Correction to: Analyzing ipsative data in psychological research

The article Some Mathematical Concepts of the Analytic Hierarchy Process, written by Thomas L. Saaty, was originally published Online First without Open Access.

An empirical study of the career anchors that govern career decisions

Purpose – The purpose of this paper is to examine two of Feldman and Bolino's proposals: career anchor plurality and career anchor relationships. Design/methodology/approach – A novel method for examining the relationships between career anchors called “indices of mutual presence” is developed for this study to generate meaningful results from ordinal and ipsative career anchor data. Findings – Evidence for some individuals having multiple career anchors was found. Complementary and exclusivity career anchor relationships are identified and a model for representing them is presented. The importance and possible benefit of understanding both an individual's preferred and “unpreferred” anchors is discussed. The non-reflexive nature of career anchors is explored and the idea of “mutually” exclusive career anchors is rejected. Weaknesses in the octagon shaped career anchor relationships diagram presented by Feldman and Bolino are discussed. Research limitations/implications – Despite the benefits associated with forced-choice assessments, some have expressed concern because of the nature of this type of evaluation. Each time an item is preferred, another item must be “unpreferred.” Thus, for one item to have a high preference count, some other item must necessarily have lower preference counts. The resulting data is ordinal rather than interval or ratio. It contains information regarding order of preference, but provides little insight into magnitude of preference. This makes it difficult to identify and examine how much more or less one individual prefers an item when compared to another individual. Originality/value – The second property of forced-choice data that raises concern is its ipsative nature. As respondents are constrained to unprefer an item each time the prefer one, the total preference counts remain the same for every individual. As a result, the preference scores for every individual will always sum to the same value. When data has this property, it is called ipsative. Ipsative data is not free to vary, and thus statistical methods which analyze variance may yield spurious results. Thus, traditional factorial statistical methods cannot be appropriately used with ipsative data (Baron, 1996; Bartram, 1996; Closs, 1996). It is commonly believed that researchers trade ease of use and accuracy for fewer available statistical tools when using forced-choice methods. However, this paper attempts to use “indices of mutual presence” developed for this study (described below) that do not rely on variance to generate meaningful results from ipsative career anchor data.

Vocational Students' Learning Preferences: The Interpretability of Ipsative Data

A number of researchers have argued that ipsative data are not suitable for statistical procedures designed for normative data. Others have argued that the interpretability of such analyses of ipsative data are little affected where the number of variables and the sample size are sufficiently large. The research reported here represents a factor analysis of the scores on the Canfield Learning Styles Inventory for 1,252 students in vocational education. The results of the factor analysis of these ipsative data were examined in a context of existing theory and research on vocational students and lend support to the argument that the factor analysis of ipsative data can provide sensibly interpretable results.