什么是数据与数据分析

按常识性理解，我们会单纯地认为数据就是数字（如3.1415），其实数字类数据只不过是数据中的一种——定量数据而已。除此之外，还有定性数据（如受访者面对开放问卷的文字回复）和分类数据。科学研究中需要对这些数据进行分析以获得有用的结论和见解。为了向读者介绍关于数据的一般性知识（数据分析的定义与作用、定性数据与定量数据的定义与差异以及如何在研究中获取与分析定性数据和定量数据），本文分享Questionpro网站的文章，原文题目为“研究中的数据分析:为什么要数据、数据的类型、定性和定量研究中的数据分析”。需要指出的是，QuestionPro是一个帮助企业进行数据分析和研究的在线调查平台，因此，该文的针对数据的一些描述仅以调查问卷获取的数据作为例子，但相关知识可以用于面对其它研究数据时参考。——译者注

目录

一、什么是研究中的数据分析？

1.1 为什么要在研究中分析数据？

1.2 研究中的数据类型

二、定性研究中的数据分析

2.1 在定性数据中寻找模式

2.2  定性研究中用于数据分析的方法

三、定量研究中的数据分析

3.1 准备分析数据

3.2 定量研究中用于数据分析的方法 

四、研究数据分析中要考虑的因素

非原创声明：本文中的闪图复制自北岭加州州立大学（California State University, Northridge）网页https://www.csun.edu/~vcpsy00h/creativity/define.htm

一、什么是研究中的数据分析？

What is data analysis in research?

根据莱康普特和谢希尔（LeCompte and Schensul）的说法，研究中的数据分析是研究人员用来将数据缩减为一个故事并对其进行解释以获得见解的过程。数据分析过程有助于将大量数据缩减为有意义的更小片段。

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers for reducing data to a story and interpreting it to derive insights. The data analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense.

在数据分析过程中，有三件重要的事情发生。第一个是组织数据。第二个是通过总结和分类将数据缩减，以利于从数据中找到便于识别和关联的模式和主题。第三个是对数据进行自上而下或自下而上的分析。

Three essential things take place during the data analysis process — the first data organization. Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps in finding patterns and themes in the data for easy identification and linking. Third and the last way is data analysis – researchers do it in both top-down or bottom-up fashion.

另一方面，马歇尔和罗斯曼将数据分析描述为一个混乱、模糊和耗时的过程，但数据分析却是一个创造性和迷人的过程，通过这样的过程，大量收集起来的数据被整理成有序、结构化且包含意义的东西。

Marshall and Rossman, on the other hand, describe data analysis as a messy, ambiguous, and time-consuming, but a creative and fascinating process through which a mass of collected data is being brought to order, structure and meaning.

我们可以说，“数据分析和解释是这样一个过程，它代表了演绎和归纳逻辑在研究和分析中的应用”。

We can say that “the data analysis and interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

1.1  为什么要在研究中分析数据？

Why analyze data in research?

研究人员非常依赖数据，因为他们有故事要讲或有问题要解决。它从一个问题开始，数据只不过是这个问题的一种答案。但是，如果没有问题要问呢？嗯！即使没有问题要问，也可能会探索数据——我们称之为“数据挖掘”，它经常揭示数据中一些值得探索的有趣形态。

Researchers rely heavily on data as they have a story to tell or problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’ which often reveal some interesting patterns within the data that are worth exploring.

与数据类型无关的是，研究人员的探索精神、使命感和读者们的关注会引导他们找到形态来塑造他们想要讲述的故事。在分析数据时，对研究人员的一个基本要求是保持开放，对意想不到的形态、表达和结果保持不偏不倚。请记住，有时，数据分析会讲述在开始数据分析时没有预料到的最不可预见却又最激动人心的故事。所以，依靠你手头的数据，享受探索性研究的旅程。

Irrelevant to the type of data, researchers explore, their mission, and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased towards unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected at the time of initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research.

