1 FREE Night Included, Click Here to Book Your Room!
Venue Information: Embassy Suites By Hilton 1000 Woodward Pl NE, Albuquerque, NM 87102
Cancellation Policy:
If you cancel your registration at least two weeks prior to the day the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). If you cancel within the 14 days prior to the course you may transfer the full amount towards any future course or receive a full refund (minus a processing fee of $295).
In the unlikely event that Stats Camp Foundation must cancel a course, our team will do our best to inform you as soon as possible with more information. Students then have the option of receiving a full refund of the course fee or a full credit towards another future course. In no event shall Stats Camp Foundation be held responsible for any incidental or consequential damages that you may incur because of the cancellation. Course disclaimer. All courses include 365 days of asynchronous access after event.
*You must register prior to the event start, attendance is NOT mandatory. However; CE credits are only available if you attend live for the duration of the course. (Includes: On-Demand, Livestream and In-Person courses, all Stats Camp Retreats are in-person attendance only.)
what students are saying
The Stats Camp expert instructors were clear, concise, and helpful in addressing questions. Before attending, I was concerned that I would have trouble truly understanding all of the concepts and material in such a short time, but the instruction was fantastic and not overwhelming. I particularly found it advantageous to stay on-site at the Embassy Suites so I could participate in all of the after hours networking events.
Spiros Tzivelekis, Ph.D., GHE Director - AmgenStats Camp was the most useful statistical training I’ve ever had. The instructors are down to earth and practical in their teaching style and the classroom environment was relaxed and non-threatening, which is necessary for such a potentially daunting topic. In particular, the one-on-one private consultation with my own data was invaluable, I highly recommend signing up for the Summer Stats Camp in Albuquerque!
Kris Carlson, Ph.D., Sandia National LaboratoriesThe Stats Camp instructors have an unmistakable dedication to research methods and data analysis of the very highest quality-but are remarkably balanced in their very obvious efforts to connect with others on a professional and personal level as very likable and real people. I really enjoyed the networking opportunities that the breakout sessions provided and will be returning for another Summer Camp soon!
Chen Zhang, Ph.D., Faculty University of MemphisThank you to Dr. Todd Little and his team for facilitating such a great statistical methods training workshop in Albuquerque. I absolutely got what I needed and was energized in my work when I returned to Cleveland. This was a wonderful experience that I will be sure to share with my colleagues here at CWRU. Now I am looking forward to mediation this summer in Albuquerque! Who knew I would be excited for more stats!
Leigh-Ann Sweeney, Ph.D., Lecturer and Health Service ResearcherThe Stats Camp instructor’s clear and practical presentation of material that was once intimidating to me has uncovered a powerful analytic tool. I feel comfortable that I’ve learned the correct application of SEM Foundations and Extended Applications from experts in the field. At the same time, I was introduced to cutting edge statistics techniques and I understand the advantages of their practical use and application.
Jenny Tehan, Ph.D., Department of Psychology University of AkronStats Camp is the place for a meeting of the minds and an opportunity to sit down and sort through complex ideas with people who can guide you, side-by-side, through the sticking points so you can get back into the flow. It’s amazing that something like this exists. Dr. Little has given the world a tremendous gift with Stats Camp. I walked in knowing nothing substantial and walked out delighted and better skilled.
Benjamin Theisen, Ph.D., Business Psychology Consulting GroupThe Summer Stats Camp training institute provides a wonderful opportunity for researchers and data analysts to learn the basic foundations of SEM as well as some advanced applications and research opportunities that SEM can facilitate. Dr. Little provides personable “hands-on”instruction in a relaxed and enjoyable environment. I highly recommend this summer institute to faculty and graduate students alike!
Paul Schrodt, Ph.D., Professor and Director of Graduate StudiesI was impressed with the amount of material Dr. Todd Little and team were able to cover in Summer Camp. The instructors moved at a pace appropriate for the participants, adapted the materials as we went along to accommodate this pace, and still offered individual consultations. I am confident that I can take everything I learned, from the basic to the advanced topics & employ them independently in my own research.
Katie Paschall, Ph.D., Senior Research Scientist at Child TrendsI can strongly recommend the Stats Camp Summer course in Longitudinal Mixture Modeling. It is as systematic and comprehensive as the very best of the research methods courses I have participated in since I moved from business to academics 12 years ago. It has been an important tool box for pulling apart differing effects in subpopulations for the coming generation of social and behavioral science researchers, starting now!
