tors of person characteristics in a different analysis. If we want to predict the slopes on the basis of person characteristics, a better strategy would be to include these as person-level predictors in the analysis. In addition to the item characteristics, we have the person-level variable "age." Because there are four slopes that vary across persons, we can use the respondents' age to predict these four slopes. Age is entered into the analysis centered on its grand mean; the model is presented in Equation (6) :

r. = y^+y2s2U+y^.+y4s4S+y5mu.+y6m2ij +r1MJij+7sM4U+y9M5ij+7|0m6.. (6) +YuSUjAge. + y2lS2VAge. + Y^.Age. +ynS^Age. + u,, + u2j + uy + u4j + ey..

The estimates are presented in Table 19.1 next to the estimates of the previous model. The effects of age are not the same on all slopes. Sensitivity to reasons coming from the respondent herself and her husband increases with age, and sensitivity to reasons coming from the doctor or the media decreases with age.

In the example given, the facets are characteristics of the questions, which is how facet design is commonly used. However, the facet approach is very general and can be extended, for instance, by expanding the person facet, denoted by [X] in Figure 19.1, to include explicit definitions of important respondent characteristics. In addition, it is also possible to extend the response range by defining facets and elements for the responses. This is useful if there are multivariate outcomes or if the response range is assessed by multiple persons such as independent raters. Analyzing facet data with multiple responses requires a multilevel model for multivariate outcomes, which is set up using a separate level for the multiple 130 outcome variables (Hox, 2002). The multilevel model used is similar to the model used for contextual measurement, a subject taken up in the next section.

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