Revuelta Javier, Kessel Dominique
Universidad Autónoma de Madrid.
Psicothema. 2007 May;19(2):322-8.
Testing model fit for latent structure models (latent trait models and latent class models) is difficult because of the lack of goodness-of-fit statistics with a known distribution. This paper describes the application of the pi* goodness-of-fit statistic to latent structure models. The statistic pi* is based on the concept of latent classes and has a natural interpretation when applied to these models. This statistic assumes that the population is made up of several classes that follow a parametric model, and a residual class outside the model. The value of pi* is the population proportion in the residual class. This paper describes the estimation algorithms of pi* for latent trait and latent class models and an empirical example with a scale of study habits. There are two latent classes in these data: bad and regular students, which are related to the student s responsibility.
由于缺乏具有已知分布的拟合优度统计量,对潜在结构模型(潜在特质模型和潜在类别模型)进行模型拟合检验很困难。本文描述了π拟合优度统计量在潜在结构模型中的应用。统计量π基于潜在类别的概念,应用于这些模型时具有自然的解释。该统计量假设总体由遵循参数模型的几个类别以及模型之外的一个残差类别组成。π的值是残差类别中的总体比例。本文描述了潜在特质和潜在类别模型的π估计算法以及一个关于学习习惯量表的实证例子。这些数据中有两个潜在类别:差学生和好学生,它们与学生的责任感有关。