Quantitative Research Method Courses

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. 

ECON 103/SOC 190 - Introduction to Probability and Statistical Methods

Faculty: Profs. Phillip Levine and Joe Swingle 

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 or one course in sociology. Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. Not open to students who have taken or are taking STAT 160, STAT 218 or PSYC 205.

Distribution(s): Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL); SBA - Social and Behavioral Analysis

ECON 203 - Econometrics

Faculty: Prof. Tyler Giles (Economics)

Description: This course introduces students to the methods economists use to assess empirical relationships, primarily regression analysis. Issues examined include statistical significance, goodness-of-fit, dummy variables, and model assumptions. Includes an introduction to panel data models, instrumental variables, and randomized and natural experiments. Students learn to apply the concepts to data, read economic research, and write an empirical research paper. The Credit/Non Credit grading option is not available for this course. Letter graded only.

Prerequisite(s): All of the following -- 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, PSYC 205, STAT 160, or STAT 218) is also required.

Distribution(s): Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL); SBA - Social and Behavioral Analysis

STAT 218 - Introductory Statistics And Data Analysis

Faculty: Profs. Anny-Claude Joseph, Qing Wang

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 160,  ECON 103/SOC 190, POL 199, PSYC 205, or QR 260/STAT 260.

Distribution(s): DL - Data Literacy (Formerly QRF); MM - Mathematical Modeling and Problem Solving; DL - Data Literacy (Formerly QRDL)

STAT/QR 260 - Applied Data Analysis and Statistical Inference

Faculty: Prof. Cassandra Pattanayak 

Description: This is an intermediate statistics course focused on fundamentals of statistical inference and applied data analysis tools. Emphasis on thinking statistically, evaluating assumptions, and developing practical skills for real-life applications to fields such as medicine, politics, education, and beyond. Topics include t-tests and non-parametric alternatives, multiple comparisons, analysis of variance, linear regression, model refinement, missing data, and causal inference. Students can expect to gain a working knowledge of the statistical software R, which will be used for data analysis and for simulations designed to strengthen conceptual understanding. This course, offered through Wellesley's Quantitative Analysis Institute, can be counted as a 200-level course toward the major or minor in Mathematics, Statistics, Economics, Environmental Studies, Psychology or Neuroscience. Students who earned a Quantitative Analysis Institute Certificate are not eligible for this course.

Prerequisite(s): Any introductory statistics course (BISC 198, ECON 103/SOC 190, MATH 101/STAT 101, STAT 160, STAT 218, POL 299, or PSYC 205).

Distribution(s): MM - Mathematical Modeling and Problem Solving