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第776行：
第776行： − + − This section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a Wikipedia article. − − This section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a Wikipedia article. 第786行：
第783行： − + − The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:{{sfn|Adèr|2008a|p=337}} − − The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions: 第796行：
第790行： − + − The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. − − The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. − − *Test for [[common-method variance]]. − The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.{{sfn|Adèr|2008a|pp=338-341}} − − The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. 第816行：
第802行： − + − The quality of the [[measuring instrument|measurement instruments]] should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature. − − The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature. − − There are two ways to assess measurement: [NOTE: only one way seems to be listed] − − There are two ways to assess measurement: [NOTE: only one way seems to be listed] 第834行：
第813行： − + − − ====Initial transformations初始的转换==== − After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.{{sfn|Adèr|2008a|p=344}}<br />+ − After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.<br /> − +
→Practitioner notes 从业者注意事项
在教育方面，大多数教育工作者都可以使用数据系统来分析学生的数据。<ref>Aarons, D. (2009). [https://search.proquest.com/docview/202710770 Report finds states on course to build pupil-data systems.] ''Education Week, 29''(13), 6.</ref> 这些数据系统以'''场外交易数据格式over-the-counter data format'''（嵌入标签、补充文件和帮助系统，并作出关键的包装 / 展示和内容决策）向教育工作者提供数据以提高其数据分析的准确性。<ref>Rankin, J. (2013, March 28). [https://sas.elluminate.com/site/external/recording/playback/link/table/dropin?sid=2008350&suid=D.4DF60C7117D5A77FE3AED546909ED2 How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help.] ''Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.''</ref>
在教育方面，大多数教育工作者都可以使用数据系统来分析学生的数据。<ref>Aarons, D. (2009). [https://search.proquest.com/docview/202710770 Report finds states on course to build pupil-data systems.] ''Education Week, 29''(13), 6.</ref> 这些数据系统以'''场外交易数据格式over-the-counter data format'''（嵌入标签、补充文件和帮助系统，并作出关键的包装 / 展示和内容决策）向教育工作者提供数据以提高其数据分析的准确性。<ref>Rankin, J. (2013, March 28). [https://sas.elluminate.com/site/external/recording/playback/link/table/dropin?sid=2008350&suid=D.4DF60C7117D5A77FE3AED546909ED2 How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help.] ''Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.''</ref>
==Practitioner notes 从业者注意事项==
==从业者注意事项==
这个部分包含了一些技术性的解释，它们可能对从业者有所帮助，但是超出了维基百科文章的典型范围。
这个部分包含了一些技术性的解释，它们可能对从业者有所帮助，但是超出了维基百科文章的典型范围。
===Initial data analysis 初始数据分析===
===初始数据分析===
在初始数据分析阶段和主要分析阶段之间最重要的区别是，在初始数据分析阶段，人们不进行任何旨在回答原始研究问题的分析。初始数据分析阶段由下列四个问题指导:
在初始数据分析阶段和主要分析阶段之间最重要的区别是，在初始数据分析阶段，人们不进行任何旨在回答原始研究问题的分析。初始数据分析阶段由下列四个问题指导:
====Quality of data 数据质量====
====数据质量====
应尽早检查数据的质量。数据质量可以通过几种方式，使用不同类型的分析进行评估: 频数、描述统计学量（平均值、标准差、中位数）、正态性（偏态、峰度、频率直方图）、 n: 变量与数据集外部变量的编码方案进行比较，如果和编码方案不具有可比性，则可能对数据进行修正。
应尽早检查数据的质量。数据质量可以通过几种方式，使用不同类型的分析进行评估: 频数、描述统计学量（平均值、标准差、中位数）、正态性（偏态、峰度、频率直方图）、 n: 变量与数据集外部变量的编码方案进行比较，如果和编码方案不具有可比性，则可能对数据进行修正。
* 检验'''<font color='#ff8000'>共同方法变异common-method variance</font>'''
* 检验'''<font color='#ff8000'>共同方法变异common-method variance</font>'''
在初始数据分析阶段，评估数据质量的分析方法的选择取决于将在主要分析阶段进行的分析。
在初始数据分析阶段，评估数据质量的分析方法的选择取决于将在主要分析阶段进行的分析。
====Quality of measurements 测量的质量====
====测量的质量====
当测量仪器的质量不是研究的重点或研究问题的时候，它只能在初始数据分析阶段进行检验。从业者应该检查测量仪器的结构是否符合文献报告的结构。
当测量仪器的质量不是研究的重点或研究问题的时候，它只能在初始数据分析阶段进行检验。从业者应该检查测量仪器的结构是否符合文献报告的结构。
有两种方法来评估测量: [注意: 似乎只列出了一种方法]
有两种方法来评估测量: [注意: 似乎只列出了一种方法]
*Analysis of homogeneity ([[internal consistency]]), which gives an indication of the [[Reliability (statistics)|reliability]] of a measurement instrument. During this analysis, one inspects the variances of the items and the scales, the [[Cronbach's alpha|Cronbach's α]] of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale{{sfn|Adèr|2008a|pp=341-342}}
*Analysis of homogeneity ([[internal consistency]]), which gives an indication of the [[Reliability (statistics)|reliability]] of a measurement instrument. During this analysis, one inspects the variances of the items and the scales, the [[Cronbach's alpha|Cronbach's α]] of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale{{sfn|Adèr|2008a|pp=341-342}}
* 同质性检验（'''<font color='#ff8000'>内部一致性internal consistency</font>'''）用来表示测量仪器的'''<font color='#ff8000'>可靠性Reliability</font>'''。在这个分析过程中，我们会检查各个项目的变异和量尺刻度，量尺的'''<font color='#ff8000'>克隆巴赫α系数 Cronbach’s alpha </font>''' ，以及当一个项目从量尺上被删除时克隆巴赫α系数的变化。
* 同质性检验（'''内部一致性internal consistency'''）用来表示测量仪器的'''可靠性Reliability'''。在这个分析过程中，我们会检查各个项目的变异和量尺刻度，量尺的'''<font color='#ff8000'>克隆巴赫α系数 Cronbach’s alpha </font>''' ，以及当一个项目从量尺上被删除时克隆巴赫α系数的变化。
====初始的转换====
在对数据和测量的质量进行评估之后，从业者可能会决定填补缺失的数据，或者对一个或多个变量进行'''<font color='#ff8000'>初始的转换initial transformations</font>'''，尽管这也可以在主要分析阶段进行。
在对数据和测量的质量进行评估之后，从业者可能会决定填补缺失的数据，或者对一个或多个变量进行'''初始的转换initial transformations'''，尽管这也可以在主要分析阶段进行。
Possible transformations of variables are:<ref>Tabachnick & Fidell, 2007, p. 87-88.</ref>
Possible transformations of variables are:<ref>Tabachnick & Fidell, 2007, p. 87-88.</ref>