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Peer reviewedRozeboom, William W. – Psychometrika, 1982
Bounds for the multiple correlation of common factors with the items which comprise those factors are developed. It is then shown that under broad, but not completely general, conditions, the circumstances under which an infinite item domain does or does not perfectly determine selected subsets of its common factors. (Author/JKS)
Descriptors: Factor Analysis, Item Analysis, Multiple Regression Analysis, Test Items
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 reviewedLutz, J. Gary – Educational and Psychological Measurement, 1983
A method is presented for the construction of an artificial data set which will illustrate the behavior of the traditional, the negative, and the reciprocal suppressor variable in multiple regression analysis. It extends the method of Dayton (1972) and includes the previously reciprocal suppression defined by Conger (1974). (Author)
Descriptors: Multiple Regression Analysis, Predictive Measurement, Research Methodology, Suppressor Variables
Peer reviewedFleming, James S. – Educational and Psychological Measurement, 1981
The perfunctory use of factor scores in conjunction with regression analysis is inappropriate for many purposes. It is suggested that factoring methods are most suitable for independent variable sets when some consideration has been given to the nature of the domain, which is implied by the predictors. (Author/BW)
Descriptors: Factor Analysis, Multiple Regression Analysis, Predictor Variables, Research Problems
Peer reviewedRozeboom, William W. – Psychometrika, 1979
For idealized item configurations, equal item weights are often virtually as good for a particular predictive purpose as the item weights that are theoretically optimal. What has not been clear, however, is what happens to the similarity when the item configuration's variance structure is complex. (Author/CTM)
Descriptors: Multiple Regression Analysis, Predictor Variables, Scoring Formulas, Weighted Scores
Peer reviewedGross, Alan L. – Psychometrika, 1981
The utility of least squares multiple regression in predicting new scores from previously established equations is considered. It is shown that in the absence of useful prior information, and when normality assumptions are not violated, least squares multiple regression weights are superior to alternatives recently presented in the literature.…
Descriptors: Bayesian Statistics, Least Squares Statistics, Multiple Regression Analysis, Validity
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 reviewedMcDonald, Roderick P.; And Others – Psychometrika, 1979
Problems in avoiding the singularity problem in analyzing matrices for optimal scaling are addressed. Conditions are given under which the stationary points and values of a ratio of quadratic forms in two singular matrices can be obtained by a series of simple matrix operations. (Author/JKS)
Descriptors: Factor Analysis, Matrices, Measurement, Multiple Regression Analysis
Peer reviewedWoodward, J. Arthur; Overall, John E. – Educational and Psychological Measurement, 1976
A convenient, two-stage general linear regression approach to analysis of variance is described for use in univariate or multivariate designs involving one repeated measurement factor and one or more independent classification factors. A brief illustrative example is provided. (Author)
Descriptors: Analysis of Variance, Interaction, Multiple Regression Analysis, Multivariate Analysis
Peer reviewedLloyd, Dee Norman – Educational and Psychological Measurement, 1976
A multiple regression was used to predict the grade in which secondary school dropouts would leave school. Predictors were twenty measures drawn from sixth grade records. It was concluded that a construct labelled "level of educational attainment" is the best determinant of both whether and when a student will drop out. (Author/JKS)
Descriptors: Dropout Characteristics, Dropout Research, Multiple Regression Analysis, Predictor Variables
Peer reviewedHarris, Richard J.; McNeil, Keith – Mid-Western Educational Researcher, 1993
Presents two viewpoints about the use and interpretability of beta weights in educational research: (1) that beta weights should be interpreted as a logical index of the importance of individual predictors within the context of the entire set of predictors; and (2) that interpretation requires certain cautions and conditions. (SV)
Descriptors: Data Interpretation, Educational Research, Multiple Regression Analysis, Predictor Variables
Peer reviewedSchafer, William D. – Measurement and Evaluation in Counseling and Development, 1991
Presents first of two-part editorial (second part to appear in next journal issue) proposing guidelines for developing useful tables to report multiple regression outcomes. Pays particular attention to situations where a hierarchical multiple regression analysis is guided by theory. Concludes that some conventional way to report multiple…
Descriptors: Multiple Regression Analysis, Regression (Statistics), Research Problems, Tables (Data)
Peer reviewedFoster, Leslie; Brown, Randall; Phillips, Barbara; Carlson, Barbara Lepidus – Gerontologist, 2005
Purpose: We assess the effect of consumer-directed care on the emotional, physical, and financial well-being of the primary informal caregivers of the Medicaid beneficiaries who voluntarily joined Arkansas's Cash and Counseling demonstration. Design and Methods: The demonstration randomly assigned beneficiaries to a program in which they could…
Descriptors: Caregivers, Well Being, Health Services, Interviews
Lipovetsky, Stan; Conklin, W. Michael – International Journal of Mathematical Education in Science and Technology, 2004
Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…
Descriptors: Multiple Regression Analysis, Regression (Statistics), Mathematical Formulas, College Mathematics

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