- Undergraduate Program
- Graduate Program
- Shadish Memorial Fund
Quantitative psychologists create the methods used to gather data and the statistics used to analyze them.
Quantitative psychology is central to all aspects of psychology: science, education, public interest and practice. This essential role of quantitative psychology is reflected in the fact that Division 5 - Evaluation, Measurement, and Statistics - is one of the Charter Divisions of the APA.
Quantitative psychology includes research and development in a number of 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, quantitative psychologists develop new methodologies and evaluate existing methodologies to examine their behavior under conditions that exist in psychological data (e.g., with small samples). This work supports the substantive research of all areas within psychology.
At UC Merced, Psychological Sciences faculty members with interests in quantitative psychology have strengths in a wide array of topics, including Bayesian statistics, experimental and quasi-experimental design, meta-analysis, propensity score analysis, psychometric theory, structural equation modeling, hierarchical linear modeling, item response theory, longitudinal statistical modeling, sample size planning and statistics that are robust to violations of assumption.
Courses in these and related areas are available. The faculty members range in interests from the applied statistics to basic mathematical statistics. Click here for more details about the program, as well as a sample curriculum.
Selected Publications for the Quantitative Group (2015-present)
Bold font indicates Quantitative Psychology Faculty Member.
Underlined name indicates current or former PhD student at UC Merced.
- Citkowikz, M., & Vevea, J. L. (in press). A parsimonious weight function for modeling publication bias. Psychological Methods.
- Cobb., P., & Shadish, W. (2015). Abstract: Assessing trend in single case designs using generalized additive models. Multivariate Behavioral Research, 50, 131.
- Coburn, K. M., & Vevea, J. L. (2015). Publication bias as a function of study characteristics. Psychological Methods, 20, 310-330.
- Depaoli, S., & Clifton, J. P. (2015). A Bayesian approach to multilevel structural equation modeling with continuous and dichotomous outcomes. Structural Equation Modeling, 22, 327-351.
- Depaoli, S., Clifton, J. P., & Cobb, P. R. (2016). Just Another Gibbs Sampler (JAGS): Flexible software for MCMC implementation. Journal of Educational and Behavioral Statistics, 6, 628-649.
- Depaoli, S., & Liu, Y. (accepted). Review: Bayesian Psychometric Modeling. Psychometrika.
- Depaoli, S., Rus, H. M., Clifton, J. P., van de Schoot, R., & Tiemensma, J. (2016). An introduction to Bayesian statistics in Health Psychology. Health Psychology Review. [online first]
- Depaoli, S., & van de Schoot, R. (2016). Improving transparency and replication in Bayesian statistics: The WAMBS-checklist. Psychological Methods. [online first]
- Depaoli, S., van de Schoot, R., van Loey, N., & Sijbrandij, M. (2015). Using Bayesian statistics for modeling PTSD through latent growth mixture modeling: Implementation and discussion. European Journal of Psychotraumatology, 6, 27516.
- Depaoli, S., Yang, Y., & Felt, J. (2017). Using Bayesian statistics to model uncertainty in mixture models: A sensitivity analysis of priors. Structural Equation Modeling, 24, 198-215.
- Epperson, A., Depaoli, S., Song, A. V., Wallander, J. L., Elliott, M., Cuccaro, P., Tortolero, S., & Schuster, M. (2016). Perceived physical appearance: Assessing measurement equivalence in Black, Latino, and White adolescents. Journal of Pediatric Psychology. [online first]
- Felt, J. M., Depaoli, S., Andela, C. D., Pereira, A. M., Biermasz, N. R., Kaptein, A. A., & Tiemensma, J. (2016). Using the Common Sense Model of illness perceptions to better understand the impaired quality of life of patients treated for neuroendocrine diseases. In Neuroendocrinology (pp. SAT-502). Endocrine Society.
- Felt, J. M., Depaoli, S., Pereira, A. M., Biermasz, N. R., & Tiemensma, J. (2015). Total score or subscales in scoring the Acromegaly Quality of Life Questionnaire: Using novel confirmatory methods to compare scoring options. European Journal of Endocrinology, 173, 37-42.
