Generation of Two-Level Cost Optimal Fractional Factorials

1978 ◽  
Vol 22 (1) ◽  
pp. 599-599
Author(s):  
Joseph J. Pignatiello

It is assumed that, in a 2k factorial experiment, there are different costs per observation at each of the factor combinations. When the number of factors, k, increases, the total number of observations in the full factorial increases rapidly as does the expense of observing all observations in the full factorial. If the experimenter can assume certain classes of higher-order interactions are negligible, then advantage may be taken by observing measurements from an orthogonal fractional factorial. For any “1/2p” fraction of the full factorial, a 2k-p experiment, there are 2p feasible orthogonal fractions that could be selected at random. This paper develops an algorithm for generating the minimum cost such fraction in an efficient way. The problem is formulated as a mathematical programming problem subject to a resolution III constraint (main effects unconfounded). Computational experience is presented.

1978 ◽  
Vol 22 (1) ◽  
pp. 598-598
Author(s):  
Steven M. Sidik ◽  
Arthur G. Holms

In many cases in practice an experimenter has some prior knowledge of indefinite validity concerning the main effects and interactions which would be estimable from a two-level full factorial experiment. Such information should be incorporated into the design of the experiment.


2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Brian Sylcott ◽  
Jeremy J. Michalek ◽  
Jonathan Cagan

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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