Investigating the Normativity of Trait Estimates from Multidimensional Forced-Choice Data. (2nd January 2023)

• Record Type: Journal Article
• Authors: Frick, Susanne; Brown, Anna; Wetzel, Eunike
• Is Part Of: Multivariate behavioral research. Volume 58:Number 1(2023)
• Page Start: 1
• Page End: 29
• Publication Date: 2023-01-02
• Subjects: Forced-choice format -- Thurstonian IRT model -- ipsative data -- true-false -- rating scale; Psychometrics -- Periodicals; Psychology, Experimental -- Periodicals; Psychology, Experimental; Psychometrics; Periodicals; 150.15195
• DOI: 10.1080/00273171.2021.1938960
• Languages: English
• ISSNs: 0027-3171

Abstract

Abstract: The Thurstonian item response model (Thurstonian IRT model) allows deriving normative trait estimates from multidimensional forced-choice (MFC) data. In the MFC format, persons must rank-order items that measure different attributes according to how well the items describe them. This study evaluated the normativity of Thurstonian IRT trait estimates both in a simulation and empirically. The simulation investigated normativity and compared Thurstonian IRT trait estimates to those using classical partially ipsative scoring, from dichotomous true-false (TF) data and rating scale data. The results showed that, with blocks of opposite keyed items, Thurstonian IRT trait estimates were normative in contrast to classical partially ipsative estimates. Unbalanced numbers of items per trait, few opposite keyed items, traits correlated positively or assessing fewer traits did not decrease measurement precision markedly. Measurement precision was lower than that of rating scale data. The empirical study investigated whether relative MFC responses provide a better differentiation of behaviors within persons than absolute TF responses. However, criterion validity was equal and construct validity (with constructs measured by rating scales) lower in MFC. Thus, Thurstonian IRT modeling of MFC data overcomes the drawbacks of classical scoring, but gains in validity may depend on eliminating common method biases from the comparison.