Analysing repeated measures or randomized block designs using trimmed means

1993 ◽  
Vol 46 (1) ◽  
pp. 63-76 ◽  
Author(s):  
Rand R. Wilcox
Keyword(s):  
Repeated Measures ◽  
Block Designs ◽  
Trimmed Means ◽  
Randomized Block Designs

Power Comparisons of Eight Tests for Sphericity in Repeated Measures Designs

1992 ◽  
Vol 17 (3) ◽  
pp. 233-249 ◽  
Author(s):  
John E. Cornell ◽  
Dean M. Young ◽  
Samuel L. Seaman ◽  
Roger E. Kirk
Keyword(s):  
Repeated Measures ◽  
Covariance Matrices ◽  
Block Designs ◽  
Sample Covariance Matrices ◽  
Large Samples ◽  
Sample Covariance ◽  
Invariant Test ◽  
Power Comparisons ◽  
Repeated Measures Designs ◽  
Randomized Block Designs

A Monte Carlo simulation was conducted to investigate the relative power of eight tests for sphericity in randomized block designs. Box’s (1954) epsilon values º = .35, .55, .75, .80, .85, .90, .95, and 1.00 were used to quantify departures from sphericity for rank-1 population covariance matrices of dimension p = 3, 5, 7, and 9. Sample covariance matrices were generated for samples of size n = 10, 15, 20, and 30. The locally best invariant test demonstrated substantial power to detect departures from sphericity—regardless of p— for both small and large samples for rank-1 alternatives. Recommendations are made regarding the use of preliminary tests.

scholarly journals Addressing spatial variability in provenance experiments exemplified in two trials with black spruce

2008 ◽  
Vol 53 (No. 2) ◽  
pp. 47-56 ◽  
Author(s):  
T. Funda ◽  
M. Lstibůrek ◽  
J. Klápště ◽  
I. Permedlová ◽  
J. Kobliha
Keyword(s):  
Spatial Variability ◽  
Picea Mariana ◽  
Spatial Model ◽  
Important Implication ◽  
Black Spruce ◽  
Block Designs ◽  
Fit Statistics ◽  
Provenance Trials ◽  
Sources Of Variation ◽  
Randomized Block Designs

Two exemplary black spruce (<i>Picea mariana</i> [Mill.] B.S.P.) provenance trials were analyzed using traditional and spatial techniques. The objective was to find out possible differences between these approaches in terms of both the resulting fit-statistics and the estimated mean heights of provenances. Further, the spatial model was consequently adjusted to treat global and extraneous sources of variation. As expected, models incorporating spatial variation provided a better fit to the data. Consequently, there was also a noticeable shift in ranking of individual provenances, which has an important implication for the interpretation of provenance experiments results. Problems associated with the analysis of traditional randomized block designs in forestry research are discussed.

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