Applying Item Response Trees to Personality Data in the Selection Context
研究发现，在人员选拔中使用的七种自评人格量表上，项目反应树模型比传统单决策模型拟合更好，且其中方向决策过程的潜变量对工作绩效的预测力更强。
Self-report personality scales are used frequently in personnel selection. Traditionally, researchers have assumed that individuals respond to items within these scales using a single-decision process. More recently, a flexible set of item response (IR) tree models have been developed that allow researchers to investigate multiple-decision processes. In the present research, we found that IR tree models fit the data better than a single-decision IR model when fitted to seven self-report personality scales used in a concurrent criterion-related validity study. In addition, we found evidence that the latent variable underlying the direction of a response (agree or disagree) decision process predicted job performance better than latent variables reflecting the other decision processes for the best fitting IR tree model.