As the field of psychology rapidly advances, it is evident that quantitative methods are integral to psychological research. Knowledge of advanced statistical techniques opens new pathways to the study of human behavior by allowing scientists to examine hypotheses using a new framework or paradigm. The goal of the Minor in Quantitative Methods (MQM) is to train individuals in some of the recent developments of statistical science, and the application of these developments to real-world data. One unique feature of the MQM is the focus on the application of quantitative methods; although each faculty member has expertise in an area of statistics, he or she also conducts research in a substantive area of psychology. The MQM is intended for students who intend to pursue academic careers, wherein applied research is highly valued. Students who are likely to teach undergraduate or graduate courses in statistical methods may also find the minor helpful.
The courses required for the MQM are intended to supplement, rather than compete with, studentsâ€™ training in substantive areas of research. Therefore, the MQM requires only 15 credits of quantitative coursework. Six credits are earned by successful completion of the 1st year quantitative psychology sequence (PSYC61651/61654) which is required of all graduate students in the department. Students then must successfully complete three additional courses (nine credits) beyond the first year sequence, which may also count as departmental electives. Advanced courses that have been offered in the past include Multivariate Statistics, Longitudinal Data Analysis, Non-normal Data Analysis, and Hierarchical Linear Modeling. Students may also choose to complete courses from other departments, pending approval of the quantitative faculty. In addition, from time to time, the Coordinating Center for Quantitative Methodology has offered quantitative workshops that many of our graduate students take.
To encourage the application of quantitative training to real-world data sets, students have the option of replacing on of the three advanced electives with an independent research project (3 credits). This project must have a quantitative focus, and must be approved by all of the quantitative faculty members; the supervisor of the project, however, can be any of the KSU Psychology faculty. Prior to engaging in the project, students should consult with both their advisor and a quantitative faculty member regarding the topic of this independent research project. A brief written proposal (1-2 pages) must be approved by the studentâ€™s advisor and the quantitative faculty before the student can register for this research project. Successful completion of this project will allow the students to waive one elective course.
Psychologists with strong quantitative skills are often expected to disseminate statistical knowledge to their colleagues; thus, students who pursue the MQM must have at least one practicum experience. This practicum experience can be as an instructor of an undergrad course (Quantitative Statistical Analysis I/II), being the teaching assistant for the first year graduate sequence, or as a statistical consultant in collaboration with a quantitative faculty member. For this latter option, the practicum experience will be arranged around the student's other program requirements to facilitate completion of the practicum experience as well as other program requirements. The consulting experience is expected to be of comparable effort to other practicum experiences, and the student's quantitative mentor (Ciesla, Flessner, van Dulmen) must certify that this requirement has been fulfilled. However, the overall time commitment may vary from student to student.
Students who complete the MQM should complete a thesis/dissertation that has a strong quantitative component. Specifically, students should demonstrate that they are able to appropriately conduct and interpret sophisticated statistical analyses. Note, however, that this requirement does not necessarily suggest that students need to develop a new statistical technique or methodology.
For more information contact Coordinator Dr. Jeffrey Ciesla at email@example.com.