In high stakes assessments of personality and similar attributes, test takers may engage in impression management (aka faking). This article proposes to consider responses of every test taker as a potential mixture of “real” (or retrieved) answers to questions, and “ideal” answers intended to create a desired impression, with each type of response characterized by its own distribution and factor structure. Depending on the particular mix of response types in the test taker profile, grades of membership in the “real” and “ideal” profiles are defined. This approach overcomes the limitation of existing psychometric models that assume faking behavior to be consistent across test items. To estimate the proposed faking-as-grade-of-membership (F-GoM) model, two-level factor mixture analysis is used, with two latent classes at the response (within) level, allowing grade of membership in “real” and “ideal” profiles, each underpinned by its own factor structure, at the person (between) level. For collected data, units of analysis can be item or scale scores, with the latter enabling analysis of questionnaires with many measured scales. The performance of the F-GoM model is evaluated in a simulation study, and compared against existing methods for statistical control of faking in an empirical application using archival recruitment data, which supported the validity of latent factors and classes assumed by the model using multiple control variables. The proposed approach is particularly useful for high-stakes assessment data and can be implemented with standard software packages.

中文翻译：

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间歇性伪造高风险评估中的人格特征：会员分析等级。

在对人格和类似属性的高风险评估中，应试者可能会进行印象管理（又名伪装）。本文建议将每个应试者的回答视为问题的“真实”（或检索到）答案和旨在创造所需印象的“理想”答案的潜在混合体，每种类型的回答都有其自身的分布和因素结构体。根据应试者资料中的特定反应类型组合，会员等级在“真实”和“理想”配置文件中定义。这种方法克服了现有心理测量模型的局限性，这些模型假设伪造行为在测试项目中是一致的。为了估计拟议的伪造会员等级 (F-GoM) 模型，使用了两级因子混合分析，在响应（内部）级别有两个潜在类别，允许会员等级为“真实”和“理想”概况，在人（之间）层面，每个概况都由其自身的因素结构支撑。对于收集的数据，分析单位可以是项目分数或量表分数，后者可以分析具有许多测量量表的问卷。F-GoM 模型的性能在模拟研究中进行了评估，并与使用档案招聘数据的实证应用中现有的造假统计控制方法进行了比较，它支持模型使用多个控制变量假设的潜在因素和类别的有效性。所提出的方法对于高风险评估数据特别有用，并且可以使用标准软件包来实施。