How to Prepare for Doctoral Study
in Quantitative Psychology
Once a student has decided that quantitative psychology may be an area of possible doctoral study, the APA Task Force for Increasing the Number of Quantitative Psychologists advises students to consider the following academic and research experiences to prepare for the doctoral programs in North America. It is important to note that doctoral programs in quantitative psychology vary in the background and experience that they require for their applicants, so it is important to visit individual program websites for specific requirements.
Wherever possible, it is useful to identify a faculty member who has quantitative interests to supervise and mentor during the preparation for graduate school. At many schools this may be difficult because there are no faculty whose primary focus is on quantitative methods. If this is the case, it is useful to align oneself with a faculty mentor who is an active researcher from whom one can receive advice and gain experience as a research apprentice.
Coursework in Mathematics and Statistics in Undergraduate School
Among the frequently asked questions of undergraduate students who are considering the field of quantitative psychology is what level of mathematics training is required. In fact, this answer varies widely by quantitative program. Quantitative programs do not typically state minimum mathematics and statistics requirements for admission. Yet, admissions committees carefully review transcripts for evidence that the applicant has studied mathematics and/or statistics and has talent in this area. It is useful, and for admission to some quantitative programs essentially mandatory, that students have successfully completed a college-level calculus series. A course in linear algebra is also often useful.
A sustained interest in mathematics and/or statistics is important. Many undergraduates place out of college mathematics courses through advanced placement tests and coursework completed during high school. It is also possible that students have pursued majors during college that require few mathematics requirements. If this is the case, it is still important show evidence that there is an adequate interest and good performance in mathematics.
Coursework in mathematics in graduate school
Some quantitative graduate programs will encourage applicants, once accepted to the doctoral program, either to brush up or strengthen their math abilities by taking courses in the math, statistics, or biostatistics departments. In addition to calculus and linear algebra, math courses might include advanced calculus and multivariate calculus. Statistics courses might include introduction to statistics, statistical methods and theory, numerical methods, statistical linear models, or probability.
Undergraduate coursework in quantitative methodology in psychology
There are courses in the psychology department that would be very useful to take prior to applying to a quantitative doctoral program. The number of these courses available within the department of psychology varies widely across universities. They may consist of courses covering introductory statistics, advanced undergraduate statistics, tests and measurement, and research methods. Sometimes these courses are offered in other departments as well. Quantitative admissions committees will expect students to show excellent performance in the following types of psychology courses: Statistics for the behavioral sciences, research methods, tests and measurements (psychometric theory), and any advanced psychological statistics course that is offered.
Taking graduate methodology courses as an undergraduate.
Some psychology departments will allow (with permission) qualified undergraduates to enroll in graduate-level statistics courses such as a two-semester first year doctoral sequence or upper-level quantitative courses (e.g., multivariate analysis, structural equation modeling, and psychometric theory). These experiences are extremely valuable and demonstrate to an admissions committee a student’s ability to perform as a graduate student.
Independent research project
If there is an opportunity to conduct an honors thesis, capstone experience, or master’s thesis, this independent research is an excellent way to demonstrate the ability to conceptualize a research problem, select an appropriate design, obtain data, conduct analyses, and report findings. Honors theses that involve data-intensive experiences involving statistical/quantitative modeling are strongly encouraged.
Data-intensive research experience
Another important experience that can help an undergraduate or master’s student prepare for a quantitative program is simply joining a professor’s laboratory where there is a chance for significant involvement in a research project (e.g., as a research assistant). While research participation is always useful, for a doctoral program in quantitative a student’s involvement should ideally be focused on the data component of the research, including assisting with research design, item writing, data management, planning for statistical analyses, conducting analyses using common and specialized statistical software, and reporting findings.
Other valuable experience
The following additional activities are useful ways to help an admissions committee see a student’s commitment to quantitative. Sometimes there are opportunities to participate in additional educational experiences, such as multi-day workshops in quantitative methods, that are offered either at one’s own institution, prior to a national conference, or in some other forum. These workshops do not substitute for a course, but they provide a valuable overview of quantitative area. Students are encouraged to present their research (preferably with a quantitative emphasis) at a professional conference and/or publish their research. Most often, it is expected that these professional activities will be conducted under the supervision of a mentor or research advisor. Some students have the opportunity to be a paid (or unpaid) teaching assistant for a research methods course, a statistics course, or other related course.
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