Assessing Dimensionality of the Ideal Point Item Response Theory Model Using Posterior Predictive Model Checking
研究了在贝叶斯框架下，如何用后验预测模型检验方法评估理想点项目反应理论模型的维度性，通过蒙特卡洛模拟比较了不同方法的统计表现，发现新方法能有效控制第一类错误并具有较高统计检验力。
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.