社会调查与研究方法 （下）Methodologies in Social Research (Part 2)

Peking University via Coursera

Go to Course: https://www.coursera.org/learn/shehui-diaocha-yanjiu-fangfa

Introduction

# Course Review: 方法论与社会研究（下）Methodologies in Social Research (Part 2) ## Overview "方法论与社会研究（下）" is an advanced level course on Coursera that delves into the methodologies associated with social research. This course serves as a continuation from Part 1, focusing on practical and theoretical aspects of collecting, analyzing, and applying data in various fields such as social phenomena, economics, education, politics, and public health. ### Course Objectives The main aim of this course is to equip students with systematic tools for observing and measuring social phenomena while supplementing this knowledge with scientific methods of data analysis. By the end of this course, students will not only be able to gather data effectively but also competently analyze and communicate their findings in a structured and logical manner. ## Syllabus Breakdown ### Week 8: Data Collection with Questionnaires The course kicks off with an in-depth examination of one of the most common data collection methods—questionnaires. Students will learn about various types of questionnaires, core components essential for a robust survey, and best practices for questionnaire design and implementation. Emphasizing data quality management will also be covered, ensuring students grasp how to effectively oversee the data collection process. ### Week 9: Interviews and Observational Studies Transitioning from questionnaires, this week focuses on interviews and observational studies. Students will explore the roles of interviewers and observers, necessary ethical considerations, and effective interviewing techniques. The course will clarify different contexts suitable for each method, equipping students with practical skills for conducting qualitative research. ### Week 10: Literature Reviews and Trace Data Here, students are introduced to literature reviews and trace data, including what constitutes trace data and its relation to big data. The course emphasizes the importance of literature in research, teaching students various methods to conduct literature reviews and the nuances of analyzing trace data. ### Week 11: Data Organization and Quality Assessment In this session, students will learn about categorizing data types, effective organization, and the importance of data quality assessment. The course tackles the pitfalls of poor data quality and offers techniques for data cleaning and validation, crucial skills required for any researcher. ### Week 12: Case Studies The concept of case studies is pivotal in social research. This week, students will discover the common misconceptions surrounding case studies, their unique features, and how to design and present them effectively. This part of the course focuses on synthesizing real-world data into comprehensive case analyses. ### Week 13: Statistical Analysis and Data Mining Students will dive into statistical analysis and data mining methods, gaining insights into how to analyze univariate, bivariate, and multivariate data. The course will clarify the distinctions between traditional statistical analyses and the methodologies used in big data contexts. ### Week 14: Reporting and Presentation of Research Findings The final week will focus on communication and presentation techniques for research results. Students will learn the characteristics of effective research reporting and will explore academic paper formats and research report structures, emphasizing the importance of clear and concise expression of research findings. ## Final Thoughts "方法论与社会研究（下）" is a comprehensive course that offers a mix of theoretical foundations and practical techniques for aspiring researchers. Its structured approach allows students to build their skills gradually, while the robust discussion surrounding quality and validation prepares them to contribute meaningfully to their fields. ### Recommendation Whether you are a student, professional, or simply a curious learner, this course is invaluable for anyone looking to enhance their skills in social research methodologies. The instructor's engaging teaching style, combined with the wealth of practical exercises, makes it a worthwhile investment for your educational journey. I highly recommend taking "方法论与社会研究（下）" on Coursera to not only learn essential research skills but also to gain a deeper understanding of the complexities involved in social phenomena analysis.

Syllabus

第08周

　　搜集数据是从理论到实践的过程。从本周的课程开始，将进入到社会调查与研究的实践操作部分。本周讨论最常见的数据搜集方法：问卷调查。主要内容包括：什么是问卷调查？问卷有哪些基本类型？一份完整的问卷包括哪些基本要素？怎样设计一份问卷？怎样实施一项问卷调查？为保证问卷调查的质量，如何对问卷调查过程进行管理？

第09周

　　除了问卷调查以外，还有其他的数据搜集方法。本周探讨访谈调查和观察调查。主要内容包括：什么是访谈调查和观察调查？在访谈调查和观察调查中，访员扮演什么角色？要遵循哪些规范？要掌握哪些技巧？如何组织访谈调查和观察调查？不同类型的访谈或观察方法各自适用于什么情境？

第10周

　　在常见的数据搜集方法中，还有文献调查与痕迹调查。本周的主要内容包括：什么是文献调查？文献有哪些类型？文献调查有哪些方法和技巧？什么是痕迹数据？大数据又是什么？痕迹数据与大数据有什么关系？大数据调查采用什么方法？

第11周

　　没有质量的数据，就是垃圾。本周探讨调查数据的整理和数据质量的评估。主要内容包括：调查数据有哪些类型？如何整理、清理并数据库化各类调查数据？数据的质量会受哪些因素的影响？如何评估数据质量？

第12周

　　从这一周开始，进入数据运用的讨论。本周课程探讨社会调查与研究方法中在方法上具有综合意义的案例研究。主要内容包括：什么是案例研究？人们对案例研究有哪些误解？案例研究有哪些特征和用途？如何设计一项案例研究？如何搜集、分析案例研究的数据？案例研究的结果如何呈现？

第13周

　　本周探讨常见的数据运用的方法：统计分析与数据挖掘。主要内容包括：利用统计方法分析调查数据的基本原理是什么？针对单变量、双变量和多变量数据各有哪些统计分析方法？大数据分析与调查数据的统计分析有什么不同？数据挖掘有哪些类型，又有哪些分析技术和方法？

第14周

　　研究设计、搜集数据、分析数据，目的是为了理解事物之间的关系模式，结果表达是也是其中重要的一环。本周的主要内容包括：调查研究结果有什么特点？调查研究结果的表达有哪些方式？需要遵循哪些规范？作为调查研究结果的重要表达方式，学术论文与研究报告的格式分别是什么？两者之间有什么区别？

期末

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