Lauren Trichtinger is an Assistant Professor of Statistics and Data Science at Simmons University. Lauren earned her Ph.D. 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
What I Teach
- 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.