News and Updates


A New Podcast on All Things Quantitative: Quantitude

October 30, 2019

Our very own Patrick Curran has teamed up with Greg Hancock (Professor, College of Education, University of Maryland) to launch a new podcast called Quantitude. It is dedicated to all things quantitative, ranging from the relevant to the highly irrelevant. Picture a cross between the Car Talk guys, the two old men from the Muppets,…

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How can I estimate statistical power for a structural equation model?

July 6, 2019

This is a question that often arises when using structural equation models in practice, sometimes once a study is completed but more often in the planning phase of a future study. To think about power, we must first consider ways in which we can make errors in hypothesis testing (Cohen, 1992). Briefly, the Type I…

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What are modification indices and should I use them when fitting SEMs to my own data?

June 10, 2019

This is a great question and is one that prompts much disagreement among quantitative methodologists. Nearly all confirmatory factor analysis or structural equation models impose some kind of restrictions on the number parameters to be estimated. Usually, some parameters are set to zero (and thus not estimated at all), but sometimes restrictions come in the…

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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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The Importance of Studying Individual Trajectories, Even for Countries

May 31, 2018

The analysis of longitudinal data has quickly gained in importance across a variety of fields because it allows for the examination of questions about change over time. This is why all of our current workshops (Network Analysis, Latent Class/Mixture Modeling, Multilevel Modeling, Structural Equation Modeling, and Longitudinal Structural Equation Modeling) address the analysis of longitudinal…

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CBA Office Hours on Linear Regression

August 3, 2017

It is critical for researchers in the behavioral, health, and social sciences to have a full understanding of the linear regression model. Not only is this model important in its own right, but it serves as the foundation for more advanced statistical models, such as the multilevel model, factor analysis, structural equation modeling, generalized linear…

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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…

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Introduction to latent class / profile analysis

April 17, 2017

Although latent class analysis (LCA) and latent profile analysis (LPA) were developed decades ago, these models have gained increasing recent prominence as tools for understanding heterogeneity within multivariate data. Dan introduces these models through a hypothetical example where the goal is to identify voter blocks within the Republican Party by surveying which issues voters regard…

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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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