Although the use of ideal point item response theory (IRT) models for organizational research has increased over the last decade, the assessment of construct dimensionality of ideal point scales has been overlooked in previous research. In this study, we developed and evaluated dimensionality assessment methods for an ideal point IRT model under the Bayesian framework. We applied the posterior predictive model checking (PPMC) approach to the most widely used ideal point IRT model, the generalized graded unfolding model (GGUM). We conducted a Monte Carlo simulation to compare the performance of item pair discrepancy statistics and to evaluate the Type I error and power rates of the methods. The simulation results indicated that the Bayesian dimensionality detection method controlled Type I errors reasonably well across the conditions. In addition, the proposed method showed better performance than existing methods, yielding acceptable power when 20% of the items were generated from the secondary dimension. Organizational implications and limitations of the study are further discussed.

尽管在过去十年中，理想点项目反应理论 (IRT) 模型在组织研究中的使用有所增加，但在先前的研究中，对理想点量表的构造维度的评估却被忽视了。在本研究中，我们开发并评估了贝叶斯框架下理想点 IRT 模型的维度评估方法。我们将后验预测模型检查 (PPMC) 方法应用于最广泛使用的理想点 IRT 模型，即广义分级展开模型 (GGUM)。我们进行了蒙特卡罗模拟，以比较项目对差异统计的性能并评估方法的 I 类错误和功率率。仿真结果表明，贝叶斯维数检测方法在各种条件下都相当好地控制了 I 类错误。此外，所提出的方法比现有方法表现出更好的性能，当 20% 的项目是从二次维度生成时，产生可接受的功率。进一步讨论了该研究的组织影响和局限性。