上一期papercrazy给大家带来了，什么是研究中的数据分析?的相关介绍，今天我们接着介绍，为什么要在研究中分析数据?也是为了通过数据来推算研究结果，并不是凭空捏造来讲。这样对于学术研究更有实际性，对于论文写作也会依据与说服性。

　　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.

　　以上就是papercrazy给我们带来的为什么要在研究中分析数据?的中英文相关介绍，也是为了在学术研究过程中通过数据事实说话更有依据性。对于论文写作等也会更有据有理。更多精彩内容，我们会陆续为大家带来，敬请期待。