Psychometric problems and issues involved with creating and using ipsative measures for selection

Data are described as ipsative if a given set of responses always sum to the same total. However, there are many properties of data collection that can give rise to different types of ipsative data. In this study, the most common type of ipsative data used in employee selection (forced-choice ipsative data; FCID) is discussed as a special case of other types of ipsative data. Although all ipsative data contains constraints on covariance matrices (covariance-level interdependence), FCID contains additional item-level interdependencies as well. The psychological processes that give rise to FCID and the resultant psychometric properties are discussed. In addition, data from which both normative and ipsative responses were provided by job applicants illustrate very different patterns of correlations as well as very different selection decisions between normative, FCID and ipsatized measures.
Mathematically speaking, data are ipsative if a given set of responses always sum to the same total. Problems associated with ipsative data were originally recognized some time ago by Cattell [...]