COVID-19 UPDATE: The safety of our students and staff is our top priority. Therefore, Stats Camp will be holding seminars online via live interactive zoom discussion groups. Our goal is to expand on the interactivity side and provide one-on-one consulting time via virtual breakout rooms. We are offering a discount code to a future camp worth $200 off and we are offering 1-hour of post camp consultation as an added value. Registrations will be accepted up to 12 hours prior to seminar start date and time. All seminars will be conducted in CDT time and will be recorded. The recordings will be made available to you within 3-5 business days of the live recording date. Access will be granted to the recorded videos for 1 year from the date of the seminar. Have questions? Contact us
Overview: This summer institute is an intensive short seminar on the principles of structural equation modeling. Topics include confirmatory factor analysis, multiple-group comparisons, factorial invariance as well as extended applications such as hierarchical models and multi-level SEM.
Instructor: Todd D. Little Ph.D.
Todd D. Little, PhD is a Professor and director of the Institute for Measurement, Methodology, Analysis and Policy at Texas Tech University. He is widely recognized for his quantitative work on various aspects of applied SEM (e.g., modern missing data treatments, indicator selection, parceling, modeling developmental processes) as well as his substantive developmental research (e.g., action-control processes and motivation, coping, and self-regulation). His work has garnered over 29,388 citations with an h-index of 85 and an i10-index of 195. In 2001, he was elected to membership in the Society for Multivariate Experimental Psychology, and in 2009, he was elected President of APA’s Division 5 (Evaluation, Measurement, and Statistics). He is a fellow in APA, APS, and AAAS. In 2013, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development. Both awards cited his founding of Stats Camp (Statscamp.org) in 2003 and its ongoing impact on shaping the quality of scientific inquiry for both past and future generations of researchers. Download Todd’s CV (PDF)
Instructor: Elizabeth Grandfield Ph.D.
Elizabeth is a Ph.D. in Quantitative Psychology at the University of Kansas. Her research focuses on evaluating measurement invariance with an emphasis in longitudinal designs. In areas of applied research, Elizabeth has been involved in longitudinal children studies at Juniper Gardens as well as a national nursing study at Kansas University Medical Center, both in Kansas City. She also received the 2011 Multivariate Software Award, presented by Peter Bentler and Eric Wu. Elizabeth has been involved in Stats Camp since 2012.
Software and Computer Support Seminar Audience
If you need to analyze the covariance structure of multivariate data and have a basic statistical background, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You do not need to know matrix algebra, calculus, or likelihood theory (although that knowledge would be beneficial). Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication, will benefit from the seminar.
The seminar will support LISREL, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. No previous knowledge of LISREL, Mplus or Laavan is assumed. Furthermore, nearly all the techniques taught in the seminar can be translated fairly easily to most other packages.
Below are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.