Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

An 8-Hour Livestream Seminar Taught by Bethany Bray, Ph.D. and Stephanie Lanza, Ph.D.

Latent class and latent profile analysis (LCA and LPA) have proven to be useful tools for researchers in the social, behavioral, and health sciences to understand hidden structures and patterns in their data. For example, they enable researchers to discover subgroups of participants who share similar patterns of behaviors or attitudes. LCA and LPA can also provide a more nuanced understanding of the ways in which intersecting behaviors confer higher risk of adverse outcomes. Latent transition analysis (LTA) extends LCA and LPA for use with longitudinal data, so that researchers can examine incidences of transitions in subgroup membership over time.

LCA and LPA can be viewed as special kinds of structural equation models in which the latent variables are categorical rather than continuous. These methods can uncover hidden structures and patterns related to multidimensional phenomena. LCA and LPA were originally developed to measure static, categorical, latent constructs. That is, they were developed to measure constructs that do not change over time or constructs measured at only one occasion. However, developmental questions about change over time in multidimensional phenomena measured as categorical latent constructs can be addressed by examining incidences of transitions overtime in subgroup membership (i.e., class or profile membership). This method is known as LTA.

Using LTA to model change over time in complex, multidimensional latent constructs can help researchers achieve a more comprehensive understanding of the developmental phenomena under investigation. Applying this method to empirical data can inform theory, contribute to evidence-based decision-making, and shed light on heterogeneity in the effects of interventions. Ultimately, LTA empowers researchers in the social, behavioral, and health sciences to gain new insights from their longitudinal data and contribute innovative findings that advance science.

This seminar will give you the theoretical background and applied skills to address interesting research questions using LTA applied to longitudinal panel data. Topics include model identification, model selection, model interpretation, measurement invariance across time, multiple groups models, and predicting transitions over time in subgroup membership, as well as comparing LTA to other longitudinal models for panel data (e.g., growth curve models, growth mixture models). The format will combine lectures, software demonstrations, computer exercises, and discussion. There will be opportunities to discuss how LTA can be applied in your research.