autoFC

Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 doi:10.1037/apl0000414). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 doi:10.1177/1094428119836486). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 doi:10.31234/osf.io/hmnrc). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 doi:10.1177/0013164410375112) or minimize item location differences (Cao & Drasgow, 2019 doi:10.1037/apl0000414) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests (Li et al., 2022 doi:10.1177/01466216211051726), essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2024 doi:10.1177/10944281241229784). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 doi:10.1177/0013164410375112) and predict trait scores of simulated/actual respondents based on an estimated model.

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