Surveys play a key role in researching public perceptions of and attitudes toward science. Accordingly, there is a breadth of often-used survey instruments available which have also been adopted for segmentation analyses. Even though many of these segmentation solutions are similar in their aims, they often include a large numbers of variables, making it more difficult for other researchers to build on these solutions, as survey time is scarce. Therefore, we demonstrate how a large number of variables that were used for a comprehensive segmentation analysis can be reduced considerably without losing too much information. We develop and test a short survey instrument to segment populations according to their attitudes toward science. Results show that segmentation results can be replicated with over 90% accuracy by reducing the instrument from 20 to 10 variables. This reduction does not significantly affect the predictive power of segment attribution on three dependent variables, which suggests that many segmentation analyses could be similarly optimized, helping researchers save survey time and standardize segmentation analyses more.