1.2 研究中的数据类型

Types of data in research

每一种数据在赋予特定的价值后，都有一种罕见的描述事物的品质。对于分析，您需要在给定的上下文中组织、处理和呈现这些值，以使其变得有用。数据可以有不同的形式；以下是主要的数据类型。

Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

定性数据类: 当呈现的数据有文字和描述时，那么我们称之为定性数据。虽然可以观察这些数据，但是在研究中分析——特别是为了比较——这类数据时会比较主观，比较困难。例子: 品质数据（Quality data）代表所有描述味道、体验、质地或被认为是质量数据的观点的一切事物。例如，这种类型的数据通常是通过焦点小组（focus groups）、个人访谈或在调查中使用开放式问题收集的。

Qualitative data: When the data presented has words and descriptions, then we call it qualitative data. Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal interviews, or using open-ended questions in surveys.

定量数据类: 任何以数字表示的数据都称为定量数据。这种类型的数据可以分为类别数字（categories）、分组数字（grouped）、测量数字（measured）、计算数字（calculated）或排序数字（ranked.）。例如: 年龄、等级、成本、长度、体重、分数等等一切都属于这类数据。可以用图形格式、图表或统计分析方法来显示这些定量数据。（例如，）在调查研究中，OMS（Outcomes Measurement Systems——结果测量系统）问卷是收集数字数据的一个重要来源。

Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data. This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.

分类数据类（Categorical data）: 它是以分组呈现的数据。但是，分类数据中包含的条目不能属于一个以上的组。举例来说：一个人对一项调查的回应是说出他的生活方式、婚姻状况、吸烟习惯或饮酒习惯，这属于分类数据。卡方检验是分析这些数据的标准方法。

Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

二、定性研究中的数据分析

Data analysis in qualitative research

数据分析和定性数据研究的工作与数字数据略有不同，因为定性数据由文字、描述、图像、物体和偶尔还有的符号等组成。从如此复杂的信息中获得见解是一个复杂的过程。因此，它通常用于探索性研究和数据分析。

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis.

2.1 在定性数据中寻找模式

Finding patterns in the qualitative data

虽然从文本信息中找到形态有几种方法，但基于词句（word）的方法是研究和数据分析中最能依靠和最广泛使用的通用技术（global technique）。值得注意的是，定性研究中的数据分析过程是人工的（manual）。在这里，研究人员通常阅读可用的数据，并找到重复的或常用的单词。例如，在研究从非洲国家收集的数据以了解人们面临的最紧迫问题时，研究人员可能会发现“食物”和“饥饿”是最常用的词，于是将突出它们以供进一步分析。

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find “food” and “hunger” are the most commonly used words and will highlight them for further analysis.

关键词的上下文关系（The keyword context）是另一种广泛使用的基于词句的技术。在这种方法中，研究者试图通过分析参与者使用特定关键词的上下文关系来理解观点。例如，在受访者中进行“糖尿病”概念研究的研究人员可能会分析受访者何时以及如何使用或提及“糖尿病”一词的上下文关系。

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  For example, researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

基于仔细观察（scrutiny）的技术也是强烈推荐的用于识别高质量数据形态的文本分析方法之一。在这种技术下，比较和对比是广泛使用的方法，用于区分特定文本之间的相似性或不同性。举个例子: 为了搞清楚“住院医生在一个公司的重要性”，收集的数据分为认为有必要聘请住院医生的人和认为没有必要的人。比较和对比是可以用来分析具有单一回答问题类型的调查的最佳方法。

The scrutiny-based technique is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single answer questions types.

