Achievement Testing: Recent Advances (Quantitative by Isaac I. Bejar

By Isaac I. Bejar

A publication which summarizes a few of the contemporary advances within the thought and perform of feat checking out, within the mild of technological advancements, and advancements in psychometric and mental concept. It offers an creation to the 2 significant psychometric versions, merchandise reaction concept and generalizability concept, and assesses their strengths for various purposes. The ebook closes with a few speculations in regards to the way forward for success exams for the overview of people, in addition to tracking of academic growth. `...the publication comprises useful info for either novices and for complicated employees who wish an summary of modern paintings in fulfillment testing.' -- The magazine of the yankee Statistical A

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In many applications, however, there is no ''universe of items" but, rather, a long test (or pool) of items. The procedure then calls for dividing the long test into nonoverlapping subtests. , Sirotnick, 1974). Latent Trait Model The specialization of the R-S model into the so-called classical model, as described by Gulliksen (1950), has been the psychometric backbone of achievement testing over the last several decades. During the 1940s a new measurement theory began to form with the work of Ferguson (1942), Lawley (1943), and Tucker (1946); later came Lord (1952), Lazarsfeld (1959), and Rasch (1960).

The structure of the estimate is the same in both cases. Each is a linear combination of observed performance, XpI , and the performance of the group to which the testee belongs. In the crossed case, since everyone responds to the same set of items, the measure of group performance is µI. In the nested case, since everyone takes a different set of items, µ is the measure of group performance. Equations 1 and 2 are identical in form to Kelley's (1947) estimate of true score in the classical model.

The purpose of this analysis is to obtain the expected mean squares associated withand, which are given by where MS(p)=is the mean squares for persons;ni=number of items in the study;Ni=number of items in the universe; Page 25 MS(i:p)=mean squares for interaction of persons and items for the nested case;MS(ip)=mean square for interaction of persons and items for the crossed case. Substituting the appropriate terms in equation 3, we obtain for the crossed case which is equivalent to the usual KR-20 reliability coefficient when items are fixed rather than sampled from a universe.

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