- Felt, J. M., Depaoli, S., Pereira, A. M., Biermasz, N. R., & Tiemensma, J. (2015). Using novel confirmatory statistical methods to compare scoring options of the Acromegaly Quality of Life (AcroQoL) Questionnaire. In Acromegaly (pp. PP09-3). Endocrine Society.
- Konijn, E. A., van de Schoot, R. Winder, S. D., & Ferguson, C. J. (2015). Possible solution to publication bias through Bayesian statistics, including proper null hypothesis testing. Communication Methods and Measures, 9, 280-302.
- Lai, K., & Green, S. B. (2016). The problem of having two watches: Assessment of model fit when RMSEA and CFI disagree. Multivariate Behavioral Research, 51, 220-239.
- Lai, K., Green, S. B., Levy, R., Xu, Y., Yel, N., Thompson, M. S., Eggum-Wilkens, N. D., Kunze, K., Iida, M., Reichenberg, R., & Zhang, L. (2016). Assessing model similarity in structural equation modeling. Structural Equation Modeling, 23, 491-506.
- Liu, Y., & Hannig, J. (2016). Generalized fiducial inference for binary logistic item response models. Psychometrika, 81, 290-324.
- Liu, Y., & Hannig, J. (in press). Generalized fiducial inference for logistic graded response models. Psychometrika.
- Liu, Y., Magnus, B. E., & Thissen, D. (2016). Modeling and testing differential item functioning in unidimensional binary item response models with a single continuous covariate: A functional data analysis approach. Psychometrika, 81, 371-398.
- Magnus, B. E., Liu, Y., He, J., Quinn, H., Thissen, D., Gross, H. E., DeWalt, D. A., and Reeve, B. B. (2016). Mode effects between computer self-administration and telephone interviewer-administration of the PROMIS pediatrics measures, self-and proxy report. Quality of Life Research, 25, 1655-1665.
- Maydeu-Olivares, A., & Liu, Y. (2015). Item diagnostics in multivariate discrete data. Psychological Methods, 20, 276-292.
- Montoya, M., Horton, R., Vevea, J. L., & Citkowicz, M. (in press). A re-examination of the mere exposure effect: The influence of repeated exposure on recognition, familiarity, and liking. Psychological Bulletin.
- Moore, T. M., Reise, S. P., Depaoli, S., & Haviland, M. G. (2015). Iteration of partially specified target matrices in exploratory and Bayesian confirmatory factor analysis. Multivariate Behavioral Research, 50, 149-161.
- Pustejovsky, J., Hedges, L. V., & Shadish, W. R. (2015). Design-comparable effect sizes in multiple baseline designs: A general modeling framework. Quality Control and Applied Statistics, 60, 367-370.
- Reeve, B. B., Thissen, D., DeWalt, D. A., Huang, I.-C., Liu, Y., Magnus, B., Quinn, H., Gross, H. E., Kisala, P. A., Ni, P., Haley, S., Mulcahey, M., Charlifue, S., Hanks, R. A., Slavin, M., Jette, A., and Tulsky, D. S. (2016). Linkage between the PROMIS pediatric and adult emotional distress measures. Quality of Life Research, 25, 823-833.
- Scott, S., Wallander, J., Depaoli, S., Grunbaum, J., Tortolero, S. R., Cuccaro, P. M., Elliott, M. N., & Schuster, M. A. (2015). Gender role orientation and health-related quality of life among African American, Hispanic, and White youth. Quality of Life Research, 24, 2139-2149.
- Selewski, D. T., Troost, J. P., Massengill, S. F., Gbadegesin, R. A., Greenbaum, L. A., Shatat, I. F., Cai, Y., Kapur, G., Hebert, D., Somers, M. J., Trachtman, H. Pais, P., Seifert, M. E., Goebel, J. Sethna, C. B., Mahan, J. D., Gross, H. E., Herreshoff, E., Liu, Y., Song, P. X., Reeve, B. B., DeWalt, D. A., and Gipson, D. S. (2015). The impact of disease duration on quality of life in children with nephrotic syndrome: A midwest pediatric nephrology consortium study. Pediatric Nephrology, 30, 1467-1476.
