Choice Modelling applications can be designed to estimate main effects only or multiple-way interactions between attributes.
Hadamard matrices have traditionally been used for screening main effects only, because of their complex aliasing structures.
For this situation we present optimal designs in a discrete setting when the alternatives are specified by an analysis of variance model with main effects only.
The two-way ANOVA of the data from day 1 (with the 5 min time bin as the repeated factor) showed that there were main effects only of time (F 2,24 =31,5, P<0.001).
The results for the non-directional JND ANOVA showed that the model with two main effects and a condition × synchrony interaction was preferred over either main effects only model (BF = 12,802,075 ± 1.78%).
In this paper we establish the form of the optimal design for choice experiments in which attributes need not have the same number of levels for testing main effects only, when there are k attributes, and all choice sets are of size m.
Considering Table 3, the seven main effects only account for 5.7% of the explained variance, whereas all 64 effects account for 20.4% of the explained variance.
Further, we evaluate for the first time in a continuous-rating paradigm whether these four cues influence sexual perception in only an additive fashion (i.e., as main effects only) or potentially in a multiplicative fashion.
These plots indicate that of four main effects, only the variable of the initial concentration of TC has a negative effect on response (x%); effects of other variables on response were positive.
The model uses an additive, main effects only design.
We built multiple linear regression models using stepwise backwards deletion and including main effects only (no interaction terms).
