Li Cai Honored with FABBS Early Career Impact Award

CRESST co-director recognized by the Federation of Associations in Behavioral & Brain Sciences for major contributions to research on mind, brain, and behavior.

Li Cai, Co-Director of the National Center for Research on Evaluation, Standards, and Student Testing (CRESST), and professor of education at UCLA’s Graduate School of Education & Information Studies, has won an Early Career Impact Award from the Federation of Associations in Behavioral & Brain Sciences (FABBS). Professor Cai was nominated by the Society for Multivariate Experimental Psychology (SMEP) for his work on the development, estimation, integration and evaluation of innovative latent variable models. He will be given his award at the SMEP annual conference, to be held this October in Redondo Beach, Calif.

“It is an incredible honor and the news came as a pleasant surprise,” says Cai on receiving the FABBS Early Career Impact Award.

Cai’s methodological research agenda involves the development, integration, and evaluation of innovative latent variable models that have wide-ranging applications in educational, psychological, and health-related domains of study. He is a professor of education in the Social Research Methodology Division of UCLA Ed & IS, and is also affiliated with the UCLA Department of Psychology in the Quantitative Area.

Professor Cai has also collaborated with substantive researchers at UCLA and elsewhere on projects examining measurement issues in mental health, substance abuse treatment, and patient-reported outcomes research. In 2012, President Obama named Cai as a Presidential Early Career Scientist based on his early contributions to improved measurement methods, particularly in the area of statistical computing.

Cai says that the ability to conduct statistical research across a broad spectrum of disciplines helps him to understand their interrelation to one another and to help further the unique solutions that these disciplines pursue.

“As the eminent statistician John Tukey once said, ‘The best thing about being a statistician is that you get to play in everyone’s backyard,’” says Professor Cai. “The cross-pollination of ideas from different disciplines is a major driver of innovation in the development of applied statistics as a quantitative rational science that has integrated itself so well with the empirical sciences as we know today.”

“Li Cai is an exploding nova: cosmically brilliant, powerful, and awesome,” says Marcelo Suárez-Orozco, dean of UCLA Ed & IS. “This new honor reminds of how lucky we at GSE&IS are to have Li ‘play in our own backyard.’”

Professor Cai has developed an expansive framework for specifying and estimating nonlinear latent structure models with a comprehensive measurement model, allowing for a mix of scale types as well as missing data. He has developed effective and efficient new estimation methods for latent variable models and general item response theory (IRT) models that have seen applications in large-scale educational and psychological assessment and evaluation programs in the US and elsewhere. With regard to model evaluation, he has developed new methods for assessing fit of structural equation models and for assessing the fit of IRT models.

Cai has published extensively in outlets including Multivariate Behavioral Research, Psychological Methods, Psychometrika, and the Journal of Abnormal Child Psychology and Child Development.  He has also contributed 11 book chapters on quantitative topics.

Professor Cai earned his doctorate in quantitative psychology from the University of North Carolina at Chapel Hill in 2008. During his graduate training, Cai received the Harold Gulliksen Psychometric Research Fellowship from the Educational Testing Service as well as several outstanding dissertation awards.  He received the American Psychological Association’s Anne Anastasi Early Career Award in 2011 and the Society for Multivariate Experimental Psychology’s Raymond B. Cattell Award in 2013 for his distinguished early career contributions in multivariate methodology.

 

Photo by Ron Dietel