sn−m Designs containing clear main effects or clear two-factor interactions

2004 ◽  
Vol 69 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Mingyao Ai ◽  
Runchu Zhang
Keyword(s):  
Factor Interactions ◽  
Main Effects

Forms, Importance, and Ineffability of Factor Interactions to Define Personality Disorders: Comment on Lilienfeld et al. (2019)

2019 ◽  
Author(s):  
Stephen D Benning ◽  
Edward Smith
Keyword(s):  
Personality Disorders ◽  
False Positive Rate ◽  
Empirical Work ◽  
Design Studies ◽  
Linear Threshold ◽  
Positive Rate ◽  
Saturation Effects ◽  
Factor Interactions ◽  
Main Effects ◽  
Criterion Variables

The emergent interpersonal syndrome (EIS) approach conceptualizes personality disorders as the interaction among their constituent traits to predict important criterion variables. We detail the difficulties we have experienced finding such interactive predictors in our empirical work on psychopathy, even when using uncorrelated traits that maximize power. Rather than explaining a large absolute proportion of variance in interpersonal outcomes, EIS interactions might explain small amounts of variance relative to the main effects of each trait. Indeed, these interactions may necessitate samples of almost 1,000 observations for 80% power and a false positive rate of .05. EIS models must describe which specific traits’ interactions constitute a particular EIS, as effect sizes appear to diminish as higher-order trait interactions are analyzed. Considering whether EIS interactions are ordinal with non-crossing slopes, disordinal with crossing slopes, or entail non-linear threshold or saturation effects may help researchers design studies, sampling strategies, and analyses to model their expected effects efficiently.

scholarly journals Two-Level Designs to Estimate All Main Effects and Two-Factor Interactions

Technometrics ◽  
2017 ◽  
Vol 59 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Pieter T. Eendebak ◽  
Eric D. Schoen
Keyword(s):  
Factor Interactions ◽  
Main Effects

The analysis and prediction of some two factor interactions in grass breeding

1971 ◽  
Vol 76 (2) ◽  
pp. 301-306 ◽  
Author(s):  
A. J. Wright
Keyword(s):  
Regression Analysis ◽  
Binary Mixtures ◽  
Strongly Correlated ◽  
Regression Parameter ◽  
Grass Breeding ◽  
Factor Interactions ◽  
Main Effects

SUMMARYA method of analysis of two factor experiments is given. This involves a development of the regression analysis used by Finlay & Wilkinson (1963) and others, and allows for the regression of interaction components onto both main effects. The usefulness of the single joint regression parameter for prediction is outlined.The applicability of the analysis to three situations commonly found in grass breeding is illustrated by means of examples. It is concluded that the model may frequently describe variation due to genotype-environment interactions, and among diallel arrangements of binary mixtures of genotypes, but is likely to be of little utility for genetic diallel or other mating schemes unless the genes have a strongly correlated distribution among the parent plants.

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