Lack of Theory Building and Testing Impedes Progress in The Factor and Network Literature
Lack of Theory Building and Testing Impedes Progress in The Factor and Network Literature
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DOI：
10.1080/1047840x.2020.1853461
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发表时间：
2020-10-01
影响因子：
9.3
通讯作者：
Fried, Eiko I.
中科院分区：
文献类型：
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作者：
Fried, Eiko I.
使用因素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，福尔斯在三个方面缺乏理论构建和测试。首先，统计和理论模型被混为一谈，导致无效的推论，如存在的心理结构的基础上因素模型，或建议的基础上网络模型的临床干预。我在一个模拟中展示了这种推理差距：无论数据的质量或数量如何，优秀的模型拟合对证实一个理论几乎没有帮助。第二，研究者未能阐明有关心理结构的理论，而是使用内隐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，明确的理论往往是薄弱的理论：不精确的描述容易受到隐藏的假设和未知数的影响。这样的理论不能提供精确的预测，而且通常不清楚统计效应是否真的证实了弱理论。我证明了这三个挑战是共同的和有害的，并阻碍理论的形成，失败和改革。匹配的理论和统计模型是必要的，使数据承担理论，并重新关注理论心理学和形式化的理论提供了一条前进的道路。
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.