Hayduk Leslie A
Department of Sociology, University of Alberta, Edmonton, Canada.
BMC Med Res Methodol. 2014 Nov 27;14:124. doi: 10.1186/1471-2288-14-124.
Inappropriate and unacceptable disregard for structural equation model (SEM) testing can be traced back to: factor-analytic inattention to model testing, misapplication of the Wilkinson task force's [Am Psychol 54:594-604, 1999] critique of tests, exaggeration of test biases, and uncomfortably-numerous model failures.
The arguments for disregarding structural equation model testing are reviewed and found to be misguided or flawed. The fundamental test-supporting observations are: a) that the null hypothesis of the χ2 structural equation model test is not nil, but notable because it contains substantive theory claims and consequences; and b) that the amount of covariance ill fit cannot be trusted to report the seriousness of model misspecifications. All covariance-based fit indices risk failing to expose model problems because the extent of model misspecification does not reliably correspond to the magnitude of covariance ill fit - seriously causally misspecified models can fit, or almost fit.
The only reasonable research response to evidence of non-chance structural equation model failure is to diagnostically investigate the reasons for failure. Unfortunately, many SEM-based theories and measurement scales will require reassessment if we are to clear the backlogged consequences of previous deficient model testing. Fortunately, it will be easier for researchers to respect evidence pointing toward required reassessments, than to suffer manuscript rejection and shame for disrespecting evidence potentially signaling serious model misspecifications.
对结构方程模型(SEM)检验的不当且不可接受的忽视可追溯到以下几点:在因子分析中对模型检验的忽视、对威尔金森特别工作组[《美国心理学家》54:594 - 604,1999]对检验的批评的错误应用、对检验偏差的夸大以及数量多得令人不安的模型失败情况。
对忽视结构方程模型检验的论点进行了回顾,发现这些论点具有误导性或存在缺陷。支持检验的基本观察结果如下:a)χ²结构方程模型检验的零假设并非毫无意义,而是值得注意的,因为它包含了实质性的理论主张和结果;b)不能相信协方差拟合不佳的程度能反映模型设定错误的严重程度。所有基于协方差的拟合指数都有可能无法揭示模型问题,因为模型设定错误的程度与协方差拟合不佳的程度并不能可靠地对应——严重因果设定错误的模型可能拟合良好,或几乎拟合良好。
对于非偶然的结构方程模型失败的证据,唯一合理的研究回应是对失败原因进行诊断性调查。不幸的是,如果我们要清理先前模型检验不足所积压的后果,许多基于结构方程模型的理论和测量量表都需要重新评估。幸运的是,研究人员尊重指向所需重新评估的证据要比因忽视可能表明严重模型设定错误的证据而遭受稿件被拒和蒙羞更容易。