By Alina A. von Davier
This publication is aimed toward (a) practitioners who have to equate tests-including people with those tasks in trying out businesses, kingdom checking out corporations and faculty districts; (b) statisticians and different examine staff drawn to the speculation in the back of such paintings and using version dependent statistical tools of knowledge smoothing in utilized paintings; (c) complex graduate scholars in psychometric and dimension courses. whereas there are different books on try equating, and books of using kernel smoothing, not anyone has released any paintings at the kernel approach to try out equating. it really is whatever of a unifying notion in equating and brings jointly numerous tools into an geared up complete instead of treating them as a bunch of disparate tools.
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Additional resources for The Kernel Method of Test Equating (Statistics for Social Science and Behavorial Sciences)
In Chapter 9 we illustrate a tool, a type of SEED, that measures the standard error of the diﬀerence between the equating function obtained for (wX , wY ) = (1, 1) and any other choice of (wX , wY ). This version of the SEED can be used to give a basis for choosing wX and wY . 2, deﬁne the vectorized P(12) by p(12)1 .. 24) , . 3 The Counterbalanced Design (CB) 31 where p(12)k is the kth column of P(12) . Deﬁne v(P(21) ) in a similar manner. 10), respectively. , r 2 = M v(P(21) ) and s1 = N v(P(21) ).
4 Non-Equivalent groups with Anchor Test Design (NEAT) As mentioned at the beginning of this chapter, in order to control for differences in examinee ability, we need either equivalent groups of examinees, who take both tests, or we need data on common items that are given along with the two tests. ” In the Non-Equivalent groups with Anchor Test (NEAT) Design there are two populations, P and Q, of test-takers and a sample of examinees from each. The sample from P takes test X, the sample from Q takes test Y, and both samples take a set of common items, the anchor test, A.
2, we denote the possible raw-score values of X and Y , by x1 , . . , xJ and y1 , . . 2) where j = 1, . . , J and k = 1, . . , K. Finally, denote by r the (column) vector given by (r1 , . . , rJ )t and by s the (column) vector given by (s1 , . . , sK )t . , they are estimated directly. 3) where IJ is a J × J identity matrix. , ΩJ is the collection of all J-vectors with positive coordinates that sum to 1. ΩK is deﬁned analogously for K-vectors. The Design Function is more complicated in the other equating designs.
The Kernel Method of Test Equating (Statistics for Social Science and Behavorial Sciences) by Alina A. von Davier