Two-Way Analysis of Variance with Correlated Errors

1981 ◽  
Vol 49 (2) ◽  
pp. 153 ◽  
Author(s):  
Anders Holst Andersen ◽  
Eva Bjørn Jensen ◽  
Geert Schou ◽  
Eva Bjorn Jensen
Keyword(s):  
Analysis Of Variance ◽  
Correlated Errors

Testing for Correlated Errors in the Analysis of Variance

Biometrics ◽  
1988 ◽  
Vol 44 (3) ◽  
pp. 695 ◽  
Author(s):  
M. A. Cameron ◽  
G. K. Eagleson ◽  
M. E. Willcox ◽  
D. G. Laing ◽  
H. Panhuber
Keyword(s):  
Analysis Of Variance ◽  
Correlated Errors

Tests for the Analysis of Variance of Crossover Designs with Correlated Errors

Biometrics ◽  
1996 ◽  
Vol 52 (2) ◽  
pp. 607 ◽  
Author(s):  
F. Bellavance ◽  
S. Tardif ◽  
M. A. Stephens
Keyword(s):  
Analysis Of Variance ◽  
Correlated Errors ◽  
Crossover Designs

Space–Time Correlation and Its Effects on Methods for Detecting Aquatic Ecological Change

1985 ◽  
Vol 42 (8) ◽  
pp. 1391-1400 ◽  
Author(s):  
Steven P. Millard ◽  
John R. Yearsley ◽  
Dennis P. Lettenmaier
Keyword(s):  
Spatial Correlation ◽  
Analysis Of Variance ◽  
Multivariate Time Series ◽  
Simulated Data ◽  
Temporal Correlation ◽  
Time Correlation ◽  
Ecological Change ◽  
Correlated Errors ◽  
Monitoring Design ◽  
Temporally Correlated

The analysis of variance (ANOVA) is commonly used to analyze observations collected from aquatic monitoring programs designed to detect ecological change. ANOVA assumes that the deviations of the observations from their true means (the errors) are uncorrelated in space and time. Aquatic monitoring data often violate this assumption. The results of Monte Carlo simulations using simulated data generated from both statistically and mechanistically based models show that the presence of either spatially or temporally correlated errors can significantly affect the outcome of ANOVA tests. In practice, spatial correlation is more likely to be a problem than is temporal correlation, given typical monitoring frequencies. The effects of spatial correlation can be minimized through judicious use of control station pairing in the monitoring design. However, when insufficient flexibility exists in the monitoring design, alternate models, such as multivariate time series analysis, or multivariate analysis of variance, must be used in place of ANOVA.

Analysis of Variance and Covariance

2007 ◽  
Author(s):  
C. Patrick Doncaster ◽  
Andrew J. H. Davey
Keyword(s):  
Analysis Of Variance

Analysis of Variance Designs

2008 ◽  
Author(s):  
Glenn Gamst ◽  
Lawrence S. Meyers ◽  
A. J. Guarino
Keyword(s):  
Analysis Of Variance

Selflessness in Anorexia Nervosa as Reflected in the Rorschach Comprehensive System

2012 ◽  
Vol 33 (1) ◽  
pp. 78-93 ◽  
Author(s):  
Gili Curiel-Levy ◽  
Laura Canetti ◽  
Esti Galili-Weisstub ◽  
Myrna Milun ◽  
Eitan Gur ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Anorexia Nervosa ◽  
Analysis Of Variance ◽  
Clinical Observation ◽  
Comparison Group ◽  
Comprehensive System ◽  
Multivariate Analysis Of Variance ◽  
Rorschach Comprehensive System ◽  
Group A

This study examines the expression of selflessness – the tendency to ignore one’s own needs and serve others’ needs – in Rorschach protocols of women suffering from anorexia nervosa. The protocols of 35 women suffering from anorexia nervosa were compared to 30 protocols of a psychiatric comparison group. A multivariate analysis of variance over five variables (AG, PER, PHR, COP, and GHR) was significant: Anorexic patients showed higher characteristics of selflessness compared to the psychiatric comparison group. These findings contribute to the validation of the Rorschach technique and to the clinical observation of selflessness in anorexic patients, and they emphasize specific characteristics in the treatment of anorexia nervosa patients.

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