Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 0 |
Since 2006 (last 20 years) | 2 |
Descriptor
Multiple Regression Analysis | 14 |
Statistical Analysis | 14 |
Correlation | 5 |
Mathematical Models | 5 |
Measurement | 4 |
Factor Analysis | 3 |
Models | 3 |
Algorithms | 2 |
Analysis of Covariance | 2 |
Mathematics | 2 |
Multivariate Analysis | 2 |
More ▼ |
Source
Psychometrika | 14 |
Author
Cramer, Elliot M. | 1 |
Dekker, David | 1 |
Dusseldorp, Elise | 1 |
Frane, James W. | 1 |
Goldberger, Arthur S. | 1 |
Guttman, Louis | 1 |
Hettmansperger, Thomas P. | 1 |
Jennrich, R. I. | 1 |
Krackhardt, David | 1 |
Kruskal, J. B. | 1 |
Lee, S. Y. | 1 |
More ▼ |
Publication Type
Journal Articles | 7 |
Reports - Research | 5 |
Reports - Descriptive | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Dekker, David; Krackhardt, David; Snijders, Tom A. B. – Psychometrika, 2007
Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among "n" objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors.…
Descriptors: Statistical Bias, Multiple Regression Analysis, Geometric Concepts, Social Networks
Shieh, Gwowen – Psychometrika, 2007
The underlying statistical models for multiple regression analysis are typically attributed to two types of modeling: fixed and random. The procedures for calculating power and sample size under the fixed regression models are well known. However, the literature on random regression models is limited and has been confined to the case of all…
Descriptors: Sample Size, Monte Carlo Methods, Multiple Regression Analysis, 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

Hettmansperger, Thomas P. – Psychometrika, 1978
A unified approach, based on ranks, to the statistical analysis of data arising from complex experimental designs is presented. The rank methods closely parallel the familiar methods of least squares, so that the estimates and tests have natural interpretations. (Author/JKS)
Descriptors: Analysis of Covariance, Multiple Regression Analysis, Nonparametric Statistics, Statistical Analysis

Lord, Frederic M.; Stocking, Martha L. – Psychometrika, 1976
A numerical procedure is outlined for obtaining an interval estimate of the regression of true score or observed score, utilizing only the frequency distribution of observed scores. The procedure assumes that the conditional distribution of observed scores for fixed true scores is binomial. Several illustrations are given. (Author/HG)
Descriptors: Correlation, Multiple Regression Analysis, Raw Scores, Statistical Analysis

Kruskal, J. B. – Psychometrika, 1971
Descriptors: Mathematical Models, Mathematics, Multiple Regression Analysis, Statistical Analysis

Guttman, Louis – Psychometrika, 1971
Descriptors: Definitions, Item Analysis, Measurement, Multiple Regression Analysis

Cramer, Elliot M.; Nicewander, W. Alan – Psychometrika, 1979
A distinction is drawn between redundancy measurement and the measurement of multivariate association between two sets of variables. Several measures of multivariate association between two sets of variables are examined. (Author/JKS)
Descriptors: Correlation, Measurement, Multiple Regression Analysis, Multivariate Analysis

Frane, James W. – Psychometrika, 1976
Several procedures are outlined for replacing missing values in multivariate analyses by regression values obtained in various ways, and for adjusting coefficients (such as factor score coefficients) when data are missing. None of the procedures are complex or expensive. (Author)
Descriptors: Correlation, Discriminant Analysis, Factor Analysis, Multiple Regression Analysis

Goldberger, 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
Dusseldorp, Elise; Meulman, Jacqueline J. – Psychometrika, 2004
The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable with "multiple" covariates. The performance of RTA is compared to the classical…
Descriptors: Simulation, Psychometrics, Multiple Regression Analysis, Models

Novick, Melvin R.; And Others – Psychometrika, 1973
This paper develops theory and methods for the application of the Bayesian Model II method to the estimation of binomial proportions and demonstrates its application to educational data. (Author/RK)
Descriptors: Bayesian Statistics, Educational Testing, Mathematical Models, Measurement

Lee, S. Y.; Jennrich, R. I. – Psychometrika, 1979
A variety of algorithms for analyzing covariance structures are considered. Additionally, two methods of estimation, maximum likelihood, and weighted least squares are considered. Comparisons are made between these algorithms and factor analysis. (Author/JKS)
Descriptors: Analysis of Covariance, Comparative Analysis, Correlation, Factor Analysis

Tenenhaus, Michel – Psychometrika, 1988
Canonical analysis of two convex polyhedral cones involves looking for two vectors whose square cosine is a maximum. New results about the properties of the optimal solution to this problem are presented. The convergence of an alternating least squares algorithm and properties of limits of calculated sequences are discussed. (SLD)
Descriptors: Algorithms, Analysis of Variance, Graphs, Least Squares Statistics