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Finkbeiner, Carl – Psychometrika, 1979
A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with five heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation. (Author/CTM)
Descriptors: Factor Analysis, Mathematical Models, Maximum Likelihood Statistics, Simulation
Wolfram, Stephen – Scientific American, 1984
Discusses the use of computer programs in science and mathematics. Provides examples of how computation offers a new means of describing and investigating scientific and mathematical systems and how computer simulation can be used to examine new kinds of models for natural phenomena. (JN)
Descriptors: Biological Sciences, Computation, Computer Simulation, Computer Software
Karplus, Walter J. – Perspectives in Computing, 1983
Mathematical modeling problems encountered in many disciplines are discussed in terms of the modeling process and applications of models. The models are classified according to three types of abstraction: continuous-space-continuous-time, discrete-space-continuous-time, and discrete-space-discrete-time. Limitations in different kinds of modeling…
Descriptors: Computer Science, Computer Science Education, Higher Education, Mathematical Applications
Peer reviewed Peer reviewed
Reckase, Mark D. – Journal of Educational Statistics, 1979
Since all commonly used latent trait models assume a unidimensional test, the applicability of the procedure to obviously multidimensional tests is questionable. This paper presents the results of the application of latent trait, traditional, and factor analyses to a series of actual and hypothetical tests that vary in factoral complexity.…
Descriptors: Achievement Tests, Factor Analysis, Goodness of Fit, Higher Education