Log-transformation and its implications for data analysis.
Log-transformation and its implications for data analysis.
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对数转换及其对数据分析的影响。
DOI：
10.3969/j.issn.1002-0829.2014.02.009
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发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
Tu XM
中科院分区：
文献类型：
--
作者：
Feng C;Wang H;Lu N;Chen T;He H;Lu Y;Tu XM
对数变换广泛应用于生物医学和心理社会学研究中，以处理偏斜数据。本文强调了严重的问题，在这个经典的方法来处理偏斜的数据。尽管人们普遍认为对数变换可以减少数据的变异性，使数据更接近正态分布，但通常情况并非如此。此外，对对数转换数据进行的标准统计检验的结果通常与原始的非转换数据无关，我们通过使用模拟数据的例子来证明这些问题。我们的结论是，如果使用，数据转换必须非常谨慎地应用。我们建议，在大多数情况下，研究人员放弃这些传统的方法来处理偏态数据，而是使用新的分析方法，不依赖于分布的数据，如广义估计方程（GEE）。
The log-transformation is widely used in biomedical and psychosocial research to deal with skewed data. This paper highlights serious problems in this classic approach for dealing with skewed data. Despite the common belief that the log transformation can decrease the variability of data and make data conform more closely to the normal distribution, this is usually not the case. Moreover, the results of standard statistical tests performed on log-transformed data are often not relevant for the original, non-transformed data.We demonstrate these problems by presenting examples that use simulated data. We conclude that if used at all, data transformations must be applied very cautiously. We recommend that in most circumstances researchers abandon these traditional methods of dealing with skewed data and, instead, use newer analytic methods that are not dependent on the distribution the data, such as generalized estimating equations (GEE).