Upcoming Livestream Workshops
Can't wait for the next Livestream or missed a workshop? No problem. Register for any asynchronous workshop and get access to its replays and materials for 1 year after purchase, including FREE asynchronous access to Introduction to Structural Equation Modeling
Asynchronous Access to Prior Workshops
Did you miss a workshop or can't wait for the next Livestream? No problem. You can enroll in our recently conducted workshops to obtain asynchronous access. You will receive access to all workshop videos for 1 year from purchase, detailed lecture and software demonstration notes, and data and script files. All files other than videos can be downloaded and retained indefinitely.
Free Introduction to Structural Equation Modeling
Replay Access: 1 year
Recorded: May 11-13, 2022
Instructors: Dan Bauer & Patrick Curran
Multilevel Modeling
Replay Access: 1 year
Recorded: June 6-10, 2022
Instructors: Dan Bauer & Patrick Curran
Latent Class/Cluster Analysis and Mixture Modeling
Replay Access: 1 year
Recorded: May 23-27, 2022
Instructors: Dan Bauer & Doug Steinley
Applied Measurement Modeling
Replay Access: 1 year
Recorded: May 23-27, 2022
Instructors: Patrick Curran & Greg Hancock
Applied Research Design Using Mixed Methods
Replay Access: 1 year
Recorded: June 3-4, 2021
Instructor: Greg Guest
Introduction to Data Visualization in R
Replay Access: 1 year
Recorded: May 31-June 3, 2022
Instructor: Michael Hallquist
Longitudinal Structural Equation Modeling
Replay Access: 1 year
Recorded: May 16-20, 2022
Instructors: Dan Bauer & Patrick Curran
Modern Missing Data Analysis
Replay Access: 1 year
Recorded: June 1-3, 2022
Instructor: Craig Enders
Applied Qualitative Research
Replay Access: 1 year
Recorded: May 30-June 3, 2022
Instructors: Greg Guest & Emily Namey
Introduction to Quantitative Meta-Analysis
Replay Access: 1 year
Recorded: June 15-18, 2021
Instructor: Tasha Beretvas
News & Updates
What exactly qualifies as intensive longitudinal data and why am I not able to use more traditional growth models to study stability and change over time?
Recent years have seen increasing interest in the collection and analysis of intensive longitudinal data (or ILD) to generate unique insights into within-person processes and change over time. In this post, we first define ILD by contrasting it to data obtained from other common longitudinal designs. Next, we consider the distinct features of ILD that we must address and can...
CenterStat Partners with APA to Offer Free Training on Intensive Longitudinal Data
With the evolution and ever greater penetration of smartphones, other personal electronic devices, and wearable technologies, scientists are increasingly able to collect and analyze Intensive Longitudinal Data (ILD). The extensive sampling of observations for each person in ILD provides researchers with unique opportunities to study psychological processes as they unfold in real time. To help participants learn more about how to conduct...
What’s the best way to determine the number of latent classes in a finite mixture analysis?
Finite mixture models, which include latent class analysis, latent profile analysis, and growth mixture models, have grown greatly in popularity over the past decade or so. Most statistical models assume a unitary (or homogeneous) population wherein all observations are governed by the same basic process. In contrast, finite mixture models aim to identify latent subgroups within the population, sometimes referred...
My advisor told me to use principal components analysis to examine the structure of my items and compute scale scores, but I was taught not to use it because it is not a “true” factor analysis. Help!
Help, indeed. This issue has been a source of both confusion and contention for more than 75 years, and papers have been published on this topic as recently as just a few years ago. A thorough discussion of principal components analysis (PCA) and the closely related methods of exploratory factor analysis (EFA) would require pages of text and dozens of...
Second Annual Winter Institute
This December and January we will again be conducting livestream classes on our most popular topics, including a new 3-day workshop on Causal Inference. All classes come with six months access to video recordings to catch up later when life interferes (because it usually does). We hope you'll consider joining us. And don't forget that you can also obtain asynchronous access to any of our great workshops...
I fit a multilevel model and got the warning message “G Matrix is Non-Positive Definite.” What does this mean and what should I do about it?
Anyone who uses multilevel models will eventually encounter the dreaded “G matrix is non-positive definite” message (or “Tau Matrix” or “Psi Matrix”, depending on the labeling used by your software). This anxiety-inducing warning is often a signal that something is wrong in your model that needs fixing. With an NPD G matrix, the obtained estimates usually reflect an “improper” solution...
Introduction to Latent Curve Modeling at Society for Psychotherapy Research
On June 23, 2021, Patrick and Dan conducted a pre-conference workshop, Introduction to Latent Curve Modeling, at the 52nd Annual International Meeting of the Society for Psychotherapy Research. A copy of the notes for participants is available at https://centerstat.org/wp-content/uploads/2021/06/BauerCurran_handouts.pdf for those who may be interested.
I’m reporting within- and between-group effects in from a multilevel model, and my reviewer says I need to address “sampling error” in the group means. What does this mean, and what can I do to address this?
This is a long-neglected topic, and one that is receiving increasing attention in the methodological literature. The problem that the reviewer is referring to is that the usual ways we obtain within- and between-group effects for lower-level predictors within the multilevel model (MLM) oftentimes generate biased estimates. The same issue arises with any form of clustering, such as when trying...
My advisor told me I should group-mean center my predictors in my multilevel model because it might “make my effects significant” but this doesn’t seem right to me. What exactly is involved in centering predictors within the multilevel model?
This is an excellent question and the topic of centering is often a source of confusion when using multilevel models (MLMs) in practice. This confusion is in part due to the need to address a complexity that arises within the MLM but is not relevant within the traditional multiple regression model: when modeling the effect of a lower-level predictor on...
A reviewer recently asked me to comment on the issue of equivalent models in my structural equation model. What is the difference between alternative models and equivalent models within an SEM?
Differentiating between alternative models and equivalent models has long been a point of confusion in many research applications. Although the challenge of equivalent models can arise within almost any analytic setting, this is particularly salient within the structural equation model (or SEM). It is helpful first distinguish between alternative and equivalent models. To begin, one of the greatest strengths of...
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