Multivariate Multiple Regression Analyses: A Permutation Method for Linear Models

2002 ◽  
Vol 91 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Multiple Regression ◽  
Euclidean Distance ◽  
Linear Models ◽  
Latin Square ◽  
Experimental Designs ◽  
Regression Analyses ◽  
Permutation Procedure ◽  
Multivariate Extension ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances

A multivariate extension of a univariate procedure for the analysis of experimental designs is presented. A Euclidean-distance permutation procedure is used to evaluate multivariate residuals obtained from a regression algorithm, also based on Euclidean distances. Applications include various completely randomized and randomized block experimental designs such as one-way, Latin square, factorial, nested, and split-plot designs, with and without covariates. Unlike parametric procedures, the only required assumption is the randomization of subjects to treatments.

A Fortran Program for Permutation Covariate Analyses of Residuals Based on Euclidean Distance

1998 ◽  
Vol 82 (2) ◽  
pp. 371-375 ◽  
Author(s):  
Kenneth J. Berry ◽  
Paul W. Mielke
Keyword(s):  
Euclidean Distance ◽  
Latin Square ◽  
Fortran Program ◽  
Experimental Designs ◽  
Permutation Procedure ◽  
Split Plot

A FORTRAN program is presented to analyze the results of experimental designs. A Euclidean distance permutation procedure is used to evaluate residuals obtained from least sum of absolute deviations regression. Applications include completely randomized and randomized block configurations including one-way, factorial, split plot, and Latin square designs, with or without covariates.

Multivariate Multiple Regression Prediction Models: A Euclidean Distance Approach

2003 ◽  
Vol 92 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Prediction Model ◽  
Multiple Regression ◽  
Effect Size ◽  
Euclidean Distance ◽  
Prediction Models ◽  
Regression Coefficients ◽  
Multiple Response ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances ◽  
Distance Approach

An extension of a multiple regression prediction model to multiple response variables is presented. An algorithm using least sum of Euclidean distances between the multivariate observed and model-predicted response values provides regression coefficients, a measure of effect size, and inferential procedures for evaluating the extended multivariate multiple regression prediction model.

Permutation Covariate Analyses of Residuals Based on Euclidean Distance

1997 ◽  
Vol 81 (3) ◽  
pp. 795-802 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Euclidean Distance ◽  
Latin Square ◽  
Experimental Designs ◽  
Permutation Procedure ◽  
Split Plot ◽  
Compound Symmetry ◽  
Homogeneity Of Variance

A new procedure to analyze the results of experimental designs is introduced. A permutation procedure based on Euclidean distance is used to evaluate residuals obtained from least sum of absolute deviations regression. Applications include completely randomized and randomized block configurations including one-way, factorial, split-plot, and Latin square designs, with or without covariates. Parametric assumptions of homogeneity of variance, compound symmetry, and normality are eliminated with this procedure.

Behavioral Referents for Molar Ratings of Heterosocial Skill

1982 ◽  
Vol 50 (1) ◽  
pp. 139-146 ◽  
Author(s):  
W. Stephen Royce
Keyword(s):  
Multiple Regression ◽  
Linear Models ◽  
Eye Contact ◽  
Regression Analyses ◽  
Linear Modeling ◽  
Hand Gestures ◽  
Men And Women ◽  
Behavioral Cues ◽  
Wide Range ◽  
Highly Skilled

A linear modeling technique was used to identify valid behavioral referents of molar heterosocial skill ratings in both men and women. Videotapes of the heterosocial interactions of 30 men and 30 women representing a wide range of skill were shown to untrained peers who made molar heterosocial skill ratings and supplied lists of the behavioral cues they believed to be useful in discriminating skillful and unskillful subjects. The most widely endorsed cues were then scored for their rates of occurrence in the target subjects' interactions, and multiple regression analyses were used to construct linear models of behavioral referents for the molar heterosocial skill ratings. Highly skilled men were those who kept their gaze up, asked questions, and used appropriate hand gestures. Highly skilled women were those who kept their gaze up, made eye contact, and avoided speaking too softly.

scholarly journals Organizational cooperation and knowledge management in research and development organizations

