Situational judgment tests have gained popularity in educational and psychological measurement and are widely used in personnel assessment. A situational judgment item presents a hypothetical scenario and a list of actions, and the individuals are asked to select their most likely action for that scenario. Because actions have no explicit order, the item generates nominal responses consisting of the actions selected by the individuals. This article shows how to factor-analyze the nominal responses originated from such a test, including the estimation of the number of latent factors and a factor invariance analysis in a multiple group design. The method consists of applying the MNCM, a multidimensional extension of the nominal categories model by Bock. The article includes the results of two studies: (1) a simulation study about Type-I error rate, statistical power, and recovery of the parameters in a multigroup factorial invariance design and (2) a real data example using responses to a situational judgment test measuring gender stereotypes to illustrate the approach. Results suggest the use of the Akaike information criterion, Bayesian information criterion, and corrected Bayesian information criterion indices to guide the selection of the number of factors with nominal responses. All the analyses are conducted using the computer program Mplus. The code is included as Supplemental Material (available online) for the readers so that they can adapt it to their own purposes.

情境判断测验在教育和心理测量中得到普及，并广泛用于人员评估。情境判断项目提出了一个假设场景和一系列行动，要求个人为该场景选择他们最可能采取的行动。因为动作没有明确的顺序，所以该项目生成由个人选择的动作组成的名义响应。本文展示了如何对源自此类测试的标称响应进行因子分析，包括多组设计中潜在因子数量的估计和因子不变性分析。该方法包括应用 MNCM，这是 Bock 对名义类别模型的多维扩展。文章包括两项研究的结果：（1）关于 Type-I 错误率的模拟研究，多组因子不变性设计中的统计功效和参数恢复，以及 (2) 使用对测量性别刻板印象的情境判断测试的响应的真实数据示例来说明该方法。结果建议使用 Akaike 信息准则、贝叶斯信息准则和修正的贝叶斯信息准则指数来指导选择具有标称响应的因子数。所有的分析都是使用计算机程序进行的 并修正了贝叶斯信息标准指数，以指导选择具有名义响应的因素数量。所有的分析都是使用计算机程序进行的 并修正了贝叶斯信息标准指数，以指导选择具有名义响应的因素数量。所有的分析都是使用计算机程序进行的加号。该代码作为补充材料（在线提供）提供给读者，以便他们可以根据自己的目的对其进行调整。