Spring 2012 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:

FAQs for Students

FAQs for Faculty

 


 

HD FS 597B - Causal Inference in the Behavioral Sciences I

CRN: 842278

Instructors: Dr. Donna Coffman, The Methodology Center

Course Dates: January 11, 18, 25, February 1, 8 (5 weeks)

Time and Location: Wednesdays, 1-3:30 PM at the Methodology Center

 

Prerequisites: HDFS 519 or equivalent

 

Enrollment limited to 15 students

 

Course Description: This course will provide an introduction to causal inference. Social and behavioral scientists will learn practical methods for estimating causal effects from observational data. We will discuss the meaning of an average causal effect (ACE) in the potential outcomes framework and how this differs from a regression coefficient. We will learn about the various propensity score methods for estimating an ACE and will extend ACE models to include moderating variables and time-varying treatments. All techniques for estimating the ACE and computing standard errors will be illustrated with R code and applied examples. Knowledge of computing with R is beneficial but not required.

 

Course Requirements: Course grade is based on homework assignments and class participation

 


 

HD FS 597C - Causal Inference in the Behavioral Sciences II

CRN: 842281

Instructors: Dr. Donna Coffman, The Methodology Center

Course Dates: February 15, 22, 29, March 7, 21 (5 weeks; no class over spring break)

Time and Location: Wednesdays, 1-3:30 PM at the Methodology Center

 

Prerequisites: HDFS 519 or equivalent

 

Enrollment limited to 15 students

 

Course Description: Intended for social and behavioral scientists, this course will build on Causal Inference in the Behavioral Sciences I to introduce more advanced causal inference methods under the potential outcomes framework. Those who wish to enroll only in this course should discuss it with the instructor first. We will extend causal effect models to include mediating variables and non-compliance. Students will learn additional practical methods for estimating causal effects from observational data. All techniques will be illustrated with R code and applied examples. Knowledge of R will be beneficial but not required.

 

Course Requirements: Course grade is based on homework assignments and class participation

 


 

HD FS 597D - Social Network Analysis

CRN: 842284

Instructors: Dr. Scott Gest, Associate Professor of Human Development and Family Studies

Course Dates: March 28, April 4, 11, 18, 25 (5 weeks)

Time and Location: Wednesdays, 5:15-7:45 PM at the Methodology Center

 

Prerequisites: One year graduate social statistics sequence.

 

Enrollment limited to 15 students

 

Course Description: This course provides a brief introduction to the concepts and methods of social network analysis. We will focus on peer networks in childhood and adolescence, and especially on concepts and measures that may be useful in developmental or intervention studies. For example, we will discuss measures of network centrality that can be used to describe an individual's potential for influence within a peer network, and measures of cohesion, cliquishness and status hierarchies that can characterize entire networks. Each class will include an introduction of new concepts and methods, a demonstration of their application to peer network data, and time for students to practice applying the methods with example data sets.

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