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Based on dependent variables (i.e., observed ratings of each scenario) and the specified attributes (i.e. independent variables), part worth utility values were estimated.
Total utility of each respondent with respect to a scenario, was determined from the combination of individual or part worth utility values for each factor.
The coefficient range is the difference between the smallest (negative) part worth utility and the largest part worth utility within factor levels.
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Within the factors, part worth utilities of each level were also investigated.
The data were analyzed using latent class conditional logit models to estimate values (part worth utilities) for each level of the nine attributes relating to the CHU9D.
A cluster analysis of the part-worth utility scores revealed three clusters: packaging, quality and convenience.
A negative part-worth utility indicates less desired levels of the attributes, whereas a positive part-worth utility indicates more desired levels of the attributes.
Part-worth utility is a computed preference weight for each attribute and its respective levels.
Ten park attributes were included in the survey and part-worth utility and importance scores were estimated for each attribute with Hierarchical Bayes analyses using Sawtooth Software.
In Table 1 we present the part-worth utility scores for each level of the five factors used to describe short films.
With effects coding, part-worth utility estimates represent the deviation of the mean of each attribute level from the overall mean/the model intercept.
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CEO of Professional Science Editing for Scientists @ prosciediting.com