A Computer Programme for Analysis of Variance in Experiments with Factorial Treatments and One or More Error Terms

1964 ◽  
Vol 13 (2) ◽  
pp. 110
Author(s):  
B. S. Coulter
Keyword(s):  
Analysis Of Variance ◽  
Computer Programme ◽  
Error Terms ◽  
Factorial Treatments

scholarly journals Random effects models for complex designs

2020 ◽  
Vol 29 (12) ◽  
pp. 3695-3706
Author(s):  
RG Jarrett ◽  
VT Farewell ◽  
AM Herzberg
Keyword(s):  
Random Effects ◽  
Analysis Of Variance ◽  
Two Phase ◽  
Full Account ◽  
Medical Practitioners ◽  
Random Effects Models ◽  
Multiple Measurements ◽  
Variance Table ◽  
Error Terms ◽  
Phase Design

Plaid designs are characterised by having one set of treatments applied to rows and another set of treatments applied to columns. In a 2003 publication, Farewell and Herzberg presented an analysis of variance structure for such designs. They presented an example of a study in which medical practitioners, trained in different ways, evaluated a series of videos of patients obtained under a variety of conditions. However, their analysis did not take full account of all error terms. In this paper, a more comprehensive analysis of this study is presented, informed by the recognition that the study can also be regarded as a two-phase design. The development of random effects models is outlined and the potential importance of block-treatment interactions is highlighted. The use of a variety of techniques is shown to lead to a better understanding of the study. Examination of the variance components involved in the expected mean squares is demonstrated to have particular value in identifying appropriate error terms for F-tests derived from an analysis of variance table. A package such as ASReml can also be used provided an appropriate error structure is specified. The methods presented can be applied to the design and analysis of other complex studies in which participants supply multiple measurements under a variety of conditions.

Three Additional Error Terms for Analysis of Variance

1961 ◽  
Vol 8 (3) ◽  
pp. 373-376
Author(s):  
H. F. Dingman ◽  
C. D. Windle ◽  
G. Sabagh
Keyword(s):  
Analysis Of Variance ◽  
Additional Error ◽  
Error Terms

Affective-Symbolic Connotations of the Rorschach Inkblots: Fact or Fantasy

1983 ◽  
Vol 56 (1) ◽  
pp. 287-295
Author(s):  
Judith D. Wallach
Keyword(s):  
Analysis Of Variance ◽  
Repeated Measures ◽  
A Priori ◽  
Multiple Comparison ◽  
Male Students ◽  
Point Scale ◽  
Comparison Test ◽  
Multiple Comparison Test ◽  
Repeated Measures Analysis ◽  
Error Terms

The Rorschach inkblots were rated for their similarity to 40 concepts by 111 female and 116 male students. A 5-point scale was used to compare the inkblots with eight concepts in each of five categories: psychoanalytic affective-symbolic connotations, an alternate set of affective-symbolic connotations, “popular” Rorschach responses, affectively toned concepts, and randomly selected nouns. A one-way repeated-measures analysis of variance for each inkblot provided the error terms for Dunn's multiple-comparison test. Although results varied considerably from inkblot to inkblot, the cards were rated over-all more similar to the derived concepts than to their traditionally assumed connotations. These findings and the results of other studies suggest that reliance on any set of hypothesized a priori meanings in Rorschach interpretation may be hazardous or, at best, premature.

Using the randomisation in specifying the ANOVA model and table for properly and improperly replicated grazing trials

1998 ◽  
Vol 38 (4) ◽  
pp. 325 ◽  
Author(s):  
C. J. Brien ◽  
C. G. B. Demétrio
Keyword(s):  
Analysis Of Variance ◽  
Experimental Unit ◽  
Anova Model ◽  
Variance Table ◽  
Error Terms

Summary. A method for deriving the analysis of variance for an experiment is presented and applied to grazing trials. A special feature of grazing trials, specifically utilised by our method, is that they involve at least 2 randomisations: treatments are randomised to field units (for example paddocks or plots), and field units are randomised to animals. Randomisation results in the confounding (‘mixing up’) of terms and our method includes separate terms in the analysis of variance table for confounded terms so that all sources of variability in the experiment have terms for them included in the table and the confounding between the sources of variability in the experiment is explicitly displayed in the table. This information is used in determining the valid error terms and we will present examples that show how to ascertain these for effects of interest and hence which effects can be tested. In this it fulfils the same role as the contentious process of identifying the experimental unit. It will be demonstrated that the inclusion of separate terms for confounded terms results in improper replication in grazing trials being automatically signalled, and makes its ramifications clear.

Transmission Electron Diffraction Analysis by Computer

1970 ◽  
Vol 28 ◽  
pp. 40-41
Author(s):  
R.A. Ploc ◽  
G.H. Keech
Keyword(s):  
Electron Diffraction ◽  
Input Data ◽  
Reciprocal Lattice ◽  
Computer Programme ◽  
Transmission Electron Diffraction ◽  
Usual Procedure ◽  
Transmission Electron ◽  
Complex Shapes ◽  
Computer Input ◽  
Diffraction Effects

An unambiguous analysis of transmission electron diffraction effects requires two samplings of the reciprocal lattice (RL). However, extracting definitive information from the patterns is difficult even for a general orthorhombic case. The usual procedure has been to deduce the approximate variables controlling the formation of the patterns from qualitative observations. Our present purpose is to illustrate two applications of a computer programme written for the analysis of transmission, selected area diffraction (SAD) patterns; the studies of RL spot shapes and epitaxy.When a specimen contains fine structure the RL spots become complex shapes with extensions in one or more directions. If the number and directions of these extensions can be estimated from an SAD pattern the exact spot shape can be determined by a series of refinements of the computer input data.

Sign in / Sign up

Export Citation Format

Share Document