The doctoral program in Quantitative Methods and Evaluation (QME) focuses on methods for designing studies and analyzing data for two interrelated forms of behavioral and social change: (a) change that comes about due to naturally occurring developmental processes; and (b) change that is instigated through deliberate intervention strategies and experimentation.

In both arenas, an integrated approach to the analysis of change is emphasized that involves in-depth consideration of measurement, research design, statistical theory, principles of data analysis, research synthesis and the reporting of findings. In particular, the program focuses on development and application of statistics, measurement, and research design to applied practical problems in social research generally, with specific emphasis on problems in psychology, education and program evaluation.

Research in QME falls into three categories:

• Naturally Occurring Change. Our emphasis is on longitudinal designs and statistical approaches to modeling developmental phenomena. Current coursework, projects, and faculty interests, for example, deal with (a) application of hierarchical linear and nonlinear models to assess change in psychiatric and educational settings; (b) identifying individual differences in patterns of change over time; (c) a longitudinal study on the optimal development of talent; and (d) precursors and predictors of antisocial behavior.
• Instigated Change. Our focus on the analysis of instigated change (e.g., the effects of policies, programs, interventions) highlights the development and use of state-of-the-art experimental and quasi-experimental field methods. Here, current coursework, projects, and faculty interests include (a) experimental and quasi-experimental evaluations of changes in systems of mental health care children and adolescents; (b) multi-site experimental evaluation of interventions to assist homeless substance abusers; (c) policy-driven meta-analysis of welfare-to-work experiments, examining the relative effects of alternative models, context, and implementation levels; (d) explanatory meta-analysis of the effectiveness of juvenile delinquency interventions; and (e) assessment of individual differences, developmentally-tailored educational interventions, and trait by treatment interactions pertaining to talent development.
• Research on Methods Themselves. Our focus here is on the development of innovative new methods for analyzing change, as well as basic research on the effectiveness of methods that are already available. This research combines Monte Carlo simulation, meta-analysis, and new developments in statistical theory. Our goal is to foster a continuous improvement in social science methods. Examples of current coursework, projects, and faculty interests include (a) evaluation of specific structural equation models for analyzing change; (b) development of innovative methodologies for assessing the verisimilitude of structural equation models; (c) development of taxometric methods; (d) confidence interval methods for evaluating the fit of statistical models; and (e) application of item response theory to measurement of personality and psychopathology.

Through a combination of experiences, we expect our graduates to have gained unusually good quantitative methods skills and exposure to state-of-the-art concepts in areas related to studying and analyzing change. These experiences include (a) substantial course work pertaining to theory and application in statistics, measurement, evaluation, and research design; (b) research and consultation experiences with faculty and peers on actual research projects conducted in diverse settings; and (c) guidance on effective techniques for teaching relevant courses at the undergraduate and graduate levels.

Students select one of two major emphases or tracks within the program: (a) statistical theory, measurement and applications, or (b) field research designs, program evaluation and methods. By specifying options within and across each block of courses corresponding to the tracks, students can tailor their coursework to meet their own career needs.

One year of calculus is ideal for students applying to the quantitative methods program. Students not having this background will be expected to take advantage of remedial opportunities before graduating with a Ph.D. in quantitative methods.

For more information please contact the program director: James H. Steiger (james.h.steiger@vanderbilt.edu).