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| Test Theory | 7 |
| Test Construction | 4 |
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| Computer Assisted Testing | 2 |
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| Maximum Likelihood Statistics | 2 |
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| Armstrong, Ronald D. | 2 |
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| Roid, Gale | 1 |
| Rubin, Donald B. | 1 |
| Sanders, Piet F. | 1 |
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Peer reviewedMessick, Samuel – Psychometrika, 1981
Bond criticized the base-free measure of change proposed by Tucker, Damarin, and Messick by pointing to an incorrect derivation which is here viewed instead as a correct derivation entailing an inadequately specified assumption. Bond's revision leads to negatively biased estimates, whereas the original approach leads to unbiased estimates.…
Descriptors: Algorithms, Change, Correlation, Mathematical Formulas
Peer reviewedSanders, Piet F.; Verschoor, Alfred J. – Applied Psychological Measurement, 1998
Presents minimization and maximization models for parallel test construction under constraints. The minimization model constructs weakly and strongly parallel tests of minimum length, while the maximization model constructs weakly and strongly parallel tests with maximum test reliability. (Author/SLD)
Descriptors: Algorithms, Models, Reliability, Test Construction
Peer reviewedBentler, P. M.; Tanaka, Jeffrey S. – Psychometrika, 1983
Rubin and Thayer recently presented equations to implement maximum likelihood estimation in factor analysis via the EM algorithm. It is argued here that the advantages of using the EM algorithm remain to be demonstrated. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Maximum Likelihood Statistics, Research Problems
Peer reviewedRubin, Donald B.; Thayer, Dorothy T. – Psychometrika, 1983
The authors respond to a criticism of their earlier article concerning the use of the EM algorithm in maximum likelihood factor analysis. Also included are the comments made by the reviewers of this article. (JKS)
Descriptors: Algorithms, Estimation (Mathematics), Factor Analysis, Maximum Likelihood Statistics
Peer reviewedArmstrong, Ronald D.; Jones, Douglas H. – Applied Psychological Measurement, 1992
Polynomial algorithms are presented that are used to solve selected problems in test theory, and computational results from sample problems with several hundred decision variables are provided that demonstrate the benefits of these algorithms. The algorithms are based on optimization theory in networks (graphs). (SLD)
Descriptors: Algorithms, Decision Making, Equations (Mathematics), Mathematical Models
Peer reviewedArmstrong, Ronald D.; And Others – Journal of Educational Statistics, 1994
A network-flow model is formulated for constructing parallel tests based on classical test theory while using test reliability as the criterion. Practitioners can specify a test-difficulty distribution for values of item difficulties as well as test-composition requirements. An empirical study illustrates the reliability of generated tests. (SLD)
Descriptors: Algorithms, Computer Assisted Testing, Difficulty Level, Item Banks
Peer reviewedRoid, Gale; Haladyna, Tom – Review of Educational Research, 1980
A continuum of item-writing methods is proposed ranging from informal-subjective methods to algorithmic-objective methods. Examples of techniques include objective-based item writing, amplified objectives, item forms, facet design, domain-referenced concept testing, and computerized techniques. (Author/CP)
Descriptors: Achievement Tests, Algorithms, Computer Assisted Testing, Criterion Referenced Tests
