f.

CLASS M: t = -.7075/.7497 with (16-1) d.f. -> .9437 with 15 d.f.

Squared  and normalized. The normalizing transformation is Peizer and Pratt (1968) in the Handbook of the Normal Distribution, Second Edition (Patel and Read), p. 214.

CLASS F: t**2 = 1.1546 -> normalized(t**2) = 1.0843

CLASS M: t**2 = .8905 -> normalized(t**2) = .8363

Chi-squared = 1.0843 + .8363 = 1.9207 with (2-1) = 1 d.f.

Coming Rasch-related Events
July 31 - Aug. 3, 2017, Mon.-Thurs.	Joint IMEKO TC1-TC7-TC13 Symposium 2017: Measurement Science challenges in Natural and Social Sciences, Rio de Janeiro, Brazil, imeko-tc7-rio.org.br
Aug. 7-9, 2017, Mon-Wed.	In-person workshop and research coloquium: Effect size of family and school indexes in writing competence using TERCE data (C. Pardo, A. Atorressi, Winsteps), Bariloche Argentina. Carlos Pardo, Universidad Catòlica de Colombia
Aug. 7-9, 2017, Mon-Wed.	PROMS 2017: Pacific Rim Objective Measurement Symposium, Sabah, Borneo, Malaysia, proms.promsociety.org/2017/
Aug. 10, 2017, Thurs.	In-person Winsteps Training Workshop (M. Linacre, Winsteps), Sydney, Australia. www.winsteps.com/sydneyws.htm
Aug. 11 - Sept. 8, 2017, Fri.-Fri.	On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Aug. 18-21, 2017, Fri.-Mon.	IACAT 2017: International Association for Computerized Adaptive Testing, Niigata, Japan, iacat.org
Sept. 15-16, 2017, Fri.-Sat.	IOMC 2017: International Outcome Measurement Conference, Chicago, jampress.org/iomc2017.htm
Oct. 13 - Nov. 10, 2017, Fri.-Fri.	On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
Jan. 5 - Feb. 2, 2018, Fri.-Fri.	On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
Jan. 10-16, 2018, Wed.-Tues.	In-person workshop: Advanced Course in Rasch Measurement Theory and the application of RUMM2030, Perth, Australia (D. Andrich), Announcement
Jan. 17-19, 2018, Wed.-Fri.	Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website
April 13-17, 2018, Fri.-Tues.	AERA, New York, NY, www.aera.net
May 25 - June 22, 2018, Fri.-Fri.	On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
June 29 - July 27, 2018, Fri.-Fri.	On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com
Aug. 10 - Sept. 7, 2018, Fri.-Fri.	On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Oct. 12 - Nov. 9, 2018, Fri.-Fri.	On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
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