- Shadish, W. R. (2015). Introduction to the special issue on the origins of modern meta-analysis. Research Synthesis Methods, 6, 219-220.
- Shadish, W. R. & Lecy, J. D. (2015). The meta-analytic big bang. Research Synthesis Methods, 6, 246-264.
- Shamseer, L., Sampson, M., Bukutu, C., Nikles, J., Tate, R., Johnson, B. C., Zucker, D. R., Shadish, W., Kravitz, R., Guyatt, G., Altman, D. G., Moher, D., Vohra, S., & the CENT Group. (2015). CONSORT extension for N-of-1 Trials (CENT) 2015: Explanation and elaboration. British Medical Journal, 350, h1783.
- Shadish, W. R., Zelinsky, N. A. M., Vevea, J. L., & Kratochwill, T. R. (2016). A survey of publication preferences of single-case design researchers when treatments have small or large effects. Journal of Applied Behavior Analysis. Currently pre-published on the web; print version due in fall issue.
- Sullivan, K. J., Shadish, W. R., & Steiner, P. M. (2015). An introduction to modeling longitudinal data with generalized additive models: Applications to single-case designs. Psychological Methods, 20, 26-42.
- Thissen, D., Liu, Y., Magnus, B., & Quinn, H. (2015). Extending the use of multidimensional IRT calibration as projection: Many-to-one linking and linear computation of projected scores. In L. A. van der Ark, D. M. Bolt, W. C. Wang, J. A. Douglas, & S. M. Chow (Eds.), Quantitative Psychology Research. Springer, New York, N.Y.
- Thissen, D., Liu, Y., Magnus, B., Quinn, H., Gipson, D. S., Dampier, C., Huang, I.-C., Hinds, P. S., Selewski, D. T., Reeve, B. B., Gross, H. E., & DeWalt, D. A. (2015). Estimating minimally important difference (MID) in PROMIS pediatric measures using the scale-judgment method. Quality of Life Research, 25, 13-23.
- Tiemensma, J., Depaoli, S., & Felt, J. M. (2016). Using subscales when scoring the Cushing's Quality of Life Questionnaire. European Journal of Endocrinology, 174, 33-40.
- van de Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S., & Vermunt, J. K. (2016). The GRoLTS-Checklist: Guidelines for reporting on latent trajectory studies. Structural Equation Modeling, 1-17. [online first]
- van de Schoot, R., Winter, S., Zondervan-Zwijnenburg, M., Ryan, O., & Depaoli, S. (in press). A systematic review of Bayesian applications in psychology: The last 25 years. Psychological Methods.
- Varni, J. W., Thissen, D., Stucky, B. D., Liu, Y., Magnus, B., He, J., DeWitt, E. M., Irwin D. E., Lai, J.-S., Amtmann, D., & DeWalt, D. A. (2015). Item-level information discrepencies between children and their parents on the PROMIS pediatric scales. Quality of Life Research, 24, 1921-1937.
- Vevea, J. L., & Coburn, K. M. (2015). Maximum-likelihood methods for meta-analysis: A tutorial using R. Group Processes and Intergroup Relations, 18, 329-347. (Invited Paper.)
- Wang, X., Liu, Y., & Hambleton, R. K. (2017). Detecting item preknowledge using a predictive checking method. Applied Psychological Measurement, Advance online publication.
- Zelinsky, N. A. M., & Shadish, W. R. (2016). A demonstration of how to do a meta-analysis that combines single-case designs with betwee-groups experiments: The effects of choice making on challenging behaviors performed by people with disabilities. Developmental Neurorehabilitation.
The first new American research
university in the 21st century, with a
mission of research, teaching and service.
university in the 21st century, with a
mission of research, teaching and service.
University of California, Merced
5200 North Lake Road
Merced, CA 95343
T: (209) 228-4400