Spring 2008 One-Credit Courses in Methodology
If you have a question about a specific course, please contact the instructor of that course directly. If you have a general question about the suite of one-credit methodology courses, please contact Katie Bode-Lang.
For more information about the one-credit courses, please see our frequently asked questions:
Instructors: Dr. Michael Rovine, Professor of Human Development and Family Studies
Course Dates: 1/16, 1/23, 1/30, 2/6. 2/13
Prerequisites: HDFS 519 or equivalent course in linear regression
Course Description: This course will serve as an introduction to time series modeling that will include the following topics:
Objectives, approaches and examples of time series modeling, including:
- Appropriate applications of time series models
- What time series models can tell us
What time series models can tell us
- Graphing time series
- Types of variation
- Stationary time series
- Analyzing trends, accounting for seasonal variation, smoothing
- Autocorrelations and the correlogram
An introduction to stochastic processes
Fitting time series in the time domain, including:
- Estimating autocovariance and autocorrelation functions
- Fitting an autoregressive process
- Fitting a moving average process
- Estimating the parameters of an ARIMA model
An introduction to State-space models and the Kalman Filter
Instructor: Dr. Donna Coffman, Research Associate, The Methodology Center
Course Dates/Times: 2/20, 2/27, 3/5, 3/19, 3/26
Prerequisites: HDFS 519 or equivalent course in linear regression, and some exposure to Structural Equation Modeling and Multilevel modeling
Course Description: This course will focus on mediation analysis in the social sciences, and is intended for advanced graduate students who have completed regression analysis, structural equation modeling and multilevel (or hierarchical) linear modeling. Topics to be covered include regression models for mediation, bootstrap methods for testing the mediation effect, mediation in multilevel models and with longitudinal data, models with multiple mediating variables, and moderated mediation models. Practical application of the methods will be demonstrated in popular software (e.g. SAS and LISREL) and hands-on experience with the methods will be obtained through take-home assignments.
Week 1: Introduction to mediation analysis. Regression models for mediation. Methods for testing the mediation effect.
Week 2: Structural Equation Models for mediation. Multiple mediating variables.
Week 3: Mediation in multilevel and longitudinal models. Moderated mediation models.
Week 4: Causal mediation.
Week 5: Causal mediation continued.
Course Requirements: Course grade is based equally on four homework assignments and class participation.
Instructor: Dr. Rhonda BeLue, Assistant Professor of Health Policy and Administration
Course Dates: 4/2, 4/9, 4/16, 4/23, 4/30
Prerequisites: At least second year graduate student standing
Course Outline/Syllabus: This course is designed to familiarize students with the methods and applications of complexity science and health. This course will cover the basics of complexity theory, non-linear dynamics, the current literature on complexity science and health and related methodological tools.
Course Objectives: Students will acquire an introduction to the basics of:
- Complex adaptive system
- Systems thinking and systems thinking tools
- Network Analysis
- Agent Based Modeling
- Machine Learning tools
- Hidden Order: How Adaptation Builds Complexity (Helix Books, paperback)
- Selected papers from the current literature
Software: Open source software-NetLogo and WEKA
Evaluation and Grading:
- Participation and In-Class Activities 10%
- Homework (3 at 20% each) 60%
- Final Project 30%
Homework: Three homework assignments will be given to practice and evaluate quantitative and computational applications of complexity science.
Presentation: Student teams (1-2 students per team) will present a complexity science approach to solving a health related problem.
Final Project: Final projects will be discussed during the 1st class period and tailored to the needs of the students.
Week 1: Introduction: Defining Complexity and non-linear dynamics
Week 2: Systems thinking, system dynamics, system identification
Week 3: Agent based modeling, network analysis
Week 4: Machine learning
Week 5: Final project presentations