News and Updates
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 curve”. Although commonly used in practice, there are very important proportionality assumptions that must be met for proper interpretation of the means and variances of the latent basis factors. A recent paper by Wu and Lang (2016) clearly demonstrates that when the proportionality assumption is violated the latent basis model will force the individual trajectories to conform to the assumption and this in turn biases the model estimates. The authors recommend a strategy in which multiple alternative nonlinear functions are evaluated in addition to the latent basis models so that the optimal functional form can be identified for a given sample of data.
Wu, W., & Lang, K. M. (2016). Proportionality Assumption in Latent Basis Curve Models: A Cautionary Note. Structural Equation Modeling: A Multidisciplinary Journal, 23, 140-154.Read More
We’re often asked if there are resources that offer brief introductions to various methodological techniques that can help determine if a particular methodology might be of use in a given research application. One excellent source of introductory workshops is available through the Annual Convention for the Association for Psychological Science (APS). The 2016 APS convention will be held May 26-29 in Chicago and is offering a host of high-quality introductory workshops in a variety of methodological topics taught by many leaders in the field of methodology. Topics include introduction to R, dyadic data analysis, improving reproducibility, methodological approaches to designing adaptive interventions, Bayesian hypothesis testing, and the theory and practice of machine learning, among many others. Most workshops range between two and four hours and offer initial exposure to a number of important quantitative topics in the social and behavioral sciences.Read More
Patrick and Dan bring the same level of dedication in teaching to their workshops at Curran-Bauer Analytics, training a broad audience of researchers in the application of advanced quantitative methodology.Read More
Often the most interesting research question is not whether a relationship between two variables exists but rather under what conditions the relationship holds. Put in statistical terms, we are often less interested in main effects than we are in interactions or moderation. An interaction implies that the magnitude of the relation between two variables differs as a function of some third variable. For example, there might be a positive relation between anxiety and alcohol use in adolescents, and this is particularly salient for girls compared to boys. When an interaction exists, we must probe and plot the conditional relations to fully understand the nature of the effect. To help with this, we have collaborated with Kris Preacher (who is an Associate Professor of Psychology at Vanderbilt University) to develop a set of online utilities to help probe interactions for multiple regression, multilevel models, and latent curve models. These utilities are freely accessible and are extremely helpful in understanding a variety of interaction effects that might be encountered in practice.Read More
The differences between Bayesian and Frequentist perspectives on hypothesis testing can quickly become quite complex. We recently ran across a series of excellent essays in the APS Observer written by current Association for Psychological Science President C. Randy Gallistel that provides a gentle yet clear introduction to Bayes estimation. His highly accessible essays are titled “Bayes for Beginners”, and the first is subtitled “Probability and Likelihood”Read More