Individual differences in reading comprehension have often been explored using latent variable modeling (LVM), to assess the relative contribution of domain-general and domain-specific cognitive abilities. However, LVM is based on the assumption that the observed covariance among indicators of a construct is due to a common cause (i.e., a latent variable; Pearl, 2000). This is a questionable assumption when the indicator variables are measures of performance on complex cognitive tasks. According to Process Overlap Theory (POT; Kovacs & Conway, 2016), multiple processes are involved in cognitive task performance and the covariance among tasks is due to the overlap of processes across tasks. Instead of a single latent common cause, there are thought to be multiple dynamic manifest causes, consistent with an emerging view in psychometrics called network theory (Barabási, 2012; Borsboom & Cramer, 2013). In the current study, we reanalyzed data from Freed et al. (2017) and compared two modeling approaches: LVM (Study 1) and psychometric network modeling (Study 2). In Study 1, two exploratory LVMs demonstrated problems with the original measurement model proposed by Freed et al. Specifically, the model failed to achieve discriminant and convergent validity with respect to reading comprehension, language experience, and reasoning. In Study 2, two network models confirmed the problems found in Study 1, and also served as an example of how network modeling techniques can be used to study individual differences. In conclusion, more research, and a more informed approach to psychometric modeling, is needed to better understand individual differences in reading comprehension.

经常使用潜在变量建模（LVM）探索阅读理解中的个体差异，以评估领域一般和领域特定认知能力的相对贡献。但是，LVM基于这样一个假设，即所观察到的结构指标之间的协方差是由共同原因引起的（即，潜在变量； Pearl，2000年）。当指标变量是衡量复杂认知任务绩效的指标时，这是一个有疑问的假设。根据过程重叠理论（POT; Kovacs＆Conway，2016），认知任务绩效涉及多个过程，而任务之间的协方差是由于跨任务的过程重叠。人们认为不是多个潜在的常见原因，而是多个动态的明显原因，与心理测量学中一种新兴的观点-网络理论相一致（Barabási，2012； Borsboom＆Cramer，2013）。在当前的研究中，我们重新分析了Freed等人的数据。（2017）并比较了两种建模方法：LVM（研究1）和心理测量网络建模（研究2）。在研究1中，两个探索性LVM证明了Freed等人提出的原始测量模型存在的问题。具体而言，该模型在阅读理解，语言经验和推理方面未能实现判别和收敛的有效性。在研究2中，两个网络模型确认了在研究1中发现的问题，并且还充当了如何使用网络建模技术研究个体差异的示例。总之，更多的研究和更明智的心理计量学建模方法，