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What Works Clearinghouse Rating
Sachar, Jane; Suppes, Patrick – 1977
It is sometimes desirable to obtain an estimated total-test score for an individual who was administered only a subset of the items in a total test. The present study compared six methods, two of which utilize the content structure of items, to estimate total-test scores using 450 students in grades 3-5 and 60 items of the ll0-item Stanford Mental…
Descriptors: Comparative Analysis, Elementary Education, Item Analysis, Item Banks
Myerberg, N. James – 1975
The effect of stratified sampling of items on the estimation of test score distribution parameters by multiple matrix sampling was studied. Item difficulty and/or interitem correlations were the bases of stratification. Various item iniverses were created by computer simulation and sampled according to several plans. The results indicate that…
Descriptors: Computer Programs, Item Analysis, Item Sampling, Matrices
Peer reviewedPascale, Pietro J. – Journal of Drug Education, 1976
This paper presents a procedure called multiple matrix sampling which simplifies data collection for drug incidence surveys. Advantages for the use of multiple matrix sampling with drug surveys are discussed and a practical example is outlined presenting the guidelines for a sampling plan. (Author)
Descriptors: Data Collection, Drug Abuse, Guidelines, Item Sampling
Woodson, M. I. Charles E.
It has been argued that item variance and test variance are not necessary characteristics for criterion-referenced tests, although they are necessary for norm-referenced tests. This position is in error because it considers sample statistics as the criteria for evaluating items and tests. Within a particular sample, an item or test may have no…
Descriptors: Criterion Referenced Tests, Evaluation Criteria, Item Analysis, Item Sampling
Peer reviewedSirotnik, Kenneth; Wellington, Roger – Journal of Educational Measurement, 1977
A single conceptual and theoretical framework for sampling any configuration of data from one or more population matrices is presented, integrating past designs and discussing implications for more general designs. The theory is based upon a generalization of the generalized symmetric mean approach for single matrix samples. (Author/CTM)
Descriptors: Analysis of Variance, Data Analysis, Item Sampling, Mathematical Models
Peer reviewedBunda, Mary Anne – Journal of Educational Measurement, 1973
Procedures to be applicable in situations in which large numbers of individuals are tested or in situations where multiple measures are taken. (Author/CB)
Descriptors: Data Collection, Group Norms, Individual Testing, Item Sampling
Peer reviewedScott, William A. – Educational and Psychological Measurement, 1972
Descriptors: Item Sampling, Mathematical Applications, Scoring Formulas, Statistical Analysis
Stanton, George; And Others – 1976
This is one of three volumes presenting the procedures and results of a research effort investigating the effects upon student outcomes of training teachers to implement individual study and self-directed study in their classrooms. In this volume correlation matrices drawn from tests of student variables and achievement are presented. Item…
Descriptors: Academic Achievement, Correlation, Independent Study, Individualized Instruction
Davis, Richard W.; Loadman, William E. – 1973
A subject by item matrix of test responses is shown to be a useful heuristic in criterion referenced and norm referenced test analysis, and in the teaching of measurement. The pattern of responses within the matrix provides indications of item interactions, weak deceptors, and conventional test statistics. The strong visual analogy between the…
Descriptors: Computer Programs, Item Analysis, Item Sampling, Matrices
Peer reviewedBrandenburg, Dale C.; Forsyth, Robert A. – Journal of Educational and Psychological Measurement, 1974
Descriptors: Achievement Tests, Comparative Analysis, Item Sampling, Mathematical Models
Peer reviewedvan der Linden, Wim J. – Applied Psychological Measurement, 1979
The restrictions on item difficulties that must be met when binomial models are applied to domain-referenced testing are examined. Both a deterministic and a stochastic conception of item responses are discussed with respect to difficulty and Guttman-type items. (Author/BH)
Descriptors: Difficulty Level, Item Sampling, Latent Trait Theory, Mathematical Models
Harris, Chester W. – 1975
Achievement tests which are specifically linked to an instructional program and have been developed in relation to an objectives base and/or to an item generation rule are considered, as well as student response data. Three types of studies are outlined and the kind of procedures thought useful illustrated. As various methods for examining…
Descriptors: Achievement Tests, Instructional Programs, Item Banks, Item Sampling
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
Sirotnik, Kenneth A. – 1974
One solution to the problem of obtaining test scores in order to assess multiple outcomes of educational programs is a basically simple technique called matrix sampling. Matrix sampling is the estimation of total test score statistics by administering random subsets of test items to randomly selected students. This chapter lays out the specific…
Descriptors: Data Collection, Educational Testing, Evaluation Methods, Guidelines
Peer reviewedTucker, Ledyard R.; Lewis, Charles – Psychometrika, 1973
Maximum likelihood factor analysis provides an effective method for estimation of factor matrices and a useful test statistic in the likelihood ratio for rejection of overly simple factor models. A reliability coefficient is proposed for analysis of factor solution. (Author/RK)
Descriptors: Analysis of Variance, Factor Analysis, Goodness of Fit, Item Sampling


