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Schwerdtfeger, Sara – ProQuest LLC, 2017
This study examined the differences in knowledge of mathematical modeling between a group of elementary preservice teachers and a group of elementary inservice teachers. Mathematical modeling has recently come to the forefront of elementary mathematics classrooms because of the call to add mathematical modeling tasks in mathematics classes through…
Descriptors: Preservice Teachers, Elementary School Teachers, Mathematical Models, Teacher Characteristics
Peer reviewedHolling, Heinz – Educational and Psychological Measurement, 1983
Recent theoretical analyses of the concept of suppression are identified and discussed. A generalized definition of suppression is presented and the conditions for suppressor structures in the context of the General Linear Model are derived. (Author)
Descriptors: Mathematical Models, Multiple Regression Analysis, Research Methodology, Statistical Analysis
Peer reviewedLehner, Paul E.; Norma, Elliot – Psychometrika, 1980
A new algorithm is used to test and describe the set of all possible solutions for any linear model of an empirical ordering derived from techniques such as additive conjoint measurement, unfolding theory, general Fechnerian scaling, and ordinal multiple regression. The algorithm is computationally faster and numerically superior to previous…
Descriptors: Algorithms, Mathematical Models, Measurement, Multiple Regression Analysis
Peer reviewedPreece, Peter F. W. – Journal of Experimental Education, 1978
Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…
Descriptors: Mathematical Models, Multiple Regression Analysis, Regression (Statistics), Statistical Analysis
Peer reviewedKruskal, J. B. – Psychometrika, 1971
Descriptors: Mathematical Models, Mathematics, Multiple Regression Analysis, Statistical Analysis
Peer reviewedHurst, Rex L. – American Educational Research Journal, 1970
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Research Methodology
Peer reviewedWherry, Robert J., Sr. – Personnel Psychology, 1975
The major portion of this article considered the multiple regression model, used for industrial prediction situations, and fourteen separate episodes the author discussed in trying to make that model work. (Author/RK)
Descriptors: Attitudes, Industrial Personnel, Mathematical Models, Multiple Regression Analysis
Koplyay, Janos B.
The Automatic Interaction Detector (AID) is discussed as to its usefulness in multiple regression analysis. The algorithm of AID-4 is a reversal of the model building process; it starts with the ultimate restricted model, namely, the whole group as a unit. By a unique splitting process maximizing the between sum of squares for the categories of…
Descriptors: Branching, Correlation, Mathematical Models, Multiple Regression Analysis
Elashoff, Janet Dixon; Elashoff, Robert M. – 1970
This paper introduces a model for describing outliers (observations which are extreme in some sense or violate the apparent pattern of other observations) in linear regression which can be viewed as a mixture of a quadratic and a linear regression. The maximum likelihood estimators of the parameters in the model are derived and their asymptotic…
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Research Methodology
Peer reviewedVelicer, Wayne F. – Educational and Psychological Measurement, 1978
A definition of a suppressor variable is presented which is based on the relation of the semipartial correlation to the zero order correlation. Advantages of the definition are given. (Author/JKS)
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Predictor Variables
Peer reviewedMcSweeney, Maryellen; Schmidt, William H. – Journal of Educational Statistics, 1977
The relationship between quantitative predictor variables and the probability of occurrence of one or more levels of a qualitative criterion variable can be analyzed by quantal response techniques. This paper presents and discusses two quantal response models, comparing them to multiple linear regression and discriminant analysis. (Author/JKS)
Descriptors: Discriminant Analysis, Mathematical Models, Multiple Regression Analysis, Predictor Variables
Peer reviewedWolfe, Lee M. – Multiple Linear Regression Viewpoints, 1977
The analytical procedure of path analysis is described in terms of its use in nonexperimental settings in the social sciences. The description assumes a moderate statistical background on the part of the reader. (JKS)
Descriptors: Critical Path Method, Mathematical Models, Multiple Regression Analysis, Research Tools
Peer reviewedCapra, J. R.; Elster, R. S. – Educational and Psychological Measurement, 1971
This method of generating multivariate data differs from previous techniques in that it uses Crout factorization to develop the desired variance-covariance matrix. (Author/CK)
Descriptors: Computer Programs, Mathematical Models, Mathematics, Multiple Regression Analysis
Newman, Isadore; And Others – 1980
When investigating differences between two sets of scores, the t test is appropriate. If the two sets of data are from two groups of subjects, then the independent t test is appropriate. If the two sets are from the same subjects, the dependent t test is required. In this paper, the authors describe the use of a third test when part of a data set…
Descriptors: Hypothesis Testing, Mathematical Models, Multiple Regression Analysis, Research Design
Peer reviewedGoldberger, Arthur S. – Psychometrika, 1971
Several themes which are common to both econometrics and psychometrics are surveyed. The themes are illustrated by reference to permanent income hypotheses, simultaneous equation models, adaptive expectations and partial adjustment schemes, and by reference to test score theory, factor analysis, and time-series models. (Author)
Descriptors: Economics, Factor Analysis, Mathematical Models, Multiple Regression Analysis

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