News and Updates


What is the difference between a growth model estimated as a multilevel model versus as a structural equation model?

February 18, 2019

This very common question reflects a great deal of unnecessary confusion about how to select a specific analytic approach for modeling longitudinal data. The general term “growth modeling” refers to a variety of statistical methods that allow for the estimation of inter-individual (or between-person) differences in intra-individual (or within-person) change. Often, the function describing within-person…

Read More

Announcing Minimal-Cost Introduction to Multilevel Modeling Workshop for Graduate Students

December 15, 2018

At Curran-Bauer Analytics, we have long been committed to providing broad access to high-quality training opportunities for students in the social, behavioral and health sciences. We are thus very excited to announce a new three-day workshop, Introduction to Multilevel Modeling for Graduate Students, to be held in Chapel Hill, NC on May 29-31, 2019, at steeply reduced…

Read More

Summer Workshop Schedule for 2018

October 10, 2017

We are pleased to announce our workshop schedule for this summer: May 9-11: Network Analysis May 21-25: Multilevel Modeling June 4-8: Latent Class/Cluster Analysis and Mixture Modeling June 18-22: Structural Equation Modeling June 25-29: Longitudinal Structural Equation Modeling   This year, we will be holding all workshops at the Chapel Hill-Carrboro Hampton Inn & Suites,…

Read More

Using Nested Data to Enhance Causal Inference

May 30, 2017

There has been an ongoing controversy about whether a mother’s use of antidepressants during pregnancy results in elevated rates of autism in their children. Although much research has focused on this question, it has been limited by the omission of potential confounding variables and the study of just one child per mother. A recent study…

Read More

Growth modeling within a multilevel modeling framework

March 31, 2017

In an earlier episode of Office Hours, Patrick addressed the question, “What is growth curve modeling?” In this episode he explores how a growth curve model can be estimated within the multilevel linear modeling (MLM) framework. Patrick begins by reviewing the assumption of independence in the general linear model and how this is violated when…

Read More

Coding time in growth models

March 31, 2017

Whether estimating growth models in a structural equation or multilevel modeling framework, the researcher must choose how to numerically code the passage of time. In this episode of Office Hours, Patrick explores the implications of scaling time within the general growth curve model. Patrick begins by revisiting the interpretation of the intercept of a regression…

Read More

How many clusters do I need to fit a multilevel model?

February 6, 2017

In this edition of CBA Office Hours, Dan discusses a question that frequently comes up in our multilevel modeling workshop, namely, “How many clusters do I need to be able to fit a multilevel model?”  Here, clusters refers to upper-level units, so in the case of individuals nested within groups, the groups, and in the case of…

Read More

Syntax for Computing Random Effect Estimates in SPSS

September 27, 2016

Many programs can be used to fit multilevel models. For instance, in our multilevel modeling summer workshop, we demonstrate three programs: SAS, SPSS, and Stata. Unlike SAS, Stata, and many other programs, however, SPSS does not currently offer the option to output estimates of the random effects. Obtaining estimates of the random effects can be…

Read More

Introduction to Growth Curve Modeling

April 19, 2016

We are pleased to make available a number of materials related to a workshop titled Introduction to Growth Curve Modeling: An Overview and Recommendations for Practice that Curran-Bauer Analytics conducted at the biennial convention of the Society for Research on Adolescence on April 3rd, 2016. We have posted a copy of the slides from the…

Read More

Within- and Between-Group Effects

December 9, 2015

An important concept to understand with multilevel data is the distinction between between-group and within-group effects. For instance, larger animal species (elephants) tend to live longer than smaller species (ducks); this is the between-group effect of size on life expectancy. Within a species, however, larger individuals (big ducks) tend to live less long than smaller…

Read More