Psihologija ◽  
2017 ◽  
Vol 50 (1) ◽  
pp. 1-20
Author(s):  
Dos Rebelo ◽  
Leonor Pais ◽  
Lisete Mónico ◽  
Luísa Rebelo ◽  
Carolina Moliner
Keyword(s):  
Knowledge Management ◽  
Research And Development ◽  
Multiple Regression ◽  
Short Form ◽  
Regression Analyses ◽  
Large Effect Size ◽  
Development Organizations ◽  
Multivariate Multiple Regression ◽  
Organizational Cooperation ◽  
A Current

Organizational Cooperation (OC) is a current concept that responds to the growing interdependence among individuals and teams. Likewise, Knowledge Management (KM) accompanies specialization in all sectors of human activity. Most KM processes are cooperation-intensive, and the way both constructs relate to each other is relevant in understanding organizations and promoting performance. The present paper focuses on that relationship. The Organizational Cooperation Questionnaire (ORCOQ) and the Short form of the Knowledge Management Questionnaire (KMQ-SF) were applied to 639 members of research and development (R&D) organizations (Universities and Research Institutes). Descriptive, correlational, linear multiple regression and multivariate multiple regression analyses were performed. Results showed significant positive relationships between the ORCOQ and all the KMQ-SF dimensions. The prediction of KMQ-SF showed a large effect size (R2 = 62%). These findings will impact on how KM and OC are seen, and will be a step forward in the development of this field.

Language and Literacy Predictors of Dialect Density Among School-Age African American Children From Two Geographic Regions

2020 ◽  
Vol 51 (3) ◽  
pp. 807-820
Author(s):  
Lena G. Caesar ◽  
Marie Kerins
Keyword(s):  
African American ◽  
Multiple Regression ◽  
Oral Language ◽  
East Coast ◽  
Geographic Region ◽  
Literacy Skills ◽  
School Age ◽  
Regression Analyses ◽  
Geographic Regions ◽  
American Children

Purpose The purpose of this study was to investigate the relationship between oral language, literacy skills, age, and dialect density (DD) of African American children residing in two different geographical regions of the United States (East Coast and Midwest). Method Data were obtained from 64 African American school-age children between the ages of 7 and 12 years from two geographic regions. Children were assessed using a combination of standardized tests and narrative samples elicited from wordless picture books. Bivariate correlation and multiple regression analyses were used to determine relationships to and relative contributions of oral language, literacy, age, and geographic region to DD. Results Results of correlation analyses demonstrated a negative relationship between DD measures and children's literacy skills. Age-related findings between geographic regions indicated that the younger sample from the Midwest outscored the East Coast sample in reading comprehension and sentence complexity. Multiple regression analyses identified five variables (i.e., geographic region, age, mean length of utterance in morphemes, reading fluency, and phonological awareness) that accounted for 31% of the variance of children's DD—with geographic region emerging as the strongest predictor. Conclusions As in previous studies, the current study found an inverse relationship between DD and several literacy measures. Importantly, geographic region emerged as a strong predictor of DD. This finding highlights the need for a further study that goes beyond the mere description of relationships to comparing geographic regions and specifically focusing on racial composition, poverty, and school success measures through direct data collection.

The use of linear models and matrix least squares in clinical chemistry.

1979 ◽  
Vol 25 (6) ◽  
pp. 840-855 ◽  
Author(s):  
S N Deming ◽  
S L Morgan
Keyword(s):  
Least Squares ◽  
Precise Measurement ◽  
Linear Models ◽  
Clinical Chemistry ◽  
Experimental Designs ◽  
Unified Approach

Abstract We present a unified approach to the use of linear models and matrix least squares with the intention of providing a better understanding of the techniques themselves and of the statistics that arise from these techniques as they are used in clinical chemistry. Emphasis is placed on the importance of appropriate experimental designs and adequately precise measurement processes for efficiently obtaining the desired information.

THE EVALUATION OF SELF-MONITORING ATTRIBUTES

1991 ◽  
Vol 19 (3) ◽  
pp. 205-215 ◽  
Author(s):  
Arts Jiujias ◽  
Peter Horvath
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Regression Analyses ◽  
Self Monitoring

Eighty-six Canadian female undergraduates attributed self-monitoring traits to a target presented on videotape, and evaluated her in terms of liking. Attributed self-monitoring was negatively correlated with attraction to the target and was the only predictor of attraction in a multiple regression analysis. Multiple regression analyses with subscales of attributed self-monitoring as predictors suggested that the evaluations may be the result of the attributed unpredictability of the high self-monitoring prototype.

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