OPTIMAL FRACTIONAL FACTORIAL PLANS FOR MAIN EFFECTS ORTHOGONAL TO TWO- FACTOR INTERACTIONS: 2 TO THE mth POWER SERIES

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.

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

10.31234/osf.io/v8t7q ◽

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.

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Are More Profiles Better Than Fewer?: Searching for Parsimony and Relevance in Stated Choice Experiments

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1719-22 ◽

2000 ◽

Vol 1719 (1) ◽

pp. 165-174 ◽

Cited By ~ 13

Author(s):

Peter R. Stopher ◽

David A. Hensher

Keyword(s):

New Zealand ◽

Degrees Of Freedom ◽

Choice Model ◽

Fractional Factorial ◽

Sources Of Information ◽

Stated Choice ◽

Complex Design ◽

Data Collection Instrument ◽

Main Effects ◽

Better Than

Transportation planners increasingly include a stated choice (SC) experiment as part of the armory of empirical sources of information on how individuals respond to current and potential travel contexts. The accumulated experience with SC data has been heavily conditioned on analyst prejudices about the acceptable complexity of the data collection instrument, especially the number of profiles (or treatments) given to each sampled individual (and the number of attributes and alternatives to be processed). It is not uncommon for transport demand modelers to impose stringent limitations on the complexity of an SC experiment. A review of the marketing and transport literature suggests that little is known about the basis for rejecting complex designs or accepting simple designs. Although more complex designs provide the analyst with increasing degrees of freedom in the estimation of models, facilitating nonlinearity in main effects and independent two-way interactions, it is not clear what the overall behavioral gains are in increasing the number of treatments. A complex design is developed as the basis for a stated choice study, producing a fractional factorial of 32 rows. The fraction is then truncated by administering 4, 8, 16, 24, and 32 profiles to a sample of 166 individuals (producing 1, 016 treatments) in Australia and New Zealand faced with the decision to fly (or not to fly) between Australia and New Zealand by either Qantas or Ansett under alternative fare regimes. Statistical comparisons of elasticities (an appropriate behavioral basis for comparisons) suggest that the empirical gains within the context of a linear specification of the utility expression associated with each alternative in a discrete choice model may be quite marginal.

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

Statistics & Probability Letters ◽

10.1016/j.spl.2004.06.015 ◽

2004 ◽

Vol 69 (2) ◽

pp. 151-160 ◽

Cited By ~ 21

Author(s):

Mingyao Ai ◽

Runchu Zhang

Keyword(s):

Factor Interactions ◽

Main Effects

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Exploring the Role of Interaction Effects in Visual Conjoint Analysis

Journal of Mechanical Design ◽

10.1115/1.4031054 ◽

2015 ◽

Vol 137 (9) ◽

Cited By ~ 3

Author(s):

Brian Sylcott ◽

Jeremy J. Michalek ◽

Jonathan Cagan

Keyword(s):

Conjoint Analysis ◽

Consumer Preferences ◽

Consumer Preference ◽

Interaction Effects ◽

Fractional Factorial ◽

Main Effects ◽

Full Factorial ◽

Biased Estimates ◽

Data Requirements

In conjoint analysis, interaction effects characterize how preference for the level of one product attribute is dependent on the level of another attribute. When interaction effects are negligible, a main effects fractional factorial experimental design can be used to reduce data requirements and survey cost. This is particularly important when the presence of many parameters or levels makes full factorial designs intractable. However, if interaction effects are relevant, main effects design can create biased estimates and lead to erroneous conclusions. This work investigates consumer preference interactions in the nontraditional context of visual choice-based conjoint analysis, where the conjoint attributes are parameters that define a product's shape. Although many conjoint studies assume interaction effects to be negligible, they may play a larger role for shape parameters. The role of interaction effects is explored in two visual conjoint case studies. The results suggest that interactions can be either negligible or dominant in visual conjoint, depending on consumer preferences. Generally, we suggest using randomized designs to avoid any bias resulting from the presence of interaction effects.

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