New items are often evaluated prior to their operational use to obtain item response theory (IRT) item parameter estimates for quality control purposes. Fixed parameter calibration is one linking method that is widely used to estimate parameters for new items and place them on the desired scale. This article provides detailed descriptions of two fixed parameter calibration methods for the bifactor model and compares their relative performance through simulation. The two methods, which were natural generalizations of their counterparts in the unidimensional context, are the one prior weights updating and multiple expectation‐maximization (EM) cycles (OWU‐MEM) and multiple prior weights updating and multiple EM cycles (MWU‐MEM) methods. In addition, for comparison purposes, the separate calibration method with Haebara linking was included in the simulation. In general, the MWU‐MEM method recovered item parameters well for both equivalent and nonequivalent groups, whereas the OWU‐MEM method worked well only for equivalent groups. With a few exceptions, the MWU‐MEM and Haebara methods showed comparable item parameter recovery.

中文翻译：

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双因子模型的两种IRT固定参数校准方法

通常在对新项目进行操作使用之前对其进行评估，以获得用于质量控制目的的项目响应理论（IRT）项目参数估计。固定参数校准是一种链接方法，广泛用于估算新项目的参数并将其放置在所需的比例上。本文提供了双因子模型的两种固定参数校准方法的详细说明，并通过仿真比较了它们的相对性能。这两种方法是一维上下文中对等物的自然归纳，分别是一个先验权重更新和多个期望最大化（EM）周期（OWU-MEM）和多个先验权重更新和多个EM周期（MWU-MEM）方法。此外，出于比较目的，仿真中包括了具有Haebara链接的单独校准方法。通常，MWU‐MEM方法对于等效和非等效组都能很好地恢复项目参数，而OWU‐MEM方法仅对等效组有效。除少数例外，MWU-MEM和Haebara方法显示出可比的项目参数恢复。