比喻（metaphors）可以用来减少数据堆积（data pile），并在其中找到形态，以便更容易地将数据与理论联系起来。

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

变量分割（Variable Partitioning）是另一种用于分割变量的技术，这样研究人员可以从大量数据中找到更一致的描述和解释。

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

2.2  定性研究中用于数据分析的方法

Methods used for data analysis in qualitative research 

定性研究中有几种分析数据的技术，但这里只给一些常用的方法。

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods

内容分析法: 它是研究方法中被广泛接受、最常用的数据分析技术。它可用于从文本、图像，有时还可用于分析实物（physical items）中，分析所记录的信息（documented information）。何时何地使用这种方法，要看研究问题是什么。

Content Analysis: It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.

叙事分析法: 这种方法用于分析从各种来源收集的内容，如个人访谈、实地观察和调查。大多数时候，人们分享的故事或观点都集中在寻找研究问题的答案上。

Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys. The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.

话语分析法（Discourse Analysis）: 与叙事分析类似，话语分析用于分析与人的互动。然而，这种特殊的方法考虑了研究者和被研究者之间进行交流的社会背景。除此之外，话语分析在得出任何结论时还关注生活方式和日常环境。

Discourse Analysis: Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.

扎根理论（Grounded Theory）: 当你想解释一个特定现象发生的原因时，使用扎根理论来分析定性数据是最好的方法。扎根理论用于研究在不同环境下发生的大量类似案例的数据。当研究人员使用这种方法时，他们可能会改变解释或产生新的解释，直到他们得出某种结论。

Grounded Theory: When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

三、定量研究中的数据分析

Data analysis in quantitative research

3.1 准备分析数据

Preparing data for analysis 

研究和数据分析的第一阶段是为分析数据做准备，以便让名义数据可以转化为有意义的东西。数据准备包括以下阶段。

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

3.1.1 第一阶段: 数据验证（Phase I: Data Validation）

进行数据验证是为了了解收集的数据样本是否符合预设标准，或者它是一个有偏差的数据样本，数据验证又分为四个不同的阶段 。

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages 

预防错误: 例如确保一个人所记录的对调查或问卷的每个回复真实。

Fraud: To ensure an actual human being records each response to the survey or the questionnaire 

数据甄别：例如确保每个参与者或回答者都是根据研究标准选择的。

Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria 

过程：确保在收集数据样本时保持道德标准。

Procedure: To ensure ethical standards were maintained while collecting the data sample 

 完整性：例如确保受访者回答了在线调查中的所有问题。否则，面试官已经问了问卷中设计的所有问题。

Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

3.1.2 第二阶段:数据编辑（Phase II: Data Editing）

更常见的情况是，大量的研究数据样本充满了错误。例如问卷回答者有时会不正确地填写某些字段，有时会不小心跳过这些字段。数据编辑是一个过程，其中研究人员必须确认提供的数据没有这样的错误。他们需要进行必要的检查和异常值检查，以编辑原始编辑，并为分析做好准备。

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

3.1.3 第三阶段:数据编码 （Phase III: Data Coding）

例如，在这三个阶段中，第三阶段是与对调查答复进行分组和赋值相关的数据准备的最关键阶段。如果一项调查是在1000个样本的情况下完成的，研究人员将创建一个年龄组，根据受访者的年龄来区分他们。因此，分析小数据集比处理大数据堆更容易。

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses. If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

3.2 定量研究中用于数据分析的方法 

Methods used for data analysis in quantitative research

在数据准备好进行分析后，研究人员可以使用不同的研究方法和数据分析方法来获得有意义的见解。当然，统计技术是最有利于分析数字数据的。该方法再次分为两组。首先，“描述性统计”用于描述数据。第二，“推理统计”有助于比较数据。

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical techniques are the most favored to analyze numerical data. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data.

3.2.1 描述性统计（ Descriptive statistics）

这种方法用于描述研究中通用数据类型的基本特征。它以如此有意义的方式呈现数据，以至于数据中的形态（pattern）开始有意义。然而，描述性分析并没有超出作出结论。这些结论又一次基于研究人员迄今提出的假设。以下是几种主要的描述性分析方法。

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

频率测量法：计数、百分比、频率；通常用来表示一个特定事件多久发生一次；当研究人员想展示一个反应多久发生一次时，就用频率来表示。

Measures of Frequency：Count, Percent, Frequency；It is used to denote home often a particular event occurs；Researchers use it when they want to showcase how often a response is given.

