075 or 075 Be careful As it stands now the program doesnt do everything exactly

075 or 075 be careful as it stands now the program

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UDIF value exceeding .075 or -.075. Be careful : As it stands now, the program doesn’t do everything exactly as we want. As you look at the UDIF indices of the sample items, you notice that the UDIF indices for the polytomous items are not on a 0-1 metric as those values for the dichotomous items are. To do the analyses correctly (with regard to the polytomous items), we need to rescale those UDIF values for the polytomous items. You should divide the UDIF statistic by the maximum number of score points for the item to obtain an indication of the DIF on a “per point basis.” So, for example, if the maximum number of score points is 4 and UDIF=.20, On a per point basis, the amount of DIF is about .05 and this is not large enough to worry about. Even though a UDIF value of .20 seems high, that difference is on a four point item, and so the level is actually 6
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relatively small. When the DIF is viewed like the binary scored items on the 0-1 scale, the DIF is actually quite small (only a difference of .05 for each scoring point). Now if on the same 4 point item and if UDIF=.50, then on a per point basis, the difference is .125 and this difference is substantial and should be very much a concern. Example : At first glance, an item with a UDIF value of 0.16 should be flagged. However, if the item is polytomous, (as is item 39 below in the example), divide that UDIF value by 4 (its IMXS value) to get a revised UDIF value of .04. Thus this item WOULD NOT be flagged. Here’s an example of the UDIF values. imxs Item SDIF UDIF 1 1 0.03 0.03 1 2 0.02 0.02 1 3 0.07 0.07 1 4 -0.02 -0.03 1 5 0.00 0.02 1 6 -0.01 -0.03 1 7 -0.03 -0.03 1 8 -0.01 -0.02 1 9 0.01 0.02 1 10 0.03 0.03 1 11 0.01 0.02 1 12 -0.02 -0.03 1 13 0.04 0.04 1 14 -0.05 -0.05 1 15 0.02 0.02 1 16 0.07 0.07 1 17 -0.03 -0.03 1 18 0.00 0.01 1 19 -0.02 -0.03 1 20 0.04 0.04 1 21 -0.01 -0.02 1 22 -0.01 -0.02 1 23 0.03 0.03 1 24 0.04 0.04 1 25 -0.03 -0.04 1 26 -0.03 -0.04 1 27 -0.03 -0.03 1 28 -0.02 -0.03 1 29 0.01 0.02 1 30 -0.04 -0.04 1 31 0.07 0.08 *FLAG 1 32 -0.05 -0.05 1 33 -0.02 -0.03 7
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1 34 0.01 0.02 4 35 -0.02 -0.07 4 36 -0.09 -0.09 4 37 0.22 0.22 4 38 0.00 0.03 4 39 -0.16 -0.16 In this example, for the item flagged the UDIF value exceeds +/- .075. That means item 31 seems to be showing DIF. That’s the first stage. Go back into the data file, and modify that sequence of “switches” you placed on the second line of the data file. For each item with a UDIF value greater than .075, a ‘0’ should correspond to that column. Think of it in this way: by putting a ‘0’ in a given column, you are effectively “switching off” that item from being included in the computation of the criterion score, thereby making the total test score a better matching criterion. From stage 1 we saw that item 31 seems to be DIF. Here’s what the revised first few lines of the data files will look like (pay particular attention to the second line): 111111111111111111111111111111111144444 111111111111111111111111111111011111111 000000000000000000000000000000000000000 5050000474 110101101110111001010000101100110111313 5050000502 001000010001100001010110101010010000000 5050000577WM010110010010010000000000000000000012000 9002038068WM111111010111111111001011111001110033243 9002038228WF010000010010010000011000000000000011010 9002038710HF000011111100000000000100000000100112013 9002038827WF100010111100111011100011111110111122213 9002038836WM111111000100100011100010100110111031033 9002039000WF111111101101110111001110111110110142022 Note #1 : Always make certain to align your datafile with the name of the datafile in your command file. If you change a datafile name in modifying the “switch” line or anything, be certain to change the name of the data file in the command file as well, or STDIF might read in a data set different than the one that you want.
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  • Spring '16
  • STDIF

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