A Lognormal Ipsative Model for Multidimensional Compositional Items

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Photo of Postdoctoral Research Fellow Chia-Wen Chen.

Postdoctoral Research Fellow Chia-Wen Chen.

Photo: Øysten Andresen/UiO.

Compositional items – a form of forced-choice items – require respondents to allocate a fixed total number of points to a set of statements. To describe the responses to these items, the Thurstonian item response theory (IRT) model was developed. Despite its prominence, the model requires that items composed of parts of statements result in a factor loading matrix with full rank. Without this requirement, the model cannot be identified, and the latent trait estimates would be seriously biased. Besides, the estimation of the Thurstonian IRT model often results in convergence problems. To address these issues, this study developed a new version of the Thurstonian IRT model for analyzing compositional items – the lognormal ipsative model (LIM) – that would be sufficient for tests using items with all statements positively phrased and with equal factor loadings. We developed an online value test following Schwartz’s values theory using compositional items and collected response data from a sample size of N = 512 participants with ages from 13 to 51 years. The results showed that our LIM had an acceptable fit to the data, and that the reliabilities exceeded 0.85. A simulation study resulted in good parameter recovery, high convergence rate, and the sufficient precision of estimation in the various conditions of covariance matrices between traits, test lengths and sample sizes. Overall, our results indicate that the proposed model can overcome the problems of the Thurstonian IRT model when all statements are positively phrased and factor loadings are similar.

Chen, Chia-Wen; Wang, Wen-Chung; Mok, Magdalena Mo Ching & Scherer, Ronny (2021). A Lognormal Ipsative Model for Multidimensional Compositional Items.. Frontiers in Psychology.  ISSN 1664-1078.  12, s 1- 19 . doi: 10.3389/fpsyg.2021.573252 Full text in Research Archive.

Published Oct. 25, 2021 10:56 AM - Last modified Oct. 25, 2021 10:56 AM