scholarly journals Construction of blocked factorial designs to estimate main effects and selected two‐factor interactions

2020 ◽  
Author(s):  
J. D. Godolphin
Keyword(s):  
Factorial Designs ◽  
Factor Interactions ◽  
Main Effects

Optimal Fractional Factorial Designs with 18, 34 and 66 Runs

1998 ◽  
Vol 48 (3-4) ◽  
pp. 207-220
Author(s):  
Lijian He ◽  
Mike Jacroux ◽  
Lan Yu
Keyword(s):  
Fractional Factorial ◽  
Factorial Designs ◽  
Fractional Factorial Designs ◽  
Factor Interactions ◽  
Main Effects

In this paper, we show how to augment many of the resolution III and IV minimwn aberration two-level fractional factorial designs given in Chen, Sun and Wu (1993) with two additional runs so that a majority of the designs obtained are optimal under models which contain only main effects or estimable main effects and two factor interactions.

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
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