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Haberman, Shelby J. – ETS Research Report Series, 2020
Best linear prediction (BLP) and penalized best linear prediction (PBLP) are techniques for combining sources of information to produce task scores, section scores, and composite test scores. The report examines issues to consider in operational implementation of BLP and PBLP in testing programs administered by ETS [Educational Testing Service].
Descriptors: Prediction, Scores, Tests, Testing Programs
Drasgow, Fritz; Levine, Michael V. – 1985
Optimal appropriateness indices, recently introduced by Levine and Drasgow (1984), provide the highest rates of detection of aberrant response patterns that can be obtained from item responses. These optimal appropriateness indices are used to study three important problems in appropriateness measurement. First, the maximum detection rates of two…
Descriptors: Error of Measurement, Latent Trait Theory, Mathematical Models, Maximum Likelihood Statistics
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Yen, Wendy M. – Journal of Educational Measurement, 1984
A procedure for obtaining maximum likelihood trait estimates from number-correct (NC) scores for the three-parameter logistic model is presented. It produces an NC score to trait estimate conversion table. Analyses in the estimated true score metric confirm the conclusions made in the trait metric. (Author/DWH)
Descriptors: Achievement Tests, Error of Measurement, Estimation (Mathematics), Latent Trait Theory
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Tatsuoka, Kikumi K. – Journal of Educational Measurement, 1983
A newly introduced approach, rule space, can represent large numbers of erroneous rules of arithmetic operations quantitatively and can predict the likelihood of each erroneous rule. The new model challenges the credibility of the traditional right-or-wrong scoring procedure. (Author/PN)
Descriptors: Addition, Algorithms, Arithmetic, Diagnostic Tests
Linacre, John M. – 1990
Rank ordering examinees is an easier task for judges than is awarding numerical ratings. A measurement model for rankings based on Rasch's objectivity axioms provides linear, sample-independent and judge-independent measures. Estimates of examinee measures are obtained from the data set of rankings, along with standard errors and fit statistics.…
Descriptors: Comparative Analysis, Error of Measurement, Essay Tests, Evaluators
Haertel, Edward H. – 1992
Classical test theory, item response theory, and generalizability theory all treat the abilities to be measured as continuous variables, and the items of a test as independent probes of underlying continua. These models are well-suited to measuring the broad, diffuse traits of traditional differential psychology, but not for measuring the outcomes…
Descriptors: Ability, Data Analysis, Error of Measurement, Generalizability Theory
Wise, Lauress L. – 1986
A primary goal of this study was to determine the extent to which item difficulty was related to item position and, if a significant relationship was found, to suggest adjustments to predicted item difficulty that reflect differences in item position. Item response data from the Medical College Admission Test (MCAT) were analyzed. A data set was…
Descriptors: College Entrance Examinations, Difficulty Level, Educational Research, Error of Measurement
Kingston, Neal M.; Holland, Paul W. – 1986
This study addresses the test-disclosure-related need for more Graduate Record Examinations (GRE) General Test editions in a situation where the number of examinees is stable or declining. Equating is used to guarantee that examinees of different test editions are treated equitably. The data collection designs used in this study were: (1)…
Descriptors: College Entrance Examinations, Data Collection, Equated Scores, Error of Measurement
Carlson, James E.; Spray, Judith A. – 1986
This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…
Descriptors: Cutting Scores, Data Analysis, Difficulty Level, Error of Measurement