News and Updates


Do you have any materials that demonstrate how to estimate structural equation models using lavaan in R?

April 24, 2019

This is a question we often hear, particularly from students and junior researchers who don’t have access to sometimes expensive commercial software for fitting structural equation models. It is possible to estimate a wide array of SEMs, ranging from simple path models to fully latent SEMs to growth curve models and beyond, using the lavaan…

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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…

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Can I estimate an SEM if the sample data are not normally distributed?

January 23, 2019

Continuous distributions are typically described by their mean (central tendency), variance (spread), skew (asymmetry), and kurtosis (thickness of tails). A normal distribution assumes a skew and kurtosis of zero, but truly normal distributions are rare in practice. Unfortunately, the fitting of standard SEMs to non-normal data can result in inflated model test statistics (leading models…

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How do I know if my structural equation model fits the data well?

January 11, 2019

This is one of the most common questions we receive and, unfortunately, there are no quick answers. However, there are some initial guidelines that can be followed when assessing the fit of an SEM. For most SEMs, the goal of the analysis is to define a model that results in predicted values of the summary…

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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,…

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Growth modeling within a structural equation modeling framework

March 31, 2017

In a prior episode of Office Hours, Patrick explored “Growth modeling in a multilevel modeling framework.” In the current episode he discusses how growth models can also be estimated within the structural equation modeling (SEM) framework. He begins with a brief review of the confirmatory factor analysis model and describes this as the foundation of…

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Why use a Structural Equation Model?

March 23, 2017

In this edition of CBA Office Hours, Dan discusses some of the principal advantages of the structural equation model (SEM) relative to more traditional data analytic approaches like the linear regression model. Advantages include the ability to account for measurement error when estimating effects, test the fit of the model to the data, and specify statistical…

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New Guidelines for Obtaining Optimal Scale Scores

December 4, 2016

By far one of the most challenging aspects of any empirical research application is how to best obtain valid and reliable scale scores of the theoretical constructs under study. The field of psychometrics has given rise to a myriad of methods for designing assessments, evaluating dimensionality, and estimating person-specific scores for subsequent analysis. The traditional…

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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…

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Care Needed When Using Latent Basis Functions of Change

March 15, 2016

A common challenge when modeling repeated measures data is finding the linear or nonlinear shape that best characterizes the observed pattern of change over time. The SEM-based latent curve model offers several options for modeling nonlinearity, and a particularly flexible method is to freely estimate a subset of factor loadings to define a “latent basis…

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