Quantitative psychology refers to the development, testing, and application of statistics to psychological research. Statistics are a tool that psychology researchers use to make arguments for or against the existence of phenomena based on hypothesis testing.
Faculty mentors in the Quantitative Methods track differ regarding whether they expect students to work on projects focused on statistical development and testing versus statistical application (see more information about the distinction below).
Students interested this track are expected to contact a potential faculty mentor with expertise in quantitative methods before applying to the program to determine whether they will be accepting students and their expectations for a thesis project.
Development and Testing
Some Quantitative Methods track students will complete thesis and other research projects that develop and test a new statistic. These students work with faculty mentors who tend to identify primarily as quantitative psychologists or methodologists. They work on projects that create a new statistical tool to test a hypothesis and will also examine whether a new statistic outperforms another one.
For example, one student in this track developed and tested different forms of a t-test. She identified the conditions (e.g., sample size, effect size) wherein one method would outperform another, and then provided general recommendations to psychology researchers based on the conditions they would face in their own research. The project mainly required computer coding and data simulation.
Some Quantitative Methods track students will complete thesis and other research projects that apply advanced statistical methods to answer research questions in their content area. These students work with faculty mentors who tend to identify primarily as social or industrial-organizational psychologists but use advanced statistical methods in their research programs.
For example, students may use factor analysis techniques to validate a new measure of social stigma or seek to understand work-family conflict differences across cultures using multi-level modeling and measurement invariance techniques. These projects required data collection using newly developed surveys or use archival data from large organizational databases.