||Computational Social Science
||The course aims to introduce computational social science (CSS) to students in various data science disciplines such as computer science, mathematics, statistics, electronic engineering, bio-/medical informatics, etc., who are interested in individual human behaviors and aggregate social processes. Assuming the enrolled students to have already known how to perform basic data analysis using a variety of algorithms or tools, the course focuses on the fundamental principles (e.g., what are good or bad practices) and the research design (e.g., how to plan rigorous studies to answer causal questions). Ample examples from the existing literature will be used to help illustrate the principles and apply the research design. At the end of the course, the students are expected to be able to do the following: (1) communicating and collaborating with social scientists using their vocabulary and reasoning logic; (2) planning and implementing rigorous computational studies on human behaviors or social processes; (3) evaluating the quality of other social science studies and suggesting feasible improvements.
||Principles of Social Research,Earl Babbie,Tsinghua University Press,2011,11；
Bit by Bit: Social Research in the Digital Age,Matthew J. Salganik,Princeton University Press,2018；
Session 1: Introduction to CSS and Research Design. 4 class hours
The basic features of computational social science (CSS, e.g., what it is, how it has come about and why, what it aims to do, and how it works), primary purpose of social science research (descriptive, explanatory, or predictive), and most common research designs in social research (within-subjects design and between-subjects design).
Session 2: Experiment in Social Research. 4 class hours
Principles and procedure of social science experiment (i.e., comparison between subjects with and without exposure to stimulus) as the key solution to problems and challenges in causal reasoning.
Session 3: Data for CSS. 4 class hours
How to collect data on human behaviors through online and offline means; technical, operational, and quality challenges involved.
Session 4: Sampling. 4 class hours
Principles and methods of classic probability sampling; opportunities and challenges of applying probability sampling to online data.
Session 5: Measurement. 4 class hours
How to quantify human thoughts and behaviors from empirical data; how to link latent concepts and observed variables; how to control for measurement noises.
Session 6: Multivariate Analysis. 4 class hours
Principles, procedure, and exemplar applications of multivariate analysis (e.g., multiple regressions) to describe, explain, and predict causal processes with multiple causes.
Session 7: Multilevel and Temporal Analysis. 4 class hours
Multilevel analysis: principles, procedure, and exemplar applications of multilevel analysis (e.g., mixed effects model and network analysis) to identify socially structured effects on individuals and emergent effects from individuals on social structure. Temporal analysis: principles, procedure, and exemplar applications of temporal analysis (e.g., time series analysis, cohort analysis, survival analysis, sequence analysis) to model dynamic characteristics of individual human behaviors and aggregated social processes.
Session 8: Research Ethics and Student Presentation. 4 class hours
Ethical concerns for traditional social science research; new challenges for computational social science; causes of and possible solutions to threats to privacy, confidentiality, security, copyrights, and other issues. Students present the results of assignment 3.
Attendance and Discussions: 25%
Assignments(Readings, In-class and online discussions, and take-home exercises): 45%