Bertani Alessandro, Di Paola Gioacchino, Russo Emanuele, Tuzzolino Fabio
Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, Division of Thoracic Surgery and Lung Transplantation, IRCCS ISMETT - UPMC, Palermo, Italy.
Office of Research, IRCCS ISMETT, Palermo, Italy.
J Thorac Dis. 2018 Feb;10(2):1133-1137. doi: 10.21037/jtd.2018.01.134.
The role of scientific research is not limited to the description and analysis of single phenomena occurring independently one from each other (univariate analysis). Even though univariate analysis has a pivotal role in statistical analysis, and is useful to find errors inside datasets, to familiarize with and to aggregate data, to describe and to gather basic information on simple phenomena, it has a limited cognitive impact. Therefore, research also and mostly focuses on the relationship that single phenomena may have with each other. More specifically, bivariate analysis explores how the dependent ("outcome") variable depends or is explained by the independent ("explanatory") variable (asymmetrical analysis), or it explores the association between two variables without any cause and effect relationship (symmetrical analysis). In this paper we will introduce the concept of "causation", dependent ("outcome") and independent ("explanatory") variable. Also, some statistical techniques used for the analysis of the relationship between the two variables will be presented, based on the type of variable (categorical or continuous).
科学研究的作用并不局限于对彼此独立发生的单一现象进行描述和分析（单变量分析）。尽管单变量分析在统计分析中具有关键作用，并且有助于发现数据集中的错误、熟悉和汇总数据、描述并收集关于简单现象的基本信息，但它的认知影响有限。因此，研究也主要聚焦于单一现象之间可能存在的关系。更具体地说，双变量分析探讨因变量（“结果”）如何依赖自变量（“解释性”）变量或由其解释（不对称分析），或者探讨两个变量之间不存在任何因果关系的关联（对称分析）。在本文中，我们将介绍“因果关系”、因变量（“结果”）和自变量（“解释性”）变量的概念。此外，还将根据变量类型（分类变量或连续变量）介绍一些用于分析两个变量之间关系的统计技术。
