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Educational and Psychological… | 6 |
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Brandenburg, Dale C.; Forsyth, Robert A. – Educational and Psychological Measurement, 1974
Descriptors: Comparative Analysis, Goodness of Fit, Item Sampling, Models

Raju, Nambury S. – Educational and Psychological Measurement, 1977
A rederivation of Lord's formula for estimating variance in multiple matrix sampling is presented as well as the ways Cronbach's coefficient alpha and the Spearman-Brown prophecy formula are related in this context. (Author/JKS)
Descriptors: Analysis of Variance, Comparative Analysis, Item Sampling, Mathematical Models

Bryant, N. Dale; Gokhale, Sunanda – Educational and Psychological Measurement, 1972
Authors present a formula for use when restrictions result from complex or unmeasured variables. (Authors/MB)
Descriptors: Comparative Analysis, Correlation, Item Analysis, Item Sampling

Macready, George B. – Educational and Psychological Measurement, 1975
Assesses conditional states of item mastery found among items from different item domains and the effectiveness of various procedures for identifying such conditional relations. Item domains considered were from the curriculum area of multiplication of whole numbers, and were defined by a domain referenced testing system. (Author/RC)
Descriptors: Achievement Tests, Comparative Analysis, Goodness of Fit, Grade 5

Hsu, Louis M. – Educational and Psychological Measurement, 1980
A method based on the Poisson approximation to the binomial distribution and on the relation between the Chi-Squared distribution and the Poisson distribution is suggested for selected use in determining the number of items and passing scores in mastery Lests. (Author/RL)
Descriptors: Comparative Analysis, Cutting Scores, Item Sampling, Mastery Tests

Claudy, John G. – Educational and Psychological Measurement, 1972
First purpose of this study was to investigate empirically the accuracy of prediction using regression weights selected through the use of variance-reduction procedures. (Author)
Descriptors: Comparative Analysis, Error Patterns, Factor Structure, Item Sampling