Abstract The applied social science literature using factor and network models continues to grow rapidly. Most work reads like an exercise in model fitting, and falls short of theory building and testing in three ways. First, statistical and theoretical models are conflated, leading to invalid inferences such as the existence of psychological constructs based on factor models, or recommendations for clinical interventions based on network models. I demonstrate this inferential gap in a simulation: excellent model fit does little to corroborate a theory, regardless of quality or quantity of data. Second, researchers fail to explicate theories about psychological constructs, but use implicit causal beliefs to guide inferences. These latent theories have led to problematic best practices. Third, explicated theories are often weak theories: imprecise descriptions vulnerable to hidden assumptions and unknowns. Such theories do not offer precise predictions, and it is often unclear whether statistical effects actually corroborate weak theories or not. I demonstrate that these three challenges are common and harmful, and impede theory formation, failure, and reform. Matching theoretical and statistical models is necessary to bring data to bear on theories, and a renewed focus on theoretical psychology and formalizing theories offers a way forward.

中文翻译：

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缺乏理论构建和测试阻碍了因子和网络文献的进展

摘要 使用因子和网络模型的应用社会科学文献继续快速增长。大多数工作读起来像是模型拟合的练习，并且在三个方面都缺乏理论构建和测试。首先，统计模型和理论模型混为一谈，导致无效推论，例如基于因素模型的心理结构的存在，或基于网络模型的临床干预建议。我在模拟中证明了这种推理差距：无论数据的质量或数量如何，出色的模型拟合都无法证实理论。其次，研究人员未能解释有关心理结构的理论，而是使用隐含的因果信念来指导推理。这些潜在的理论导致了有问题的最佳实践。第三，被解释的理论往往是弱理论：不精确的描述容易受到隐藏假设和未知因素的影响。这些理论不能提供精确的预测，而且通常不清楚统计效果是否确实证实了弱理论。我论证了这三个挑战是共同的和有害的，并且阻碍了理论的形成、失败和改革。匹配理论和统计模型对于将数据用于理论是必要的，重新关注理论心理学和形式化理论提供了前进的道路。