Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample.
Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample.
复制标题
了解你的人群，了解你的模型：使用基于模型的回归和后分层来概括观察样本之外的发现。
DOI：
10.1037/met0000362
复制
发表时间：
2021-10
影响因子：
7
通讯作者：
Gelman, Andrew
中科院分区：
文献类型：
--
作者：
Kennedy, Lauren;Gelman, Andrew
心理学研究通常集中在相互作用上，这对来自非代表性样本的推论具有深远的影响。为了估算平均治疗效果的目的，我们建议拟合一个模型，允许治疗与背景变量相互作用，然后在人群中这些变量的分布平均值。这可以看作是多层次回归和延伸后（MRP）的扩展，这是一种政治学和其他调查研究领域的方法，研究人员希望从稀疏和可能的非代表性样本中概括为一般人群。在本文中，我们讨论可以在心理科学中使用此方法的领域。我们使用我们的方法使用开源数据来估算五巨头人格量表的规范分布。我们认为，诸如此类的大型开放数据源和其他协作数据源可能会与MRP结合使用，以帮助解决当前心理学中概括性和复制的挑战。
Psychology research often focuses on interactions, and this has deep implications for inference from non-representative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This can be seen as an extension of multilevel regression and poststratification (MRP), a method used in political science and other areas of survey research, where researchers wish to generalize from a sparse and possibly non-representative sample to the general population. In this paper, we discuss areas where this method can be used in the psychological sciences. We use our method to estimate the norming distribution for the Big Five Personality Scale using open source data. We argue that large open data sources like this and other collaborative data sources can potentially be combined with MRP to help resolve current challenges of generalizability and replication in psychology.