By Wim J. van der Linden
This booklet starts off with a mirrored image at the background of try design--the center task of all academic and mental trying out. It then offers a customary language for modeling attempt layout difficulties as cases of multi-objective restricted optimization. the most part of the ebook discusses try out layout types for a wide number of difficulties from the day-by-day perform of checking out, and illustrates their use with the aid of various empirical examples. The presentation comprises types for the meeting of checks to an absolute or relative aim for his or her details capabilities, classical try meeting, attempt equating difficulties, merchandise matching, attempt splitting, simultaneous meeting of a number of assessments, checks with merchandise units, multidimensional checks, and adaptive try meeting. separate chapters are dedicated to the questions of the way to layout merchandise banks for optimum help of courses with mounted and adaptive checks. Linear versions for optimum try layout, which doesn't require any particular mathematical history, has been written to be a worthy source at the table of any try specialist.
Wim J. van der Linden is Professor of dimension and information research, collage of Twente, The Netherlands. His specialization is psychometric conception and techniques, and he has been an energetic researcher of merchandise reaction conception all through his profession. His present learn is on attempt layout, adaptive checking out, attempt equating, and response-time modeling. Professor van der Linden is a previous president of the Psychometric Society and a recipient of the NCME lifetime success award for his paintings on academic measurement.
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Additional info for Linear Models for Optimal Test Design
Optimal blueprints can be calculated using the same techniques of integer programming as for optimal test assembly. 9. Besides optimal test assembly, the following three modes of assembling tests from an item pool have been used: (i) random sampling of tests, (ii) sequential sampling of tests, and (iii) adaptive test assembly. The ﬁrst two modes were popular when not enough computational power was available to implement an optimal test-assembly approach. Adaptive test assembly is treated as a special case of optimal test assembly in this book.
Except for an obvious attribute such as the number of items in the set, attributes at this level are mostly aggregates of item attributes, such as the average p-value in the set, the number of items with a certain response format, and the distribution of item content. 4. Test level. Examples of test attributes are test length, TIF, classical reliability coeﬃcient, a distribution of item content, number of item sets, and total word count. Attributes at this level are invariably quantitative and, like item-set attributes, often aggregate toward lower-level attributes.
This theory has been developed to explain the response probabilities of single persons on single test items. It supports testing from calibrated item pools, optimal test assembly, and scoring of persons on a scale deﬁned independently of a single test. 4. Item and test information functions are local measures of the accuracy of an ability estimate. These functions are additive. This feature is used in the Birnbaum approach to test design, which consists of the following steps: (i) analysis of the goal of the test, (ii) translation of this goal into a target for the information function of the test, and (iii) assembly of a test from the pool with an information function that best approximates this target.
Linear Models for Optimal Test Design by Wim J. van der Linden