Sentence examples for each level of attributes from inspiring English sources

Another major departure from the past models is the incorporation of costs associated with each level of attributes.

Linear regression analysis was employed to estimate the utility values assigned to each level of attributes.

An orthogonal fractional factorial design (SPSS Conjoint 16.0) was used to define the scenarios for the CA. The resulting orthogonal matrix captured the principal effects of each level of attributes.

We compared results from the above models on the following criteria: rank on the relative importance of the attributes, and magnitude, direction and significance of the estimates of the β coefficient within each level of the attributes, which were obtained by regressing preference onto the difference in attributes between the two choices.

We assessed the robustness (consistency and discrepancy) of the models on ranking of the relative importance between the attributes and the estimates of the β coefficients within each level of the attributes.

This analysis results in a regression coefficient for each level of the attributes.

Analysis of the scenario order gave a utility score and represented the partial contribution of each level of the attributes in the determination of preferences.

‡Relative importance scores sum to 100 across all attributes; †Aggregate utilities associated with each level of the attribute; smaller or more negative preference weight indicates less preference for that level of an attribute.

†Aggregate utilities associated with each level of the attribute; smaller or more negative preference weight indicates less preference for that level of an attribute.

As indicated, this function is assumed to be the same for each level of an attribute column.

For each respondent, utilities (zero-centered values) were calculated for each level of each attribute using hierarchical Bayes modeling.