  A strong tool for the selection of items that share a common trait from a set
of given items is proposed. The selection method is based on marginal estimates
and exploits that the estimates of the standard deviation of the mixing
distribution are rather stable if items are from a Rasch model with a common
trait. If, however, the item set is increased by adding items that do not share
the latent trait the estimated standard deviations become distinctly smaller. A
method is proposed that successively increases the set of items that are
considered Rasch items by examining the estimated standard deviations of the
mixing distribution. It is demonstrated that the selection procedure is on
average very reliable and a criterion is proposed, which allows to identify
items that should not be considered Rasch items for concrete item sets. An
extension of the method allows to investigate which groups of items might share
a common trait. The corresponding hierarchical clustering procedure is
considered an exploratory tool but works well on average.
