# DESC Readme.
Descriptive statistics in the context of social science research is the method used to summarize, illustrate, and interpret the information from a collected data set. This includes demographic data about a population or measurements obtained from a sample of that population.
There are two main categories of descriptive statistics:
Measures of Central Tendency: These measures provide a single value that describes the way the data group around a central value. This includes the mean (average), median (middle value), and mode (most frequent value).
Measures of Dispersion or Variability: These measures provide information about the spread of values around the central tendency. This includes range (the difference between the highest and lowest values), interquartile range (the range of the middle 50% of values), variance (the average of the squared differences from the Mean), and standard deviation (the square root of the variance).
Descriptive statistics in social science research often form the basis for the initial analysis of the data. They allow researchers to understand the general trends, patterns, or distributions in the data before they undertake more complex statistical analyses.
For example, researchers might use descriptive statistics to illustrate the average age of respondents, the proportion of men and women, the prevalence of certain behaviors, attitudes or beliefs, the variability in responses, and so on. These statistics can be presented in the form of tables, graphs, and charts to provide a visual understanding of the data.
A sample descriptive statistic table is shown below. In this case, all measurement items are included, the the statistics of min, max, mean (ie., average) and standard deviation are presented.
In summary, descriptive statistics provide a concise summary of data sets and a foundation for further statistical analysis. However, they only describe the data and do not allow for making predictions or determining cause and effect relationships. That would require inferential statistics, which is the next step in analyzing data after descriptive statistics.
在社会科学研究中，描述性统计是用来总结、描绘和解释从收集的数据集中获得的信息的方法。这包括关于人口的人口统计数据或从人口样本中获取的测量。
# 描述性统计
描述性统计主要有两大类：
- 中心趋势的度量：这些度量提供了一个单一的值，描述数据围绕中心值的分布。这包括平均值（平均值），中位数（中间值），和众数（最常见的值）。
- 分散或变异性的度量：这些度量提供了关于值围绕中心趋势的分布的信息。这包括范围（最高值和最低值之间的差异），四分位数范围（中间50%的值的范围），方差（来自均值的平方差的平均值），和标准差（方差的平方根）。
在社会科学研究中，描述性统计常常构成了数据初步分析的基础。它们使研究人员在进行更复杂的统计分析之前，理解数据的一般趋势、模式或分布。
例如，研究者可能使用描述性统计来描绘调查对象的平均年龄，男女比例，某种行为、态度或信念的普遍性，回答的变异性等。这些统计可以通过表格、图表和图形的形式呈现，以提供对数据的视觉理解。
下面展示了一个描述性统计表的示例。在这个案例中，包含了所有的测量项，并呈现了最小值、最大值、平均值和标准差的统计。
总的来说，描述性统计为数据集提供了简洁的总结和进一步统计分析的基础。然而，它们仅描述数据，并不允许进行预测或确定因果关系。这需要推理统计，这是在描述性统计之后分析数据的下一步。
# Respondent Profile
If you are collecting survey data from your respondents, it is necessary to present the profiles of the respondents.
# 受访者资料
如果您正在从受访者那里收集调查数据，则有必要提供受访者的个人资料。
