Panel Data Analysis in Public Administration: Substantive and Statistical Considerations

Panel data analysis has become a popular tool for researchers in public policy and public administration. Combining information from both spatial and temporal dimensions, panel data allow researchers to use repeated observations of the same units (e.g., government agencies, public organizations, public managers, etc.), and could increase both quantity and quality of the empirical information. Nonetheless, practices of choosing different panel model specifications are not always guided by substantive considerations. Using a state-level panel data set related to public health administration as an example, I compare four categories of panel model specifications: (1) the fixed effects model, (2) the random effects model, (3) the random coefficients (heterogeneous parameter) model, and (4) linear dynamic models. I provide an overview of the substantive consideration relevant to different statistical specifications. Furthermore, I compare estimation results and discuss how these different model choices may lead to different substantive interpretations. Based on model comparisons, I demonstrate several potential problems of different panel models. I conclude with a discussion on how to choose among different models based on substantive and theoretical considerations. 《面板数据分析方法在公共行政管理学中的应用:理论及数理统计的考量》 作者:朱凌 面板数据分析 (Panel Data Analysis) 是公共政策和行政管理研究领域中广为应用的研究方法。面板数据在时间序列上截取多个跨地域的个截面,在这些不同截面上选取样本观测值汇总成样本数据。研究者可以运用面板数据统计同一组截面不同时间点的数据。这样的数据结构有助于增加数据的维度和信息量。然而,有多种计量统计模型适用于面板数据的分析。研究者在选用不同面板数据模型的时候需考量统计理论和实际问题的适用性。在本文中,我以美国五十个州关于公共健康管理的面板数据为例,比较四类面板数据分析的计量模型。这四类模型包括:(1)固定效应模型(fixed effects model)、(2)随机效应模型(random effects model)、(3)随机参数模型 (random coefficients model)和(4)线性动态模型(linear dynamic model)。首先,我综述这些不同计量模型所反映的不同理论基础。其次,我比较分析适用于不同面板数据模型的实际含义解读。基于对不同计量模型的比较, 我进一步探讨了每一类模型的优缺点。在文末,我讨论如何根据理论和数理统计的考量正确选择不同的面板数据模型。