Li Mengtong, Sun Tianjun, Zhang Bo
Department of Psychology, University of Illinois at Urbana-Champaign, IL, USA.
Department of Psychological Sciences, Kansas State University, KS, USA.
Appl Psychol Meas. 2022 Jan;46(1):70-72. doi: 10.1177/01466216211051726. Epub 2021 Oct 8.
Recently, there has been increasing interest in adopting the forced-choice (FC) test format in non-cognitive assessments, as it demonstrates faking resistance when well-designed. However, traditional or manual pairing approaches to FC test construction are time- and effort- intensive and often involve insufficient considerations. To address these issues, we developed the new open-source R package to facilitate automated and optimized item pairing strategies. The package is intended as a practical tool for FC test constructions. Users can easily obtain automatically optimized FC tests by simply inputting the item characteristics of interest. Customizations are also available for considerations on matching rules and the behaviors of the optimization process. The package should be of interest to researchers and practitioners constructing FC scales with potentially many metrics to match on and/or many items to pair, essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods.
最近，在非认知评估中采用强制选择（FC）测试形式的兴趣日益浓厚，因为精心设计的FC测试形式具有抗伪装能力。然而，传统的或手动的FC测试构建配对方法既耗时又费力，而且往往考虑不充分。为了解决这些问题，我们开发了新的开源R包，以促进自动化和优化的项目配对策略。该包旨在作为FC测试构建的实用工具。用户只需输入感兴趣的项目特征，即可轻松获得自动优化的FC测试。还可以进行定制，以考虑匹配规则和优化过程的行为。对于构建具有潜在许多要匹配的指标和/或许多要配对的项目的FC量表的研究人员和从业者来说，该包应该会很有用，基本上免除了用户手动进行项目配对的负担，并降低了简单排序方法带来的计算成本和偏差。