中心趋势测量法：平均值、中位数、模态；该方法被广泛用于展示各点的分布；当研究人员想展示最常见或平均出现的反应时，就会使用这种方法。

Measures of Central Tendency：Mean, Median, Mode；The method is widely used to demonstrate distribution by various points；Researchers use this method when they want to showcase the most commonlyor averagely indicated response.

离差或变化的测量法：范围、方差、标准差；这里的范围由最高点和最低点定义；方差和标准差等于观察到的记录和平均值之间的差值；它通过陈述区间来确定记录的分布；研究人员使用这种方法来展示数据的分散（spread out），它帮助他们确定数据分散的深度，直到它直接影响平均值。

Measures of Dispersion or Variation：Range, Variance, Standard deviation；Here the field equals high/low points；Variance standard deviation = difference between the observed score and mean；It is used to identify the spread of scores by stating intervals；Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affectsthe mean.

位置测量法：百分位数排名，四分位数排名；它依靠标准化的记录帮助研究者确定不同记录之间的关系；当研究者想要将记录与平均数进行比较时，通常会使用这种方法。

Measures of Position：Percentile ranks, Quartile ranks；It relies on standardized scores helping researchers to identify the relationship between different scores；It is often used when researchers want to compare scores with the averagecount.

对于定量市场研究来说，使用描述性分析往往会给出绝对的数字，但分析永远不足以证明这些数字背后的原理。然而，例如，有必要考虑适合您的调查问卷和研究人员想要讲述的故事的最佳研究方法和数据分析方法。举例来说，平均数是展示学生在学校的平均分数的最佳方法。当研究者打算将研究或结果局限于所提供的样本而不加以概括时，最好是依靠描述性统计。例如，当你想比较两个不同城市的平均投票率时，用差分统计就足够了。

For quantitative market research use of descriptive analysis often give absolute numbers, but the analysis is neversufficient to demonstrate the rationale behind those numbers. Nevertheless, itis necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scoresin schools. It is better to rely on the descriptive statistics when theresearchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you wantto compare average voting done in two different cities, differential statistics are enough.

描述性分析也被称为 "单变量分析"，因为它通常用于分析单一变量。

Descriptive analysis is alsocalled a ‘univariate analysis’ since it is commonly used to analyze a singlevariable.

3.2.2 推断性统计（Inferential statistics）

推断统计是在对代表种群收集的样本进行研究和数据分析后，对更大的种群进行预测。例如，你可以在一家电影院询问大约100名观众是否喜欢他们正在观看的电影。研究者就会对收集到的样本进行推断统计，推理出大约有80-90%的人喜欢这部电影。

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiencesat a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected sample to reason that about 80-90% of people like the movie. 

以下是推断统计的两个重要方面：1）估计参数，它从样本研究数据中提取统计数据并展示一些关于种群参数的东西；2）假设检验，就是通过抽样调查数据来回答调查研究问题。例如，研究人员可能有兴趣了解最近推出的新色口红是否好用，或者多种维生素胶囊是否有助于儿童在游戏中表现更好。

Here are two significant areas of inferentialstatistics：Estimating parameters——It takes statistics from the sample research data and demonstrates something about the population parameter. Hypothesis test——It’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or ifthe multivitamin capsules help children to perform better at games.

这些都是复杂的分析方法，用于展示不同变量之间的关系，而不是描述单一变量。它常常用于研究者想要超越绝对数字的一些东西来理解变量之间的关系。以下是研究中常用的一些数据分析方法。

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables. Here are some of the commonly used methods for data analysis in research.

