Analyzing Intensive Longitudinal Data: A Guide to Diary, Experience Sampling, and Ecological Momentary Assessment Methods... Read More»
Workshops & Conferences
Throughout the year, CRF holds intensive training workshops and conferences in advanced statistical and cutting-edge methodological techniques relevant to family researchers.
To receive announcements about our trainings please join our mailing list (See box on right). To learn about our past programs click here.
Dr. Jean-Philippe Laurenceau, University of Delaware
Dr. Niall Bolger, Columbia University
J. Scott Long, Indiana University
This workshop deals with the most important regression models for binary, ordinal, nominal and count outcomes. While advances in software make it simple to estimate these models, the effective interpretation of these nonlinear models is a vexingly difficult art that requires time, practice, and a firm grounding in the goals of your analysis and the characteristics of your model. The workshop begins by discussing the general objectives for interpreting results from any regression model and considers why these objectives are more difficult in nonlinear models.... Read More»
Dr. Aline Sayer, University of Massachusetts Amherst
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.... Read More»
Dr. Daniel Nagin, Carnegie-Mellon University
A developmental trajectory describes the course of a behavior over age or time. This three day workshop aims to provide participants with the training to apply a group-based method for analyzing developmental trajectories. This methodology has four significant capabilities:
(1) the capability to identify rather than assume distinctive groups of trajectories
(2) the capability to estimate the proportion of the population following each such trajectory group