Longitudinal Data Analysis|
|Certificates: Applied Data Science|
Work across different fields, from medicine and public health to social sciences and education often involves the collection and analysis of longitudinal data--combination of cross-section and time series (repeated measures for the unit of observation) data. This rich data structure provides opportunities to explore questions that could not be addressed with simpler data sets, but at the same time requires special considerations since we are analyzing observations that are not independent. The course introduces students to appropriate graphical exploration of the data and the specification and estimation of fixed and random effects models. It also develops the basic framework for Difference-in-Differences models and explores their applications.
||Gen Ed Area Dept:
NSM QAC, SBS QAC|
|Course Format: Laboratory Course||Grading Mode: Graded|
||Prerequisites: [QAC201 or SOC257 or GOVT201 or PSYC280 or NS&B280] OR [QAC380 or PSYC395] OR ECON300 OR [GOVT367 or QAC302] OR PSYC200
||Fulfills a Major Requirement for: (DATA-MN)
||Past Enrollment Probability: Not Available
|SECTION 01 - 4th Quarter|
|Special Attributes: CQC|
|Major Readings: Wesleyan RJ Julia Bookstore
No required textbook. Journal articles and online material
Some references from:
Fitzmaurice, Garrett M., Laird, Nan M., and Ware, James H, APPLIED LONGITUDINAL ANALYSIS, Wiley, 2011
Taris, Toon, A PRIMER IN LONGITUDINAL DATA ANALYSIS, SAGE Publications, 2000 (available in online format through Wesleyan Library)
Weiss, Robert E., MODELING LONGITUDINAL DATA, Springer-Verlag, 2005.
|Examination and Assignments: |
Several homework assignments and a take-home final exam linked to the course project. Part of the grade will depend on class preparation and participation.
|Additional Requirements and/or Comments: |
An introductory statistics/data analysis background is a prerequisite for the course and that is why QAC201, or 380, or GOVT367 etc. are listed as formal prerequisites. Pre-req overrides will be approved by the Professor for students who satisfy this basic requirements through other course work. The course includes a strong lab component and programming with a statistical analysis software (e.g. SAS, or Stata, or R) is a significant part of the course work.
|Instructor(s): Kaparakis,Emmanuel I. Times: ...W... 07:00PM-09:50PM; Location: ALLB204; |
|Total Enrollment Limit: 16||SR major: 0||JR major: 0|| || |
|Seats Available: 5||GRAD: 1||SR non-major: 6||JR non-major: 6||SO: 3||FR: 0|