This study presents the situational optimization function analysis (SOFA) and has three aims. First, to develop a Bayesian implementation of SOFA. Second, to compare this implementation with three other maximum likelihood-based models in their accuracy to estimate true scores. The third aim is to show how joint modeling can be used for validity research. A simulation study was used to examine the second aim, while an empirical example was used to illustrate the third aim. The simulation study used three data generating processes, with varying degrees of deviation from linear models and with different sample sizes. Results of the simulation study showed that the Bayesian implementation supersedes the other models. In the empirical example, data collected from 66 participants using an iterated prisoner dilemma and a scale measuring cooperation-competition attitudes were used. Results showed that joint modeling is the best fitting model, also increasing the correlation between the true scores of both measures (deviations from the iterated prisoner dilemma and the scale). Finally, implications, limitations and future studies are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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情境优化函数分析：基于 Lewin 方程的理想性能分析。

本研究提出了情境优化函数分析 (SOFA)，并具有三个目标。首先，开发 SOFA 的贝叶斯实现。其次，将此实现与其他三个基于最大似然模型的准确性进行比较，以估计真实分数。第三个目标是展示如何将联合建模用于有效性研究。模拟研究用于检查第二个目标，而经验示例用于说明第三个目标。模拟研究使用了三种数据生成过程，与线性模型的偏差程度不同，样本量也不同。模拟研究的结果表明，贝叶斯实施取代了其他模型。在实证例子中，使用重复囚徒困境和衡量合作-竞争态度的量表从 66 名参与者收集的数据。结果表明，联合建模是最合适的模型，也增加了两种测量的真实分数（与迭代囚徒困境和量表的偏差）之间的相关性。最后，讨论了影响、局限性和未来的研究。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）