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Choice Modelling applications can be designed to estimate main effects only or multiple-way interactions between attributes.
Interactions between attributes in the discrete choice surveys, and between attributes and population characteristics will be explored in the analysis for patients and caregivers.
We tested a number of different specifications for the utility function (e.g., categorical or numerical attribute levels, two-way interactions between attributes, several attribute transformations).
This sampling method will enable exploration of interactions between attributes and sociodemographic factors, and present subgroup analyses within the sample size.
39 Interactions between attributes in the discrete choice surveys and between attributes and population characteristics (eg, age, gender, income, education, prior ED experience) will be explored in the mixed logit analysis.
In practice, such a design is rarely feasible; efficient designs are therefore paramount, particularly when considering multiple choice options and interactions between attributes and socio-demographic characteristics on choice.
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The main effects analysis did not assess statistical interactions between attribute levels and sociodemographic variables.
The fully adjusted model therefore includes all interactions between attribute impact weights and level scale values for the remaining covariates.
To explore whether the heterogeneity observed in table 2 is explained by differences in gender and cigarette smoking status, we estimated additional multinomial logit models that included interactions between attribute levels and gender (figure 2A), and cigarette smoking status (figure 2B).
It was assumed that there was no interaction between attributes [ 30, 31].
Likewise, we did not find an interaction between attribute dimension and brand cue, F(10, 250)<1, p = .60, ns.
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