# News and Updates

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 MoreContinuous 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…

Read MoreThis 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…

Read MoreWe 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 MoreIn 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…

Read MoreIn 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…

Read MoreBy 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…

Read MoreWe 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 MoreA 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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