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Pandey, Tej N.; Shoemaker, David M. – Educational and Psychological Measurement, 1975
Described herein are formulas and computational procedures for estimating the mean and second through fourth central moments of universe scores through multiple matrix sampling. Additionally, procedures are given for approximating the standard error associated with each estimate. All procedures are applicable when items are scored either…
Descriptors: Error of Measurement, Item Sampling, Matrices, Scoring Formulas
Pandey, Tej N. – 1975
Standard errors of pooled mean estimate in multiple matrix sampling were compared for two procedures. The data were from tests involving items with and without replacement. The two procedures involve the formulations of Madow and Lord, and Novick; the former permits sampling of item, with or without replacement, whereas the latter is to be used…
Descriptors: Comparative Analysis, Error of Measurement, Item Sampling, Matrices
Shoemaker, David M. – 1972
Investigated empirically through post mortem item-examinee sampling was the feasibility of the jackknife as a procedure for approximating standard errors of estimate in multiple matrix sampling. The parameters estimated were the mean test score, second through fourth central moments of the test score distribution, and the variance of the item…
Descriptors: Error of Measurement, Error Patterns, Item Sampling, Matrices
Shoemaker, David M. – 1972
Investigated empirically through post mortem item-examinee sampling were the relative merits of two alternative procedures for allocating items to subtests in multiple matrix sampling and the feasibility of using the jackknife in approximating standard errors of estimate. The results indicate clearly that a partially balanced incomplete block…
Descriptors: Error of Measurement, Item Sampling, Matrices, Sampling
Peer reviewed Peer reviewed
Shoemaker, David M. – Journal of Educational Measurement, 1973
Investigated empirically through post mortem item-examinee samplings were the relative merits of two alternative procedures for allocating items to subtests in multiple matrix sampling and the feasibility of using the jackknife in approximating standard errors of estimate. (Editor)
Descriptors: Databases, Error of Measurement, Item Sampling, Research Design
Peer reviewed Peer reviewed
Shoemaker, David M. – Educational and Psychological Measurement, 1972
Descriptors: Difficulty Level, Error of Measurement, Item Sampling, Simulation
Shoemaker, David M. – 1972
Described and listed herein with concomitant sample input and output is the Fortran IV program which estimates parameters and standard errors of estimate per parameters for parameters estimated through multiple matrix sampling. The specific program is an improved and expanded version of an earlier version. (Author/BJG)
Descriptors: Computer Oriented Programs, Computer Programs, Error of Measurement, Error Patterns
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Misanchuk, Earl R. – 1978
Multiple matrix sampling of three subscales of the California Psychological Inventory was used to investigate the effects of four variables on error estimates of the mean (EEM) and variance (EEV). The four variables were examinee population size (600, 450, 300, 150, 100, and 75); number of subtests, (2, 3, 4, 5, 6, and 7), hence the number of…
Descriptors: Adults, Analysis of Variance, Error of Measurement, Item Sampling
Harris, Chester W.; And Others – 1977
The implications of a mathematical model of test scores are explored where the data are limited to a random sample of items without replacement from an indefinitely large population or item domain in which items are scored either zero or one. The purpose is to obtain an unbiased estimate of a student's proportion of items correct in the item…
Descriptors: Academic Achievement, Achievement Tests, Annotated Bibliographies, Bibliographies
Penfield, Douglas A. – 1972
Thirty-four papers on educational statistics which were presented at the 1971 AERA Conference are summarized. Six major interest areas are covered: (a) general information; (b) non-parametric methods; (c) errors of measurement and correlation techniques; (d) regression theory; (e) univariate and multivariate analysis; (f) factor analysis. (MS)
Descriptors: Analysis of Variance, Bayesian Statistics, Behavioral Science Research, Computers