Predicting variability: Using multilevel modelling to assess differences in variance

摘要

Researchers are sometimes interested in the variability of data rather than in their absolute or relative values. An important example of such a situation in social psychology is consensus: the fact that people are more similar to each other in certain conditions. Currently, methods to assess differences in consensus (or variability in general) are not very well-developed or widely known. We describe an existing tool that allows testing the extent to which variability depends on one or several predictor variables. We explain how multilevel modelling's capacity to model heterogeneity in residual variance can be used to test substantive hypotheses about differences in variance. We illustrate the procedure and warn against potential misuses of multilevel modelling to analyse differences in variability. We also provide MLwiN and SAS PROC MIXED syntax necessary to run this kind of analyses. In some specific, simple cases, SPSS MIXED can also be used. Copyright (c) 2014 John Wiley & Sons, Ltd.