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第933行：
第933行： − + − In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.{{sfn|Adèr|2008b|p=363}} − In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report. 第943行：
第941行： − + − − In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested. − − In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested. 第957行：
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→Main data analysis 主要数据分析
'''非线性分析Nonlinear analysis'''通常是必要的，当数据是从非线性系统中获取的时候。非线性系统可以表现出复杂的动力学效应，包括'''分岔bifurcations'''、'''[[混沌]] chaos'''、'''谐波harmonics'''和'''<font color='#ff8000'>次谐波subharmonics</font>'''，这些效应不能用简单的线性方法进行分析。非线性数据分析与非线性系统辨识密切相关。<ref name="SAB1">Billings S.A. "Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains". Wiley, 2013</ref>
'''非线性分析Nonlinear analysis'''通常是必要的，当数据是从非线性系统中获取的时候。非线性系统可以表现出复杂的动力学效应，包括'''分岔bifurcations'''、'''[[混沌]] chaos'''、'''谐波harmonics'''和'''<font color='#ff8000'>次谐波subharmonics</font>'''，这些效应不能用简单的线性方法进行分析。非线性数据分析与非线性系统辨识密切相关。<ref name="SAB1">Billings S.A. "Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains". Wiley, 2013</ref>
===Main data analysis 主要数据分析===
===主要数据分析阶段===
主要分析阶段进行旨在回答研究问题的分析，以及撰写研究报告初稿所需的其他相关分析。
主要分析阶段进行旨在回答研究问题的分析，以及撰写研究报告初稿所需的其他相关分析。
====Exploratory and confirmatory approaches探索性和验证性方法====
====探索性和验证性方法====
在主要的分析阶段，可以采用探索性或验证性的方法。通常方法是在收集数据之前决定的。探索性分析中，分析数据之前没有明确的假设，分析人员搜索能够很好地描述数据的模型。验证性分析对数据进行明确的假设检验。
在主要的分析阶段，可以采用探索性或验证性的方法。通常方法是在收集数据之前决定的。探索性分析中，分析数据之前没有明确的假设，分析人员搜索能够很好地描述数据的模型。验证性分析对数据进行明确的假设检验。
Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.
Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.
对探索性数据分析的解释应该非常谨慎。当同时测试多个模型时，发现其中至少一个模型具有统计学意义显著的几率很高，但这可能是由于 Ⅰ 类错误。在测试多个模型时，总是调整显著性水平很重要，例如，使用Bonferroni校正。'''<font color = '#32cd32'>另外，不应该在同一数据集中进行探索性分析后进行验证性分析。one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. </font>'''探索性分析是用来为一个理论寻找想法，但不是用来检验这个理论的。当一个数据集中使用探索性分析发现了一个模型，然后在同一个数据集中进行验证性分析，这可能仅仅意味着验证性分析的结果首先是由于和探索性分析中同样的 Ⅰ 类错误而导致的。因此，验证性分析不会比最初的探索性分析更有用。
对探索性数据分析的解释应该非常谨慎。当同时测试多个模型时，发现其中至少一个模型具有统计学意义显著的几率很高，但这可能是由于 Ⅰ 类错误。在测试多个模型时，总是调整显著性水平很重要，例如，使用Bonferroni校正。'''<font color = '#32cd32'>另外，不应该在同一数据集中进行探索性分析后进行验证性分析。one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. </font>'''
探索性分析是用来为一个理论寻找想法，但不是用来检验这个理论的。当一个数据集中使用探索性分析发现了一个模型，然后在同一个数据集中进行验证性分析，这可能仅仅意味着验证性分析的结果首先是由于和探索性分析中同样的 Ⅰ 类错误而导致的。因此，验证性分析不会比最初的探索性分析更有用。
====Stability of results 结果的稳定性====
====结果的稳定性====
It is important to obtain some indication about how generalizable the results are.{{sfn|Adèr|2008b|pp=361-371}} While this is often difficult to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing that.
It is important to obtain some indication about how generalizable the results are.{{sfn|Adèr|2008b|pp=361-371}} While this is often difficult to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing that.