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Lei, Wu; Qing, Fang; Zhou, Jin – International Journal of Distance Education Technologies, 2016
There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…
Descriptors: Causal Models, Attribution Theory, Correlation, Evaluation Methods
Pronk, Jeroen; Olthof, Tjeert; Goossens, Frits A. – Journal of Early Adolescence, 2015
This study investigated personality correlates of early adolescents' tendency to either defend victims of bullying or to avoid involvement in bullying situations. Participants were 591 Dutch fifth- and sixth-grade students (X-bar[subscript age] = 11.42 years). Hierarchical regression models were run to predict these students' peer-reported…
Descriptors: Personality Traits, Correlation, Bullying, Victims
Bentler, Peter M.; Yuan, Ke-Hai – Psychometrika, 2011
Indefinite symmetric matrices that are estimates of positive-definite population matrices occur in a variety of contexts such as correlation matrices computed from pairwise present missing data and multinormal based methods for discretized variables. This note describes a methodology for scaling selected off-diagonal rows and columns of such a…
Descriptors: Scaling, Factor Analysis, Correlation, Predictor Variables
PDF pending restorationHollingsworth, Holly – 1977
A theorem of Spjotvoll (1972) was used to determine and apply a confidence interval for the difference of two multiple correlations based on observations from a single sample. Spjotvoll's method of comparing regression functions is also applicable to a comparison of dependent multiple correlations, an unsolved problem posed by Hotelling in 1940.…
Descriptors: Comparative Analysis, Correlation, Matrices, Predictor Variables
Peer reviewedFriedman, Sally; Weisberg, Herbert F. – Educational and Psychological Measurement, 1981
The first eigenvalue of a correlation matrix indicates the maximum amount of the variance of the variables which can be accounted for with a linear model by a single underlying factor. The first eigenvalue measures the primary cluster in the matrix, its number of variables and average correlation. (Author/RL)
Descriptors: Correlation, Mathematical Models, Matrices, Predictor Variables
Ping, Chieh-min; Tucker, Ledyard R. – 1976
Prediction for a number of criteria from a set of predictor variables in a system of regression equations is studied with the possibilities of linear transformations applied to both the criterion and predictor variables. Predictive composites representing a battery of predictor variables provide identical estimates of criterion scores as do the…
Descriptors: Correlation, Factor Analysis, Matrices, Multiple Regression Analysis
Peer reviewedColes, Gary J. – Multiple Linear Regression Viewpoints, 1979
This paper discusses how full model dummy variables can be used with partial correlation or multiple regression procedures to compute matrices of pooled within-group correlations. (Author/CTM)
Descriptors: Correlation, Matrices, Multiple Regression Analysis, Predictor Variables
Rim, Eui-Do – 1975
A stepwise canonical procedure, including two selection indices for variable deletion and a rule for stopping the iterative procedure, was derived as a method of selecting core variables from predictors and criteria. The procedure was applied to simulated data varying in the degree of built in structures in population correlation matrices, number…
Descriptors: Analysis of Variance, Comparative Analysis, Correlation, Factor Analysis
Peer reviewedMalgady, Robert G.; Huck, Schuyler W. – Educational and Psychological Measurement, 1978
The t ratio used in testing the difference between two independent regression coefficients is generalized to the multivariate case of testing the difference between two vectors of regression coefficients. This is particularly useful in determining which of two variables best predicts a number of criterion variables. (Author/JKS)
Descriptors: Correlation, Hypothesis Testing, Matrices, Multiple Regression Analysis
Curtis, Ervin W. – 1976
The optimum weighting of variables to predict a dependent-criterion variable is an important problem in nearly all of the social and natural sciences. Although the predominant method, multiple regression analysis (MR), yields optimum weights for the sample at hand, these weights are not generally optimum in the population from which the sample was…
Descriptors: Correlation, Error Patterns, Factor Analysis, Matrices
Mayeske, George W.; Beaton, Albert E., Jr. – 1974
The results of an algorithm which is designed to take a set of commonality coefficients, either real or manipulated, and, if possible, produce one or more sets of regressor correlations that are consistent with them are examined. A number of different ways of resolving the higher order commonality values into their lower orders were tried and the…
Descriptors: Algorithms, Computer Programs, Correlation, Mathematical Applications
Beaton, Albert E., Jr. – 1973
Commonality analysis is an attempt to understand the relative predictive power of the regressor variables, both individually and in combination. The squared multiple correlation is broken up into elements assigned to each individual regressor and to each possible combination of regressors. The elements have the property that the appropriate sums…
Descriptors: Algorithms, Computer Programs, Correlation, Data Analysis
Vasu, Ellen S.; Elmore, Patricia B. – 1975
The effects of the violation of the assumption of normality coupled with the condition of multicollinearity upon the outcome of testing the hypothesis Beta equals zero in the two-predictor regression equation is investigated. A monte carlo approach was utilized in which three differenct distributions were sampled for two sample sizes over…
Descriptors: Correlation, Error of Measurement, Factor Structure, Hypothesis Testing
Peer reviewedCousins, J. Bradley; Leithwood, Kenneth A. – Knowledge: Creation, Diffusion, Utilization, 1993
Discussion of school improvement focuses on a study of Ontario educators that examined their use of various sources of information, including conferences, workshops, and inservice training, for improvement purposes. Knowledge utilization is discussed; interactive processes between the information and the setting are considered; and predictor…
Descriptors: Conferences, Correlation, Elementary Education, Foreign Countries
Simon, Charles W. – 1975
An "undesigned" experiment is one in which the predictor variables are correlated, either due to a failure to complete a design or because the investigator was unable to select or control relevant experimental conditions. The traditional method of analyzing this class of experiment--multiple regression analysis based on a least squares…
Descriptors: Bias, Computer Programs, Correlation, Data Analysis
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