I am an Assistant Professor in the Methodology and Statistics Department at Leiden University, studying how to leverage advanced quantitative methods to understand cognitive variability and validate statistical inferences in longitudinal and psychometric models.
My research and teaching focus on the development of longitudinal and psychometric models for addressing research hypotheses in the fields of psychology, cognitive neuroscience, and allied fields - especially as they relate to change over time. My current quantitative work focuses on improving methods for predicting the long-term consequences for individual differences in developmental trajectories and on improving measurement diagnostics for composite variable - variables that result from some combination of individual items - in the context of mediation analyses. My substantive background and interests are in understanding how behavior, and associated neural processes, change over time in response to experience and maturation. I believe that these two areas of work are mutually beneficial and jointly inform the direction of my future research.
I use a variety of modeling frameworks in this work, including psychometric and longitudinal versions of multilevel and structural equation models, computational modeling, and both uni- and multi-variate methods in functional neuroimaging.
Download my current CV.
News
New Preprint: I have a new prepring
detailing an approach for fitting longitudinal growth models
with interpretatble parameters using linear estimation with
nonlinear inference. Interested readers can find the
preprint here, and
the leni
R-package is available on Github for
estimating the models described in the preprint.
New Preprint: With co-author Dan Bauer, I have a new preprint deriving relationships between different version of TVC models in longtiudinal analysis, with implications for mediation with change scores. Interested readers can find the preprint here.
New Preprint: With co-authors Patrick Curran and Greg Hancock, I have a new preprint deriving time coding effects in latent growth models with distal outcomes, focusing on how to obtain time-invariant and interpretable predictive effects. Interested readers can find the preprint here.
New Preprint: With co-author Sam Parsons, I have a new preprint quantifying the poor recovery of individual trajectory parameters using two time point models. Interested readers can find the preprint here.
New Preprint: With co-authors Sophia Borgeest and Rogier Kievit, I have a new preprint tackling the issue of interrupted mediation with composite mediatiors. Interested readers can find the posted preprint here.
Now Available: Our R-based code companion to the preprint McCormick, Byrne, Flournoy, Mills, & Pfeifer, (preprint). Here we provide the syntax to accomplish the models we discussed in the main text. Sprinkled in are tips and tricks for the best way to fit various model options! Check it out here!