Behavior Research Methods vol:44 issue:2 pages:532-545
In many areas of the behavioral sciences, different groups of objects are measured on the same set of binary variables, resulting in coupled binary object by variable data blocks. Take as an example, success/failure scores for different samples of testees, each sample belonging to a different country, regarding a set of test items. When dealing with such data, a key challenge consists of uncovering the differences and similarities between the structural mechanisms that underlie the different blocks. To tackle this challenge for the case of a single data block, one may rely on HICLAS in which the variables are reduced to a limited set of binary bundles, which represent the underlying structural mechanisms, and the objects are given a score on these bundles. In case of multiple binary data blocks, one may perform HICLAS to each data block separately. However, such an analysis strategy obscures the similarities and, in case of many data blocks, also the differences between the blocks. To resolve this, we propose the new Clusterwise HICLAS generic modeling strategy. In this strategy, the different data blocks are assumed to form a set of mutually exclusive clusters. For each cluster, different bundles are derived. As such, blocks belonging to the same cluster have the same bundles, whereas blocks of different clusters are modeled with different bundles. Further, the performance of Clusterwise HICLAS is evaluated by means of an extensive simulation study, and by applying the strategy to coupled binary data regarding emotion differentiation and regulation.