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140.658.01 STATISTICS FOR PSYCHOSOCIAL RESEARCH: STRUCTURAL MODELS

Department: Biostatistics
Term: 2nd term
Credits: (4 credits)
Contact: Qian-Li Xue
Academic Year: 2012 - 2013
Description:

Presents quantitative approaches to theory construction in the context of multiple response variables, with models for both continuous and categorical data. Topics include the statistical basis for causal inference; principles of path analysis; linear structural equation analysis incorporating measurement models; latent class regression; and analysis of panel data with observed and latent variable models. Draws examples from the social sciences, including the status attainment approach to intergenerational mobility, behavior genetics models of disease and environment, consumer satisfaction, functional impairment and disability, and quality of life.

Student Evaluation: Student evaluation based on class participation, problem sets, and a final exam.
Learning Objective:

Upon successful completion of this course, students will be able to design path analysis models; to analyze latent variable panel data with linear structural equation models; to design latent class analysis models in the situation of categorical data; and to understand causal inference techniques.

Location: Baltimore
Class Times:
  • Monday 10:30 - 11:50
  • Wednesday 10:30 - 11:50
Lab Times:
  • Friday 10:00 - 10:50
  • Friday 11:00 - 11:50
Enrollment Minimum: 10
Instructor Consent: Consent required for all students

330.657 or consent of instructor

For consent, contact: qxue@jhsph.edu
Prerequisite:

330.657 or consent of instructor

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail
Catalog Subcommittee Actions: TargetAud, CourseLocation, CourseFormat, IRBSurvey, AuditorsAllowedId, ExpectedEnrollNumber, JointlyOffered, DeptCoList, CourseOfferRationaleNote, CourseSectionNote, LabNumber, ScheduleTypeId, LabScheduleTypeId, LabTime, .10/07/2011;
Jointly Offered With:
Special Comments: Please register for one computer lab only, either 140.958.01 or 140.958.02.