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Huang, Shaobo; Fang, Ning – Computers & Education, 2013
Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics--a high-enrollment, high-impact, and core course that many engineering…
Descriptors: Academic Achievement, Grade Point Average, Accuracy, Prediction
Kukla-Acevedo, Sharon – Journal of Educational Research, 2009
The author explored whether 3 workplace conditions were related to teacher mobility decisions. The modeling strategy incorporated a series of binomial and multinomial logistic models to estimate the effects of administrative support, classroom control, and behavioral climate on teachers' decisions to quit teaching or switch schools. The results…
Descriptors: Mathematical Models, Prediction, Probability, Multiple Regression Analysis

Jaccard, James; And Others – Multivariate Behavioral Research, 1990
Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)
Descriptors: Equations (Mathematics), Mathematical Models, Multiple Regression Analysis

Van de Geer, John P. – Psychometrika, 1984
A family of solutions for linear relations among k sets of variables is proposed. Solutions are compared with respect to their optimality properties. For each solution the appropriate stationary equations are given. For one example it is shown how the determinantal equation of the stationary equations can be interpreted. (Author/BW)
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Orthogonal Rotation

Hedges, Larry V.; Olkin, Ingram – Psychometrika, 1981
Commonality components have been defined as a method of partitioning squared multiple correlations. The asymptotic joint distribution of all possible squared multiple correlations is derived. The asymptotic joint distribution of linear combinations of squared multiple correlations is obtained as a corollary. (Author/JKS)
Descriptors: Correlation, Data Analysis, Mathematical Models, Multiple Regression Analysis

Johansson, J. K. – Psychometrika, 1981
An extension of Wollenberg's redundancy analysis (an alternative to canonical correlation) is proposed to derive Y-variates corresponding to the optimal X-variates. These variates are maximally correlated with the given X-variates, and depending upon the standardization chosen they also have certain properties of orthogonality. (Author/JKS)
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Multivariate Analysis

Holling, 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

Lehner, 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

Preece, 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

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
Curran, Patrick J. – Multivariate Behavioral Research, 2003
A core assumption of the standard multiple regression model is independence of residuals, the violation of which results in biased standard errors and test statistics. The structural equation model (SEM) generalizes the regression model in several key ways, but the SEM also assumes independence of residuals. The multilevel model (MLM) was…
Descriptors: Structural Equation Models, Multiple Regression Analysis, Observation, Mathematical Models

Bryk, Judith F.; And Others – Journal of Educational Statistics, 1980
A statistical analysis procedure is developed, based on the notion that many educational programs are dynamic interventions in natural growth processes, and is called value-added analysis. The theory of value-added analysis, and several applications are presented. (Author/JKS)
Descriptors: Data Analysis, Evaluation Methods, Mathematical Models, Multiple Regression Analysis

Cohen, Jacob – Educational and Psychological Measurement, 1980
When sample sizes and/or X intervals are unequal, the analysis of variance computations for trend analysis become quite complicated. This article shows how multiple regression/correlation analysis may be applied in order to accomplish with great simplicity trend analysis under "irregular" conditions. (Author/RL)
Descriptors: Correlation, Least Squares Statistics, Mathematical Models, Multiple Regression Analysis

Newman, Isadore; Thomas, Jay – Multiple Linear Regression Viewpoints, 1979
Fifteen examples using different formulas for calculating degrees of freedom for power analysis of multiple regression designs worked out by Cohen are presented, along with a more general formula for calculating such degrees of freedom. (Author/JKS)
Descriptors: Hypothesis Testing, Mathematical Models, Multiple Regression Analysis, Power (Statistics)

Fornell, Claes; And Others – Multivariate Behavioral Research, 1988
This paper shows that redundancy maximization with J. K. Johansson's extension can be accomplished via a simple iterative algorithm based on H. Wold's Partial Least Squares. The model and the iterative algorithm for the least squares approach to redundancy maximization are presented. (TJH)
Descriptors: Algorithms, Equations (Mathematics), Least Squares Statistics, Mathematical Models