[Rasch] DIF and multiple comparisons - BH method

Svend Kreiner S.Kreiner at biostat.ku.dk
Wed Feb 8 01:23:09 EST 2006


One additional advantage of B-H methods compared to Bonferroni is that the 
B-H also works when test statistics are correlated. This is very important 
since tests for DIF are correlated.

No DIF requires that items and other person covariates are conditionally 
independent given the latent trait variable. The only model where items and 
covariates  also are conditionally independent given the manifest person 
score is the Rasch model where the score is a sufficient statistic.Tests of 
conditional independence of items and covariates given the total score 
using either Partial gamma coefficients or Mantel-Haenszel methods are 
therefore Rasch-based methods.

Remember also, that DIF is uniform if and only if the item x covariate x 
score table fits a two-factor loglinear model. The Rasch model applies in 
the different  subpopulations defined by the values of the covariate, but 
item parameters of the biased items are different. Comparisons within 
subpopulations are objective. Comparisons across subpopulations are not.

Svend



At 06:26 07-02-2006 -0600, Fred Wolfe wrote:
>Hi Mike,
>
>I am not expert on the Benjamina-Hochberg method. I understand that there 
>are modifications of BH, but it seems that the original BH is still the 
>predominant method now (from what I could see looking at the literature).
>
>BH would seem to have important advantages over Bonferroni.
>
>This issue came up when a colleague was examining DIF using non-Rasch 
>methods. Using the model form: Item score = Exogenous variable + 
>Questionnaire Total, this was examined by 3 methods (with different 
>results) that had been used in the literature:
>
>Partial gamma coefficient (Stata)
>Kendal's tau b coefficient (SAS)
>Variance estimation using bootstrap methods
>
>Perhaps someone might have comments as to the usefulness/validity of Rasch 
>vs. non-Rasch methods for detecting DIF.
>
>Fred
>
>
>At 04:45 PM 2/6/2006, Mike Linacre (RMT) wrote:
>>Thank you, Randy and Fred,
>>
>>The Benjamini, Y. & Hochberg, Y. (1995) method for multiple comparisons 
>>appears to be:
>>(1) Compute individual p-values for each of the N hypothesis tests as 
>>though each was the only one.
>>(2) Sort the N p-values by size ascending, n=1,N, so that p(1) is the 
>>smallest, and p(N) is the largest
>>(3) Starting from p(N) downwards, look down for the first p(n) which is 
>>less than or equal to (n/N) * 0.05 (or your chosen significance level)
>>(4) Tests 1 to n are classified as significant.
>>
>>Is this correct? Have any useful modifications been proposed? This seems 
>>easy to implement in software, which I may well do.
>>
>>Thanks again,
>>
>>Mike L.
>>
>>Mike Linacre
>>Editor, Rasch Measurement Transactions
>>rmt at rasch.org www.rasch.org/rmt/ Latest RMT: 19:3 Winter
>
>
>Fred Wolfe
>National Data Bank for Rheumatic Diseases
>Wichita, Kansas
>Tel (316) 263-2125     Fax (316) 263-0761
>fwolfe at arthritis-research.org
>
>
>_______________________________________________
>Rasch mailing list
>Rasch at acer.edu.au
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Svend Kreiner
Associate professor
Department of Biostatistics
University of Copenhagen

Blegdamsvej 3, DK-2200 Copenhagen N, DENMARK

 From June 9, 2005:

Øster farimagsgade 5, entr. B
P.O. Box 2099
DK-1014 Copenhagen K, Denmark

Email: S.Kreiner at biostat.ku.dk
Phone: (+45) 35 32 75 97

Fax: (+45) 35 32 79 07
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