I develop and study statistical methods and models to evaluate psychological theories. My research is largely focused on structural equation models, which allow researchers to represent unobservable psychological constructs via their hypothesized relations with observed (e.g., survey) variables. Some of the questions I'm interested in are: How can we best deal with common difficult data problems, like ordinal variables and missing data? How can we plan for missing data in a way that minimizes bias and maximizes efficiency? What goes wrong when the relations between observed and latent variables are not the same as those represented by the model? And what tools can we use to diagnose these problems?
I am a post-doc in Quant Psych at UC Davis. Before joining the lab, my undergraduate studies at the University of Toronto were in cognitive psych, math, and philosophy. However, during my graduate studies at the Ludwig-Maximilians University, my research interests took a shift toward statistics and metascience, including questions such as: How do Questionable Research Practices inflate meta-analytic effect sizes, and how powerful are publication bias tests to detect this bias? What types of misconceptions do researchers fall prey to when it comes to drawing statistical inferences from data, and can these be improved? And can cross-validation techniques be used to predict the replicability of effects?
I am a third year graduate student in Quantitative Psychology at the University of California, Davis. Before joining the lab, I was a Master's student at Oakland University studying the bidirectional relationship between hormones and behavior. Currently, my research interests are mainly focused on network models specifically in improving the methods used to estimate network models.
I am a first year graduate student in the Quantitative Psychology program at University of California, Davis, and am interested in network models and longitudinal data analysis. Before joining the program, I had received my bachelor's degree in Psychology and Statistics from UC Davis. Outside of school, I enjoy attending trivia nights, baking, and rock climbing!
I am a sixth-year PhD Candidate in social psychology at the University of California, Davis, with a minor in quantitative psychology. My primary research interest is understanding how people connect abstract thinking to concrete experience. In my quantitative work, I apply this interest to issues related to statistical validity and explore how researchers can connect abstract ideals of research practice to the concrete and often messy reality of doing research. Some topics I work on are identifying the benefits and costs of using covariates ("What are potential tradeoffs when researchers 'control for' a variable?"), and power analysis in structural equation models ("How do we power our studies to detect a target effect in SEM?").
I am a third year PhD student at the University of California, Davis studying metascience, statistical and research practices, and quantitative psychology. My current projects examine how less high-profile research stakeholders, like study participants and science journalists, think about scientists’ research practices.
I am fourth year undergraduate student majoring in psychology and statistics. I started my research studying infant cognition, specifically looking at eye tracking data collected during play sessions between mothers and infants. My research interests include time series analyses, looking at ways to handle missing data, and testing research methods. I plan on applying to graduate programs in either statistics or quantitative psychology.
RIET VAN BORK
I am an assistant professor at the Psychological Methods department of the University of Amsterdam. My PhD project focused on developing methods to distinguish network models and latent variable models both empirically and theoretically. My interest in comparing these different modeling frameworks ties in with my broader interest in philosophy of statistics and the interpretation of statistical models. Some examples: What are the implications of interpreting the common factor in a common factor model as a summary of the data rather than as an underlying common cause? What are possible chance experiments that result in the response variables in a factor model or IRT model being ‘random variables’? What is actually ‘observed’ in ‘observed variables’?