Events

Presenters:

Dr. Aline Sayer, University of Massachusetts Amherst
Dr. Mark Manning, Wayne State University

Event Date(s):
June 10 - June 14, 2013 | 9:00 am- 5:00 pm
| 9:00am to 5:00pm
Location:
University of Massachusetts, Amherst
Description:

The hierarchical linear model (HLM) provides a conceptual framework and a flexible set of analytic tools to study a variety of social, political, and developmental processes. One set of applications focuses on data in which persons are clustered within social contexts, such as couples, families, schools, neighborhoods, or organizations.

Presenters:

Dr. Daniel Nagin, Carnegie-Mellon University

Event Date(s):
June 3 - June 5, 2013 | 9:00 am- 5:00 pm
| 9:00am to 5:00pm
Location:
University of Massachusetts, Amherst
Description:

A developmental trajectory describes the course of a behavior over age or time. This two and a half days workshop aims to provide participants with the training to apply a group-based method for analyzing developmental trajectories. 

Presenters:

Dr. Jean-Philippe Laurenceau,  University of Delaware
Dr. Niall Bolger,  Columbia University

Event Date(s):
June 25 - June 28, 2013 | 9:00 am- 5:00 pm
| 9:00am to 5:00pm
Location:
University of Massachusetts, Amherst.
Description:

In recent decades, researchers have become increasingly interested in understanding people’s thoughts, emotions, and behaviors in their natural contexts. The commonality in methods for doing so—experience sampling, daily diary, and ecological momentary assessment methods—is that they all involve intensive longitudinal assessments. 

Presenters:

Scott Long, Distinguished Professor of Sociology and Statistics, Indiana University

Event Date(s):
June 17 - June 21, 2013 | 9:00 am- 5:00 pm
| 9:00am to 5:00pm
Location:
University of Massachusetts, Amherst.
Description:

This intensive workshop deals with the workflow of data analysis. Workflow encompasses the entire process of scientific research: planning, documenting, and organizing your work; creating, labeling, naming, and verifying variables; performing and presenting statistical analyses; preserving your work; and, critically, producing replicable results. Most classes in statistics focus on estimating and interpreting models. In "real world" research, these activities often involve less than 10% of the total work. This workshop is about the other 90% of the work.