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Mulaik, Stanley A. – Psychometrika, 1971
Descriptors: Calculus, Factor Analysis, Goodness of Fit, Mathematical Models
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MacDonald, Theodore H. – Mathematics Teacher, 1971
Descriptors: Biology, Genetics, Instruction, Mathematical Concepts
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Farr, S. David – American Educational Research Journal, 1971
Descriptors: Data Analysis, Factor Analysis, Mathematical Models, Research Methodology
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Wennergren, E. Boyd; Nielsen, Darwin B. – Journal of Leisure Research, 1970
Descriptors: Consumer Economics, Mathematical Models, Probability, Recreational Activities
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Hurst, Rex L. – American Educational Research Journal, 1970
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Research Methodology
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Glaymann, Maurice – Educational Studies in Mathematics, 1970
Descriptors: Algebra, Elementary School Mathematics, Geometric Concepts, Geometry
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Dunkum, William – Arithmetic Teacher, 1970
Descriptors: Instruction, Mathematical Models, Number Concepts, Secondary School Mathematics
Ryan, Joseph P. – New Directions for Testing and Measurement, 1983
One of the major theoretical and practical developments in testing is latent trait analysis and item response theory. This report provides a guide for practitioners in understanding, evaluating, and using these developments to meet their testing needs. (Author)
Descriptors: Guidelines, Latent Trait Theory, Mathematical Models, Measurement Techniques
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Battista, Michael T. – Arithmetic Teacher, 1983
The "positive-negative charge" model is described and demonstrated with all four operations on integers. Its major advantages are that it is both concrete and complete. (MNS)
Descriptors: Computation, Instructional Materials, Integers, Mathematical Models
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McDonald, Roderick P. – Psychometrika, 1982
Typically, nonlinear models such as those used in the analysis of covariance structures, are not globally identifiable. Investigations of local identifiability must either yield a mapping onto the entire parameter space, or be confined to points of special interest such as the maximum likelihood point. (Author/JKS)
Descriptors: Analysis of Covariance, Mathematical Models, Maximum Likelihood Statistics, Statistical Analysis
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Murdock, Bennet B., Jr. – Psychological Review, 1982
A theory for storage and retrieval of associative information is presented. Items or events are represented as random vectors. Convolution is used as the storage operation, correlation as the retrieval operation. A distributed memory system is assumed. The theory applies to recognition and recall and covers both accuracy and latency. (Author/RD)
Descriptors: Association (Psychology), Mathematical Models, Memory, Recall (Psychology)
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Werts, C. E.; And Others – Educational and Psychological Measurement, 1981
A linear structural model for comparing quasi-Markov models across populations is demonstrated. A confirmatory factor analysis formulation of the simplex model is also developed for between group comparisons. A variety of possible applications of this approach are suggested. (Author)
Descriptors: Factor Analysis, Longitudinal Studies, Mathematical Models, Research Design
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Wasik, John L. – Educational and Psychological Measurement, 1981
The use of segmented polynomial models is explained. Examples of design matrices of dummy variables are given for the least squares analyses of time series and discontinuity quasi-experimental research designs. Linear combinations of dummy variable vectors appear to provide tests of effects in the two quasi-experimental designs. (Author/BW)
Descriptors: Least Squares Statistics, Mathematical Models, Multiple Regression Analysis, Quasiexperimental Design
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Velicer, Wayne F. – Evaluation Review, 1982
A general model for prediction and association is described for the situation in which both criterion and predictor(s) are discrete variables. The approach can be employed for the general case involving any number of predictors, and the related measures of multiple and partial association are described. (Author/GK)
Descriptors: Correlation, Mathematical Models, Prediction, Predictive Validity
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Waller, Michael I. – Journal of Educational Measurement, 1981
A method based on the likelihood ratio procedure is presented for use in selecting a measurement model from among the Rasch, two-parameter, and three-parameter logistic latent trait models. (Author/BW)
Descriptors: Comparative Analysis, Goodness of Fit, Latent Trait Theory, Mathematical Models
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