Workshops
Enroll in one or more workshops below or puchase training credits now to use toward future enrollments (handy if you want to purchase now but choose later, e.g., given time-limited funds, or if you want to "bulk purchase" now and allocate to trainees later).
Enrollment in any workshop provides lifetime access to all course materials, including detailed course notes, lecture recordings, and applicable software demonstrations, all designed to support self-paced learning. Livestreamed instruction is also periodically available for a subset of workshops, as indicated below. Listed lengths represent approximate hours of lecture time, not inclusive of potential additional software demonstration videos. Get ideas for what to take next by considering our four curricular pathways.
Free Introduction to Structural Equation Modeling
Instructors: Dan Bauer & Patrick Curran
16 hours
Livestream available May 19, 21 & 23
Introduction to Sample Size Planning for Statistical Power
Instructor: Samantha Anderson
9 hours
Livestream available May 20 & 22
Multilevel Models for Hierarchical Data
Instructors: Dan Bauer & Patrick Curran
12 hours
Livestream available May 27-29
Multilevel Models for Longitudinal Data
Instructors: Dan Bauer & Patrick Curran
16 hours
Livestream available June 3-6
News & Updates
CenterStat Announces the Availability of Training Credits
We are pleased to announce that both individuals and organizations can now purchase CenterStat Training Credits using existing resources that can be applied toward future enrollments in any of our workshops. Credits are denominated in US dollars and are redeemable for up to five years from purchase, so purchasing credits is a great way to convert time-limited funds into an...
What is a suppressor variable, and how does this differ from confounding and mediation?
A constant source of confusion within the multiple regression model (and the general linear model more broadly) relates to the terms suppression and suppressor variable. Indeed, it is not uncommon to see suppression invoked anytime some unanticipated or inexplicable finding is obtained that must be explained away. This is particularly evident when a strongly hypothesized relation is not found: The...
What are ROC curves and how are these used to aid decision making?
One of the most vexing challenges in all of statistics is the need to make a valid and reliable probabilistic assessment about some unknown condition or state of affairs based solely on information gathered from sample data. Indeed, this is the foundation of traditional null hypothesis testing: there is some unknown condition in the population (the null hypothesis is either...
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...
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...
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...
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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