ABSTRACT

Although the approach to correct for measurement errors in research has been known since the early 1970s, most researchers in the social sciences appear to ignore these recommendations. One possible reason is that the models with latent variables that were originally suggested may be too difficult to apply in practice. We suggest an alternative but simpler procedure to correct for measurement errors. If one knows the sizes of the random errors and method effects for the different measures, the correlation and covariance matrices for the observed variables can be corrected. This approach has already been shown for simple concepts measured by a single measure. In this paper, we show that this approach can be generalized to studies that use a mixture of simple and complex concepts measured by several questions. Using these corrected matrices as the basis for the estimation, the parameters of the models are automatically corrected for measurement errors. The conditions for the quality of these procedures will also be discussed.

摘要

尽管自 1970 年代初期以来，人们就已经知道在研究中纠正测量误差的方法，但大多数社会科学研究人员似乎忽略了这些建议。一个可能的原因是最初建议的带有潜在变量的模型可能太难在实践中应用。我们建议采用替代但更简单的程序来纠正测量误差。如果知道不同度量的随机误差和方法效应的大小，则可以校正观测变量的相关性和协方差矩阵。这种方法已经针对由单个度量度量的简单概念进行了展示。在本文中，我们表明这种方法可以推广到使用由几个问题衡量的简单和复杂概念的混合的研究。使用这些校正后的矩阵作为估计的基础，模型参数会自动校正测量误差。还将讨论这些程序的质量条件。