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. Donna Coffman, Research Associate, The Methodology Center
Course Dates: 1/13, 1/20, 1/27, 2/3, 2/10
Prerequisites: HDFS 519 or equivalent
Course Description: This course will provide an introduction to causal inference under the potential outcomes framework. We will discuss the meaning of an average causal effect (ACE) and how it differs from a regression coefficient. We will learn about the various methods for estimating an ACE and also extend the notion of an ACE to models which include moderating or mediating variables. Social and behavioral scientists will be provided with practical tools for estimating causal effects from observational data. All techniques for estimating the ACE and computing standard errors will be illustrated with R code and worked examples. Knowledge of computing with R will be beneficial but not required. Course grade will be based on homework assignments and class participation.
Instructor: Dr. Wayne Osgood, Professor of Sociology; Dr. Scott Gest, Associate Professor of Human Development and Family Studies
Course Dates/Times: 2/17, 2/24, 3/3, 3/17, 3/24
Prerequisites: One year graduate social statistics sequence.
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.
HD FS 597B - Experimental Design for Building and Evaluating Behavioral Interventions: Introduction to Phased Experimental Approaches
Instructor: Dr. Linda Collins, Professor of Human Development and Family Studies, Dr. John Dziak, Research Associate, The Methodology Center; Dr. Inbal Shani, Research Associate, The Methodology Center
Course Dates/Times: 3/31, 4/7, 4/14, 4/21, 4/28
Prerequisites: HDFS 516 and 519 or equivalent, and a basic working knowledge of SAS.
Course Description: Most behavioral interventions are developed a priori and evaluated as a package. If the intervention is found to be efficacious, little attention is paid to whether the intervention is as potent as it could be. An alternative approach is to take a systematic, phased experimental approach to optimize the potency of the intervention. In this course we will discuss experimental design for use in phased experimental approaches to building optimized behavioral interventions. Factorial, fractional factorial, and Sequential Multiple Assignment Randomized Trial (SMART) designs will be covered.