相关性分析法。当研究人员不进行实验研究时，研究人员有兴趣了解两个或多个变量之间的关系，他们就会选择相关研究方法。

Correlation: When researchers are not conducting experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.

交叉表格法(Cross-tabulation)。交叉格表也称为列联(contingency)表格法，用于分析多个变量之间的关系。假设所提供的数据有年龄和性别类别，以行和列的形式呈现。二维交叉表通过显示每个年龄类别中的男性和女性数量，有助于进行无缝的数据分析和研究。

Cross-tabulation: Also called contingency tables, cross-tabulation is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of malesand females in each age category.

回归分析法。为了了解两个变量之间的紧密关系，研究人员不会超出主要和常用的回归分析方法，这也是一种预测分析方法。在这种方法中，你有一个基本因素，称为因变量。在回归分析中，你也有多个独立变量。你要努力找出独立变量对因变量的影响。自变量和因变量的值被假定为以无误的随机方式确定。

Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictiveanalysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regressionanalysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.

频率表格法。该统计过程用于测试实验中两个或两个以上的变化或差异程度。相当程度的差异意味着研究结果是显著的。在许多情况下，方差分析检验和方差分析是相似的。

Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In manycontexts, ANOVA testing and variance analysis are similar.

方差分析法。统计过程用于测试实验中两个或两个以上的差异或不同程度。相当程度的变化意味着研究结果是显著的。在许多情况下，方差分析检验和方差分析是相似的。

Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In manycontexts, ANOVA testing and variance analysis are similar.

四、研究数据分析中的要考虑的因素

Considerations in reseacrh data analysis

研究数据分析中需要考虑以下因素。

Considerations in research data analysis

1. 研究人员必须具备分析数据的必要技能，接受培训以展示高标准的研究实践。理想情况下，研究人员必须对选择一种统计方法而不是另一种的基本原理有更多的了解，以获得更好的数据洞察力。

Researchers must have the necessary skills to analyze the data, Gettingtrained to demonstrate a high standard of research practice. Ideally,researchers must possess more than a basic understanding of the rationale ofselecting one statistical method over the other to obtain better data insights.

2. 通常，研究和数据分析方法因科学学科不同而不同，因此，在分析之初获得统计学建议有助于设计调查问卷、选择数据收集方法和选择样本。

Usually, research and data analytics methods differ by scientificdiscipline; therefore, getting statistical advice at the beginning of analysis helpsdesign a survey questionnaire, select data collection methods, andchoose samples.

3. 数据研究和分析的主要目的是得出无偏见的最终见解。任何错误或保持偏见的心态去收集数据，选择分析方法，或选择受众样本，都会得出有偏见的推论。

The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind tocollect data, selecting an analysis method, or choosing audience sample il to draw a biased inference.

4. 与研究数据和分析中使用的复杂程度无关足以纠正客观结果测量定义不清的问题。是设计有问题还是意图不明确都不要紧，但不明确可能会误导读者，所以要避免这种做法。

Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It doesnot matter if the design is at fault or intentions are not clear, but lack ofclarity might mislead readers, so avoid the practice.

5. 在研究中，数据分析背后的动机是为了呈现准确可靠的数据。尽可能避免统计错误，并想办法处理日常挑战，如异常值、数据缺失、数据改变、数据挖掘或开发图形表示。

The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way todeal with everyday challenges like outliers, missing data, data altering, data mining, or developing graphical representation.

每天产生的数据量是令人生畏的，尤其是当数据分析已经成为中心工作的时代。在2018年，数据供应总量达到了2.8万亿千兆字节。因此，很明显，想要在竞争激烈的世界中生存下来的企业必须拥有出色的能力，分析复杂的研究数据，得出可操作的见解，并适应新的市场需求。

The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, itis clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

原文：Data analysis in research: Why data, types of data, data analysis in qualitative and quantitative research

https://www.questionpro.com/blog/data-analysis-in-research