Lauren Trichtinger

Lauren Trichtinger is an Assistant Professor of Statistics and Data Science at Simmons University. Lauren earned her PhD in Quantitative Psychology from the University of Notre Dame. Her research interests include developing statistical methods for analyzing big data, particularly intensive longitudinal data.
 

Area of Expertise

• Times Series Analysis
• Dimension Reduction Techniques
• Quantitative Methods for Psychological Research

Courses

• STAT118 Introductory Statistics
• STAT227 Intermediate Statistics: Design & Analysis
• STAT228 Introduction to Data Science
• STAT391 Special Topics in Statistics and Biostatistics
• CS347 Applied Data Science

Publications/Presentations

Selected Publications

Liu, Q., Joiner, R. J., Trichtinger, L. A., Tran, T., & Cole, D. A. (2023) Dissecting the depressed mood criterion in adult depression: The heterogeneity of mood disturbances in major depressive episodes. Journal of Affective Disorders. 323, 392–399. doi: 10.1016/j.jad.2022.11.047

Zhang, G., Hattori, M., Trichtinger, L. A. (2022). Rotating factors to simplify their structural paths. Psychometrika. doi: 10.1007/s11336-022-09877-3

Zhang, G., Trichtinger, L. A., Lee, D. & Jiang, G. (2022) PolychoricRM: An efficient R function for estimating polychoric correlations and their asymptotic covariance matrix. Structural Equation ModelingStructural Equation Modeling: A Multidisciplinary Journal, 29:2, 310-320, DOI: 10.1080/10705511.2021.1929996

Trichtinger, L. A. & Zhang, G. (2021). Testing P-technique factor analysis with non-normal time series. Multivariate Behavioral Research, DOI: 10.1080/00273171.2021.1919047.

Trichtinger, L. A. & Zhang, G. (2021). Quantifying the model error in P-technique factor analysis. Multivariate Behavioral Research, doi: 10.1080/00273171.2020.1717414

Professional Affiliations & Memberships:

• Psychometric Society
• American Psychological Association
• Caucus for Women in Statistics
• Psi Chi

Research/Special Projects

Trichtinger's research interests are in models for intensive longitudinal data and data reduction techniques such as exploratory factor analysis and principal component analysis. Her dissertation investigated different methods for handling missing data in dynamic factor analysis.