Quantitative Methods, Measurement, and Statistics is central to all aspects of social and behavioral sciences: science, education, public interest, and practice. This essential role of quantitative methods is reflected in the fact that Division 5 - Evaluation, Measurement, and Statistics - is one of the Charter Divisions of the APA.
QMMS program includes research and development three broad areas: measurement, research design and statistical analysis (see Aiken, West, Sechrest & Reno, 1990), as well as mathematical and statistical modeling of psychological processes.
Within each area, faculty in the QMMS program develop new methodologies and evaluate existing methodologies to examine their behavior under conditions that exist in behavioral science data (e.g., with small samples). This work supports the substantive research of all areas within social and behavioral sciences.
Faculty in the QMMS program have strengths in a wide array of topics, including Bayesian statistics, experimental and quasi-experimental design, measurement and psychometric theory, structural equation modeling, social network theory, missing data analysis, hierarchical linear modeling, item response theory, longitudinal statistical modeling, sample size planning, etc.
In addition to the core coursework, students interested in quantitative psychology are encouraged to take the following courses:
- PSY 202c: Multivariate Statistics
- PSY 203: Multilevel Modeling
- PSY 205: Measurement Theory and Psychometrics
- PSY 207: Structural Equation Modeling
- PSY 209: Longitudinal Data Analysis and Bayesian Extensions
- PSY 210: Item Response Theory
- PSY 213: Mathematical Toolbox for Quantitative Psychology
- PSY 215: Essential Mathematics for Quantitative Social Research
- PSY 290: Statistical Computing
- PSY 290: Bayesian Statistics
- PSY 290: Missing Data Analysis
Additional specialized courses will be offered within this area. Students should work with their faculty mentors to select appropriate courses that can provide the best foundations for their research. This may include taking courses in other specialties within Psychological Sciences and courses offered by other programs or by other UC campuses offering courses in quantitative methods.
Students who are interested in quantitative psychology can also take substantive psychology courses in another area of psychology (e.g., developmental, health). This serves two purposes. First, it ensures a minimal level of contact with the field of psychology, commensurate with getting a doctorate in psychology. Second, it can increase the marketability of quantitative psychologists by demonstrating the ability to talk to faculty members in substantive areas such as developmental psychology or health psychology.
Representative Publications for QMMS Faculty
Bold font indicates QMMS Faculty Member.
- Citkowicz, M., & Vevea, J.L. (2017). A parsimonious weight function for modeling publication bias. Psychological Methods, 22, 28-41.
- Coburn, K.M., & Vevea, J.L. (2015). Publication bias as a function of study characteristics. Psychological Methods, 20 310-30.
- (2014). The impact of inaccurate “informative” priors for growth parameters in Bayesian growth mixture modeling. , 239-252.
- (2013). Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation. , 186-219.
- (2012). The ability for posterior predictive checking to identify model mis-specification in Bayesian growth mixture modeling. , 534-560.
- (2012). Measurement and structural model class separation in mixture-CFA: ML/EM versus MCMC. , 178-203
- , and Clifton, J. (2015). A Bayesian approach to multilevel structural equation modeling with continuous and dichotomous outcomes. 327-351
- , Lai, K, and Yang, Y. (2020). Bayesian model averaging as an alternative to model selection for multilevel models.
- , Rus, H., Clifton, J., van de Schoot, R., and Tiemensma, J. (2017). An introduction to Bayesian statistics in health psychology. 248-264.
- , and van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-checklist. 240-261.
- , Winter, S. D., Lai, K., & Guerra-Peña, K. (2019). Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration. .
- , Yang, Y., and Felt, J. (2017). Using Bayesian statistics to model uncertainty in mixture models: A sensitivity analysis of priors. Journal, , 198-215.
- Lai, K. (in press). Using information criteria under missing data: Full information maximum likelihood versus two-stage estimation. Structural Equation Modeling.
- Lai, K. (2020). Correct estimation methods for RMSEA under missing data. Structural Equation Modeling. Advance online publication.
- Lai, K. (2020) Confidence interval for RMSEA or CFI difference between nonnested models. Structural Equation Modeling, 27, 16-32.
- Lai, K. (2019). Correct point estimator and confidence interval for RMSEA given categorical data. Structural Equation Modeling. Advance online publication.
- Lai, K. (2019). Creating misspecified models in moment structure analysis. Psychometrika, 84, 781-801.
- Lai, K. (2019). More robust standard error and confidence interval for SEM parameters given incorrect model and nonnormal data. Structural Equation Modeling, 26, 260-279.
- Lai, K., Green, S. B., & Levy, R. (2017). Graphical displays for understanding SEM model similarity. Structural Equation Modeling, 24, 803-818.
- Lai, K., & Green, S. B. (2016). The problem with having two watches: Assessment of fit when RMSEA and CFI disagree. Multivariate Behavioral Research, 51, 220-239.
- Qu, W., Liu, H., & Zhang, Z. (2020). A method of generating multivariate non-normal random numbers with desired multivariate skewness and kurtosis. Behavior Research Methods, 52, 939-946.
- Rhemtulla, M., Jia, F., Wu, W., & Little, T. D. (2014). Planned missing designs to optimize the efficiency of latent growth parameter estimates. International Journal of Behavioral Development, 38(5), 423-434.
- Wu, W., & Jia, F. (2013). A new procedure to test mediation with missing data through nonparametric bootstrapping and multiple imputation. Multivariate Behavioral Research, 48(5), 663-691.
- Vevea, J.L. & Coburn, K.M. (2019). Publication Bias. In Valentine, J., Cooper, H., & Hedges, L.V., The Handbook of Research Synthesis and Meta-Analysis (3rd Edition). New York: Russel Sage Foundation.