Quantitative Research Methods in Social Sciences
The courses listed below offer students an opportunity to develop skills necessary to work with faculty whose social science research employs quantitative methods, such as data analysis using Excel, Stata, and R.
Faculty: Profs. Robin McKnight and Joe Swingle (Spring 2019)
Description: An introduction to the collection, analysis, interpretation, and presentation of quantitative data as used to understand problems in economics and sociology. Using examples drawn from these fields, this course focuses on basic concepts in probability and statistics, such as measures of central tendency and dispersion, hypothesis testing, and parameter estimation. Data analysis exercises are drawn from both academic and everyday applications.
Prerequisite(s): ECON 101, ECON 102, or one course in sociology and fulfillment of the basic skills component of the Quantitative Reasoning requirement. Not open to students who have taken or are taking MATH 220, PSYC 205, or POL 199
Distribution(s): QRF; Social and Behavioral Analysis
Faculty: Profs. Gauri Shastry and Kristin Butcher (Spring 2019)
Description: Application of statistical methods to economic problems. Emphasis will be placed on regression analysis that can be used to examine the relationship between two or more variables. Issues involved in estimation, including goodness-of-fit, statistical inference, dummy variables, hetero-skedasticity, serial correlation, and others will be considered. Emphasis will be placed on real-world applications. The credit/noncredit grading option is not available for this course.
Prerequisite(s): ECON 101, ECON 102, and one math course at the level of MATH 115 or higher. The math course must be taken at Wellesley. One course in statistics (ECON 103, MATH 220 or PSYC 205) is also required.
Distribution(s): Social and Behavioral Analysis
Faculty: Prof. Qing Wang (Spring 2019)
Description: This is a calculus-based introductory statistics course. Topics covered include data collection, data visualization, descriptive statistics, linear regression, sampling schemes, design of experiment, probability, random variables (both discrete and continuous cases), Normal model, statistical tests and inference (e.g. one-sample and two-sample z-tests and t-tests, chi-square test, etc). Statistical language R will be used throughout the course to realize data visualization, linear regression, simulations, and statistical tests and inference.
Prerequisite(s): MATH 205. Not open to students who have taken or are taking STAT 101, MATH 101Z/STAT 101Z, ECON 103/SOC 190, POL 199, or PSYC 205, or who have taken STAT 260/QR 260.
Distribution(s): QRF, Mathematical Modeling