About
Sierra Bainter is a quantitative psychologist whose research is focused on improving areas where available quantitative methods may not be adequate for real psychological data or where a research question may not be addressed using standard analysis techniques.
Education
| 2016 | Ph.D. , University of North Carolina at Chapel Hill |
| 2010 | B.A. Psychology, Arizona State University |
Professional Experience
| 2016 - | Assistant Professor, University of Miami |
Research Interests
My program of methodological research is focused on two key issues; these are improving areas where available methods may not be adequate for real psychological data, or where a research question may not be addressed using standard analysis techniques. Specifically, I investigate Bayesian estimation as a tool to help overcome estimation difficulties in structural equation models. I also focus on improving the match between statistical models and psychological theory, because mismatch between model and theory can obscure our understanding of psychological processes and influences.
Please do not hesitate to contact me if you are interested in the kinds of methodological research I do.
Selected Publications
Bainter, S. A., McCauley, T. G., Tor, W., & Losin, E. R. (in press). Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain. Forthcoming in Advances in Methods and Practices in Psychological Science.
supplemental materials: https://osf.io/m8djx/
Bolt, T., Nomi, J. S., Bainter, S. A., Cole, M. W., Uddin, L. Q. (2019). The situation or the person? Individual and task-condition differences in task-evoked activity. Human Brain Mapping, advance online publication.
Bainter, S.A., & Forster, D. (2018). The impact of moderate priors for Bayesian estimation and testing of item factor analysis models when Maximum Likelihood is unsuitable. Structural Equation Modeling: A Multidisciplinary Journal, 26(1), 80-93.
Bainter, S.A.(2017). Bayesian estimation for item factor analysis models with sparse categorical indicators. Multivariate Behavioral Research, 52(5), 593-615.
Bainter, S.A. & Howard, A.L. (2016). Comparing multivariate longitudinal models with time-linked effects. Developmental Psychology, 52(12), 1955-1968. [MPlus Scripts]
Bainter, S.A. (2015). Advantages of a Bayesian approach for examining class structure in finite mixture models [Abstract]. Multivariate Behavioral Research, 50(1), 127.
Bainter, S.A. & Bollen, K.A. (2015). Moving forward in the debate on causal indicators: rejoinder to comments. Measurement: Interdisciplinary Research and Perspectives, 13(1), 63-74.
Tracy, E.C., Bainter, S.A., & Satariano, B.S. (2015). Judgments of self-identified gay and heterosexual male speakers: Which phonemes are most salient in determining sexual orientation? Journal of Phonetics, 52, 13-25.
Bainter, S.A. & Bollen, K.A. (2014). Interpretational confounding or confounded interpretations of causal indicators? Measurement: Interdisciplinary Research and Perspectives, 12(4), 125-140.
Bainter, S.A., & Curran, P.J. (2014). The promise of integrative data analysis for developmental science. Journal of Cognition and Development, 16, 1-10
Curran, P.J., Howard, A.L., Bainter, S.A., Lane, S., Burfeind, C., & McGinley, J. (2014). The separation of between-person and within-person components of individual change over time: a latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82(5), 879-894.
