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Wasik, John L. – 1971
This report examines existing mathematical methods that have been utilized to develop educational enrollment estimates and analyzes the applicability of these methods in the community college setting. Models surveyed are based on (1) extrapolation--by either survival cohort or cohort-regression, (2) structural flow, and (3) Markov-type procedures.…
Descriptors: Community Colleges, Enrollment Projections, Enrollment Rate, Enrollment Trends
Burke, John P.; Elashoff, Janet Dixon – 1971
This paper provides a survey of models for the analysis of individual growth data emphasizing the problems posed by serial or time dependence in the application of polynomial regression models. The concepts of serial correlation and autoregressive models are illustrated. It is demonstrated that standard inference procedures may be quite misleading…
Descriptors: Data Analysis, Individual Development, Mathematical Models, Methods Research
Peer reviewed Peer reviewed
Hakstian, Ralph A.; Skakun, Ernest N. – Multivariate Behavioral Research, 1976
Populations of factorially simple and complex data were generated with first the oblique and orthogonal factor models, and then solutions based on special cases of the general orthomax criterion were compared on the basis of these characteristics. The results are discussed and implications noted. (DEP)
Descriptors: Comparative Analysis, Factor Analysis, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Kaiser, Henry F.; Serlin, Ronald C. – Applied Psychological Measurement, 1978
A least-squares solution for the method of paired comparisons is given. The approach provokes a theorem regarding the amount of data necessary and sufficient for a solution to be obtained. A measure of the internal consistency of the least-squares fit is developed. (Author/CTM)
Descriptors: Higher Education, Least Squares Statistics, Mathematical Models, Measurement
Peer reviewed Peer reviewed
Blair, R. Clifford; Higgings, J. J. – American Educational Research Journal, 1978
Kaufman and Sweet's article on the regression analysis of unbalanced factorial designs (EJ 111 767) is reviewed. A number of errors are noted, and relevant literature is cited. (GDC)
Descriptors: Least Squares Statistics, Mathematical Models, Multiple Regression Analysis, Research Design
Peer reviewed Peer reviewed
Sirotnik, 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 reviewed Peer reviewed
Loos, J. S.; Dawson, S. L. – American Journal of Physics, 1978
Some of the properties of electromagnetic showers observed in an experimental study are illustrated. Experimental data and results from quantum electrodynamics are discussed. Data and theory are compared using computer simulation. (BB)
Descriptors: College Science, Computers, Electricity, Energy
Peer reviewed Peer reviewed
Th.van der Kamp, Leo J.; Mellenbergh, Gideon J. – Educational and Psychological Measurement, 1976
Joreskog's model of cogeneric tests is used to analyze agreement between raters. Raters are treated as measuring instruments. The model of cogeneric tests, of which classical parallelism and tau-equivalence are shown to be special cases, is applied to teachers' ratings of students' responses on open-end questions. (Author/RC)
Descriptors: Goodness of Fit, Mathematical Models, Rating Scales, Statistical Analysis
Peer reviewed Peer reviewed
Rennie, Robert R.; Villegas, C. – Journal of Multivariate Analysis, 1976
An asymptotic theory is developed for a new time series model introduced in TM 502 289. An algorithm for computing estimates of the parameters of this time series model is given, and it is shown that these estimators are asymptotically efficient in that they have the same asymptotic distribution as the maximum likelihood estimators. (Author/RC)
Descriptors: Algorithms, Analysis of Covariance, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Bunda, 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 reviewed Peer reviewed
Young, Forrest W.; Cliff, Norman – Psychometrika, 1972
A metric multidimensional scaling (MDS) procedure based on computer-subject interaction is developed, and an experiment designed to validate the procedure is presented. (Author)
Descriptors: Algorithms, Mathematical Models, Multidimensional Scaling, Predictive Measurement
Peer reviewed Peer reviewed
Schmidt, Frank L. – Educational and Psychological Measurement, 1972
Descriptors: Mathematical Models, Multiple Regression Analysis, Predictor Variables, Psychological Testing
Peer reviewed Peer reviewed
Kristof, Walter – Psychometrika, 1973
Paper is concerned with the hypothesis that two variables have a perfect disattenuated correlation, hence measure the same trait except for errors of measurement. (Author/RK)
Descriptors: Analysis of Variance, Correlation, Error of Measurement, Mathematical Models
Peer reviewed Peer reviewed
Samejima, Fumiko – Psychometrika, 1972
This paper proposes a general model for free-response data collected for measuring a specified unidimensional psychological process; systematizes situations which vary with respect to the scoring level of items; and finds out general conditions for the operating characteristic of an item response category to provide a unique maximum likelihood…
Descriptors: Mathematical Applications, Mathematical Models, Mathematics, Measurement Techniques
Peer reviewed Peer reviewed
Ebert, Ronald J. – Psychometrika, 1971
Descriptors: Comparative Analysis, Decision Making, Management Systems, Mathematical Models
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