ABSTRACT

While in recent years a number of new statistical approaches have been proposed to model group differences with a different assumption on the nature of the measurement invariance of the instruments, the tools for detecting local misspecifications of these models have not been fully developed yet. In this study, we present a novel approach using a Deep Neural Network (DNN). We compared the proposed model with the most popular traditional methods: Modification Indices (MI) and Expected Parameter Change (EPC) indicators from the Confirmatory Factor Analysis (CFA) modeling, logistic DIF detection, and sequential procedure introduced with the CFA alignment approach. Simulation studies show that the proposed method outperformed traditional methods in almost all scenarios, or it was at least as accurate as the best one. We also provide an empirical example utilizing European Social Survey data including items known to be miss-translated, which are correctly identified with presented DNN approach.

摘要

虽然近年来已经提出了许多新的统计方法来模拟组差异，但对仪器测量不变性的性质有不同的假设，但用于检测这些模型的局部错误指定的工具尚未完全开发。在这项研究中，我们提出了一种使用深度神经网络 (DNN) 的新方法。我们将所提出的模型与最流行的传统方法进行了比较：验证性因子分析 (CFA) 建模中的修正指数 (MI) 和预期参数变化 (EPC) 指标、逻辑 DIF 检测以及通过 CFA 对齐方法引入的顺序程序。仿真研究表明，所提出的方法在几乎所有场景中都优于传统方法，或者至少与最好的方法一样准确。