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Growth Models with Time-Varying Covariates

June 20, 2017

In a prior episode of Office Hours, Patrick discussed predicting growth by time-invariant covariates (TICs), predictors for which the numerical values are constant over time. In this episode, Patrick describes the inclusion of time-varying covariates (TVCs), predictors with numerical values that can differ across time. Examples of TVCs are numerous and include time-specific measures of…

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Growth Models with Time-Invariant Covariates

June 20, 2017

Once an optimal model of linear or nonlinear change has been established, it is often of interest to try to predict individual differences in change over time. In this installment of our Office Hours series on growth modeling, Patrick discusses how to incorporate time-invariant covariates (TICS) into a growth model. TICs are predictors that do…

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Modeling Nonlinear Growth Trajectories

June 20, 2017

In this installment to our series of Office Hour videos on growth curve modeling, Patrick describes how to model nonlinear trajectories. Although the most basic form of growth model specifies a linear trajectory in which the model-implied change in the outcome is constant per unit-change in time, many constructs under study in the social and…

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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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Regression to the Mean in Everyday Life

May 12, 2017

Regression to the mean is an often misunderstood phenomena that routinely arises in both empirical research and in every day life. First described by Sir Francis Galton, regression to the mean is a process by which a measured observation that obtains an extreme value on one assessment will tend to obtain a less extreme value…

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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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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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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…

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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…

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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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