The Ph.D. in Survey Methodology at the University of Maryland, College Park, is an advanced degree program designed for students seeking deep expertise in the design, execution, and analysis of surveys. This interdisciplinary program bridges the fields of statistics and social sciences, equipping students with the theoretical knowledge and practical skills necessary to conduct rigorous survey research. The program aims to foster a strong foundation in the essential concepts of survey methodology, preparing graduates for impactful careers in academia, government, and the private sector.
GRE scores are not required and should not be submitted.
This program does not provide departmental assistantships or fellowships. The special tuition rate is not fully covered by graduate assistantships, fellowships, or tuition remission. Additional graduate student fees are charged.
Applicants need a Master's degree with a minimum GPA of 3.75 or an undergraduate degree with a minimum GPA of 3.5 and other evidence of outstanding potential. GRE scores are not required. Applicants must also submit English proficiency scores, with a Duolingo score of 120, IELTS of 7.0, or TOEFL score (specific score not provided).
International students pay a special tuition rate that is the same for residents and non-residents. This rate is not fully covered by assistantships or fellowships, and additional graduate student fees are charged. Please refer to the official Tuition and Fees page for current rates.
This program does not provide departmental assistantships or fellowships. There is an optional application for 'Fellowships in Support of Diversity and Inclusion' which can be submitted with the graduate school application.
You must complete the University of Maryland Graduate School online application and submit required documents, including transcripts, an essay, three letters of recommendation, and English proficiency scores. The application deadline was January 7, 2020.
Graduates are prepared for careers in survey research, statistical analysis, and data science across academia, government, and the private sector.