Polynomial Algorithms for Item Matching

Ronald D. Armstrong; Douglas H. Jones

doi:10.1177/014662169201600408

Polynomial Algorithms for Item Matching

Ronald D. Armstrong
, Douglas H. Jones

Rutgers Business School, Management & Information Systems

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

To estimate test reliability and to create parallel tests, test items frequently are matched. Items can be matched by splitting tests into parallel test halves, by creating T splits, or by matching a desired test form. Problems often occur. Algorithms are presented to solve these problems. The algorithms are based on optimization theory in networks (graphs) and have polynomial complexity. Computational results from solving sample problems with several hundred decision variables are reported.

Original language	English (US)
Pages (from-to)	365-371
Number of pages	7
Journal	Applied Psychological Measurement
Volume	16
Issue number	4
DOIs	https://doi.org/10.1177/014662169201600408
State	Published - Dec 1992

All Science Journal Classification (ASJC) codes

Social Sciences (miscellaneous)
Psychology (miscellaneous)

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title = "Polynomial Algorithms for Item Matching",

abstract = "To estimate test reliability and to create parallel tests, test items frequently are matched. Items can be matched by splitting tests into parallel test halves, by creating T splits, or by matching a desired test form. Problems often occur. Algorithms are presented to solve these problems. The algorithms are based on optimization theory in networks (graphs) and have polynomial complexity. Computational results from solving sample problems with several hundred decision variables are reported.",

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AB - To estimate test reliability and to create parallel tests, test items frequently are matched. Items can be matched by splitting tests into parallel test halves, by creating T splits, or by matching a desired test form. Problems often occur. Algorithms are presented to solve these problems. The algorithms are based on optimization theory in networks (graphs) and have polynomial complexity. Computational results from solving sample problems with several hundred decision variables are reported.

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U2 - 10.1177/014662169201600408

DO - 10.1177/014662169201600408

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JO - Applied Psychological Measurement

JF - Applied Psychological Measurement

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Polynomial Algorithms for Item Matching

Abstract

All Science Journal Classification (ASJC) codes

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