Weighting Respondents

Weighting lets some respondents have more impact on summary statistics than others. For example, a respondent with a weight of 2.0 is counted twice as much as another respondent with a weight of 1.0. Weighting is useful for adjusting a sample to reflect known population characteristics. Weights can be applied during all stages of analysis.

There are two ways to define weights:

1. Use a merged variable value as a weight. You may have calculated a weight for each respondent and placed those weights along with the respondent numbers in a text-only file for merging with your part-worth data.

2. Assign weights to segmentation categories. In this case, the weighting variable must be defined as a Custom Segmentation Variable. For example, assume we wanted to weight our sample based on Gender. The table below shows the weight that needs to be given to respondents to achieve the target gender representation.

Actual Proportion Target Proportion Weight Male 0.35 0.49 0.49/0.35 = 1.4000 Female 0.65 0.51 0.51/0.65 = 0.7846

If we apply these weights, the average weight will be 1.0, and the unweighted number of respondents will be equal to the weighted number of respondents. Whether you use a merged variable value as the weight or assign weights to categories of a segmentation variable, we strongly suggest your weights have this property. If not, some statistics in Tables will be incorrect.