Identification of Influential Cases in Structural Equation Models Using the Jackknife Method
提出用刀切法识别结构方程模型中的有影响个案，这些个案可能影响整体拟合或特定参数，并通过模拟和实证数据验证。
Relatively little attention has been given to detecting influential cases (ICs) when estimating structural equation models (SEMs). Most techniques examine individual cases using covariance-based techniques such as the Mahalanobis distance, which examine the distributional characteristics of the cases but ignore the model. Cases identified using such model-free techniques are usually referred to as out-liers. In SEM, however, the model is of central importance. The characteristics of the model (number of latent variables, etc.) have an effect on which cases are influential. The authors propose applying the well-known jackknife procedure to detect model-based ICs, which may be influential with respect to overall fit, particular model parameters, or both. The procedure is illustrated by two studies—one using simulated data, the other empirical data.