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Ethan C. Brown; Mohammed A. A. Abulela – Practical Assessment, Research & Evaluation, 2025
Moderated multiple regression (MMR) has become a fundamental tool for applied researchers, since many effects are expected to vary based on other variables. However, the inherent complexity of MMR creates formidable challenges for adequately performing power analysis on interaction effects to ensure reliable and replicable research results. Prior…
Descriptors: Statistical Analysis, Multiple Regression Analysis, Models, Programming Languages
Samuel, Koji; Mulenga, H. M.; Angel, Mukuka – Journal of Education and Practice, 2016
This paper investigates the challenges faced by secondary school teachers and pupils in the teaching and learning of algebraic linear equations. The study involved 80 grade 11 pupils and 15 teachers of mathematics, drawn from 4 selected secondary schools in Mufulira district, Zambia in Central Africa. A descriptive survey method was employed to…
Descriptors: Secondary School Teachers, Secondary School Students, Secondary School Mathematics, Algebra
Peer reviewedFindeisen, Peter – Psychometrika, 1979
Guttman's assumption underlying his definition of "total images" is rejected. Partial images are not generally convergent everywhere. Even divergence everywhere is shown to be possible. The convergence type always found on partial images is convergence in quadratic mean; hence, total images are redefined as quadratic mean-limits.…
Descriptors: Factor Analysis, Mathematical Formulas, Multiple Regression Analysis, Sampling
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
Peer reviewedSkinner, C. J. – Psychometrika, 1984
Multivariate selection can be represented as a linear transformation in a geometric framework. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation. (Author/BW)
Descriptors: Factor Analysis, Geometric Concepts, Mathematical Formulas, Multiple Regression Analysis
Peer reviewedSklar, Michael G. – Journal of Educational Statistics, 1980
It has long been popular to utilize the least squares estimation procedure for fitting the multiple linear regression model to observed data. In this paper, two useful alternatives to least squares estimation in exploratory data analysis are examined: least absolute value estimation and Chebychev estimation. (Author/JKS)
Descriptors: Data Analysis, Least Squares Statistics, Linear Programing, Mathematical Formulas
Peer reviewedClaudy, John G. – Applied Psychological Measurement, 1979
Equations for estimating the value of the multiple correlation coefficient in the population underlying a sample and the value of the population validity coefficient of a sample regression equation were investigated. Results indicated that cross-validation may no longer be necessary for certain purposes. (Author/MH)
Descriptors: Correlation, Mathematical Formulas, Multiple Regression Analysis, Predictor Variables
Peer reviewedCarter, David S. – Educational and Psychological Measurement, 1979
There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)
Descriptors: Comparative Analysis, Correlation, Mathematical Formulas, Multiple Regression Analysis
Peer reviewedAnd Others; Drasgow, Fritz – Applied Psychological Measurement, 1979
A Monte Carlo experiment was used to evaluate four procedures for estimating the population squared cross-validity of a sample least squares regression equation. One estimator was particularly recommended. (Author/BH)
Descriptors: Correlation, Least Squares Statistics, Mathematical Formulas, Multiple Regression Analysis
Peer reviewedNewman, Isadore; And Others – Multiple Linear Regression Viewpoints, 1979
A Monte Carlo simulation was employed to determine the accuracy with which the shrinkage in R squared can be estimated by five different shrinkage formulas. The study dealt with the use of shrinkage formulas for various sample sizes, different R squared values, and different degrees of multicollinearity. (Author/JKS)
Descriptors: Computer Programs, Correlation, Goodness of Fit, Mathematical Formulas
Shin, Tacksoo – Asia Pacific Education Review, 2007
This study introduces three growth modeling techniques: latent growth modeling (LGM), hierarchical linear modeling (HLM), and longitudinal profile analysis via multidimensional scaling (LPAMS). It compares the multilevel growth parameter estimates and potential predictor effects obtained using LGM, HLM, and LPAMS. The purpose of this multilevel…
Descriptors: Multidimensional Scaling, Academic Achievement, Structural Equation Models, Causal Models
Peer reviewedPlomin, Robert; Daniels, Denise – Merrill-Palmer Quarterly, 1984
Discusses the concept of temperament interactions in the context of statistical interaction. Categorizes temperament interactions that involve temperament as an independent variable, as a dependent variable, or as both. Describes use of hierarchical multiple regression for the analysis of temperament interactions. (Author/CI)
Descriptors: Classification, Environmental Influences, Family Environment, Hypothesis Testing
Peer reviewedMarques, Todd E.; And Others – Journal of Educational Psychology, 1979
Faculty and students rated profiles of 100 hypothetical instructors. Findings suggest that valid systems of instructional evaluation should focus on amount of information, arousal of student interest, presentation style, and general knowledge of the field. The relative importance of content to style dimensions was slightly greater for faculty…
Descriptors: College Faculty, College Students, Departments, Higher Education

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