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Psychometrika | 5 |
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Arabie, Phipps | 1 |
Fischer, Gerhard H. | 1 |
Ponocny, Ivo | 1 |
Robinson, D. H. | 1 |
Takane, Yoshio | 1 |
Wackerly, D. D. | 1 |
Williams, Richard H. | 1 |
Zimmerman, Donald W. | 1 |
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Journal Articles | 5 |
Reports - Research | 5 |
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Zimmerman, Donald W.; Williams, Richard H. – Psychometrika, 1982
Formulas for the standard error of measurement of three measures of change (simple differences; residualized difference scores; and a measure introduced by Tucker, Damarin, and Messick) are derived. A practical guide for determining the relative error of the three measures is developed. (Author/JKS)
Descriptors: Achievement Gains, Algorithms, Differences, Error of Measurement

Arabie, Phipps – Psychometrika, 1980
A new computing algorithm, MAPCLUS (Mathematical Programming Clustering), for fitting the Shephard-Arabie ADCLUS (Additive Clustering) model is presented. Details and benefits of the algorithm are discussed. (Author/JKS)
Descriptors: Algorithms, Cluster Analysis, Least Squares Statistics, Measurement Techniques

Wackerly, D. D.; Robinson, D. H. – Psychometrika, 1983
A statistical method for testing the agreement between a judge's assessment of an object or subject and a known standard is developed and shown to be superior to two other methods which appear in the literature. (Author/JKS)
Descriptors: Algorithms, Computer Programs, Judges, Measurement Techniques

And Others; Takane, Yoshio – Psychometrika, 1980
An individual differences additive model is discussed which represents individual differences in additivity by differential weighting or additive factors. A procedure for estimating model parameters for various data measurement characteristics is developed. The method is found to be very useful in describing certain types of developmental change…
Descriptors: Algorithms, Data Analysis, Least Squares Statistics, Mathematical Models

Fischer, Gerhard H.; Ponocny, Ivo – Psychometrika, 1994
An extension to the partial credit model, the linear partial credit model, is considered under the assumption of a certain linear decomposition of the item x category parameters into basic parameters. A conditional maximum likelihood algorithm for estimating basic parameters is presented and illustrated with simulation and an empirical study. (SLD)
Descriptors: Algorithms, Change, Estimation (Mathematics), Item Response Theory