Shopping Cart

No products in the cart.

Shopping Cart

No products in the cart.

The Center for Statistical Training

by Curran-Bauer Analytics

Statistical training in advanced quantitative methods for researchers in the social, health, and behavioral sciences.

Online Workshops

Accessible, effective, comprehensive training in statistics, data science, and research methods are just one click away.

Learn More
View Sample Videos
Free 3-Day SEM Workshop

Upcoming Livestream Workshops

Can't wait for the next Livestream or missed a workshop? No problem. Register for self-paced access to prior workshops and get access to replays and materials for 1 year after purchase, including our FREE workshop, Introduction to Structural Equation Modeling

Free Introduction to Structural Equation Modeling

May 10-12, 2023
Livestream & Replay Access
Instructors: Dan Bauer & Patrick Curran

Mixture Modeling and Latent Class Analysis

May 15-19, 2023
Livestream & Replay Access
Instructor: Dan Bauer & Doug Steinley

Applied Measurement Modeling

May 16-19, 2023
Livestream & Replay Access
Instructors: Patrick Curran & Greg Hancock

Applied Qualitative Research

May 22-26, 2023
Livestream & Replay Access
Instructors: Greg Guest & Emily Namey

Causal Inference

May 22-26, 2023
Livestream & Replay Access
Instructor: Doug Steinley

Multilevel Modeling

May 22-26, 2023
Livestream & Replay Access
Instructors: Dan Bauer & Patrick Curran

Introduction to Data Visualization in R

May 22-26, 2023
Livestream & Replay Access
Instructor: Michael Hallquist

Modern Missing Data Analysis

June 5-7, 2023
Livestream & Replay Access
Instructor: Craig Enders

Machine Learning: Theory and Applications

June 5-9, 2023
Livestream & Replay Access
Instructor: Doug Steinley

Sample Size Planning for Power and Accuracy

June 6-8, 2023
Livestream & Replay Access
Instructor: Samantha Anderson

Analyzing Intensive Longitudinal Data

June 12-16, 2023
Livestream & Replay Access
Instructors: JP Laurenceau & Niall Bolger

Longitudinal Structural Equation Modeling

June 12-16, 2023
Livestream & Replay Access
Instructors: Dan Bauer & Patrick Curran

Self-Paced Access to Prior Workshops

Missed a workshop or can't wait for the next Livestream? No problem. You can set your own schedule by registering for self-paced access to recently conducted workshops. You will receive access to all workshop videos for 1 year from purchase and all workshop materials (detailed lecture notes and, when applicable, software demonstration notes, data files and syntax). 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

Causal Inference

Replay Access: 1 year
Recorded: December 15-17, 2021
Instructor: Doug Steinley

Network Analysis

Replay Access: 1 year
Recorded: June 6-10, 2022
Instructor: Doug Steinley

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

Analyzing Intensive Longitudinal Data

Replay Access: 1 year
Recorded: June 13-17, 2022
Instructors: JP Laurenceau & Niall Bolger

Machine Learning: Theory and Applications

Replay Access: 1 year
Recorded: May 16-20, 2022
Instructor: Doug Steinley

News & Updates

Announcing CenterStat Unscripted: A New Weekly YouTube Livestream on Quantitative Methods

February 2, 2023
In large part because Dan and Patrick have jobs from which they can't be fired, they keep coming up with random activities with which to entertain themselves (and possibly others). CenterStat is thus excited to announce their latest boondoggle: CenterStat Unscripted, a weekly YouTube Live broadcast in which Dan and Patrick ad lib their way through a different topic in...
Keep Reading

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?

October 14, 2022
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...
Keep Reading

CenterStat Partners with APA to Offer Free Training on Intensive Longitudinal Data

August 28, 2022
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...
Keep Reading

What’s the best way to determine the number of latent classes in a finite mixture analysis?

November 11, 2021
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...
Keep Reading

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!

October 12, 2021
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...
Keep Reading

Second Annual Winter Institute

September 21, 2021
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...
Keep Reading

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?

July 22, 2021
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...
Keep Reading

Introduction to Latent Curve Modeling at Society for Psychotherapy Research

June 23, 2021
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.
Keep Reading

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?

March 30, 2021
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...
Keep Reading

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?

March 16, 2021
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...
Keep Reading

Subscribe to Our Newsletter

Please enter your name.
Please enter a valid email address.
Subscribe
Something went wrong. Please check your entries and try again.