Alan R. Johnson, Ph.D., Senior Research Fellow at NORD University, Bodø, NorwayI participated in the SEM: Foundations and Extended Applications course earlier this summer. It was such a wonderful statistics training opportunity that has, already, found lots of applications in various grant proposals and analyses. As I’ve said to several colleagues since, Stats Camp has a special ability to take something that prior to my arrival seemed overwhelming and complicated and make it seem so do-able.
Amy K. Syvertsen, Ph.D., Applied Developmental ScientistI took both Foundations of SEM and Longitudinal SEM last summer and I have to say this has been one of the most useful learning experiences of my life. The courses were excellent! Everything was explained in a clear fashion with plenty of time for questions and practice. Learning was fun and the teaching happened at multiple levels, such that everyone would have a lot of knowledge gain regardless of their level of expertise.
Rodica Damian, Ph.D., Associate Professor University of California, DavisThe highlight of the summer break was attending ‘Stats Camp,’ an educational camp in Albuquerque New Mexico that provides advanced-level training in Statistics. The 5-day class I took (‘SEM with Mplus’) passed by quickly, as an information-packed series of lectures, hands-on examples, personal consultations, and jokes. Classes were small, lectures succinct/direct, and training content based on each individual student requests.
Anna Yu Lee, PhD, MPH, MA, Counselor/TherapistI can honestly say that for the first time in years I have been able to focus on myself and my research. I feel physically and mentally healthier than ever and I am excited about the cutting-edge knowledge and resources I am gaining in the rapidly evolving field of latent variable modeling. I am going to make it a goal to prioritize Statscamp for myself and graduate students on a yearly basis. Definitely recommend!
Sarah D. Lynne-Landsman, Ph.D., Family, Youth & Community SciencesIN PERSON – 5-day Statistics Short Course
Data Mining and Machine Learning Seminar Overview:
An intermediate 5-day course introducing several popular machine learning approaches such as regression based methods (ridge and lasso regularized regression, regression splines), tree methods (random forests, boosted trees), support vector machines, and Interpretative Machine Learning (ILM) as well as their application to empirical data. The course combines lectures and hands-on practice using R.
Seminar Topics:
- Review of linear regression and the least squares criterion
- Regularization methods (ridge regression, lasso, elastic net)
- Regression splines
- Prediction error and k-fold cross validation
- Tree methods to predict categorical or continuous outcomes (CART, random forest, boosting
- Interpretative Machine Learning (IML)
- Support vector machines for classification
Seminar Description:
Machine learning refers to leveraging data to build statistical models or algorithms. The objective is usually to gain knowledge about the structure in the data in order to make predictions or decisions.
This short course is based on
- An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)
- Hands-on Machine Learning with R (Boemke & Greenwell)
- Interpretable Machine Learning (Molnar)
The course starts with briefly outlining the key differences and similarities between standard parametric modeling (e.g., linear regression, structural equation modeling) and machine learning (aka statistical learning, aka data mining). The course provides basic insights into a number of popular methods such as regression methods (ridge regression and the lasso, regression splines), tree methods (CART, random forests, boosting), interpretable machine learning (IML), and support vector machines. The emphasis is on a conceptual understanding of these methods and their appropriate application to empirical data. Importantly, these methods are useful not only for large data collections, but also more generally for exploratory analyses when the substantive theory to design and fit parametric models (e.g. SEM) is lacking. Machine learning is used in a wide variety of fields including but not limited to public health, education, biology, and the different social sciences.
Participants are invited to discuss potential machine learning applications to their data during individual consultations with the instructor scheduled at the end of days 2-5.
Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts (R), relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.
Instructor: Gitta Lubke, Ph.D.
Gitta Lubke is a Professor Emerita in the Department of Psychology/Quantitative Area at the University of Notre Dame. Her research interests included machine learning and general latent variable modeling. Empirical applications were mainly in the field of psychiatric disorders and behavioral genetics. Other areas of expertise include mixture models, twin models, multi-group factor analysis and measurement invariance, longitudinal analyses, and the analysis of categorical data.
APA Continuing Education Credits:
This course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.
Seminar Includes:
Materials, downloads, recorded course video viewable for up to one year.