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Public Policy and Governance

Contact the Evans School of Public Policy and Governance for enrollment information.


The main goals of the PhD concentration in statistics are to provide students with applied quantitative and statistical skills in Public Policy and Management, and with tools for carrying out quantitative research. The track is largely built around a curriculum developed by the Center for Statistics and the Social Sciences (CSSS; course code: CS&SS). Students who complete the Statistics Concentration will have advanced training in statistics for social science research relevant to their own research needs. Taking a coherent set of CSSS courses will expose students to the cutting edge of statistics and the social sciences.

Certification Requirements Coherent Set Of Four Courses In Social Statistics

Students will take a set of four courses in social statistics (chosen primarily from the list below). The student will submit a list of the courses to the Ph.D. faculty program coordinator for approval. These courses must be more advanced than any required for a Ph.D. degree in Public Policy and Management. These courses should be selected to form a coherent concentration in social statistics.

The advanced courses offered by CSSS will automatically qualify for the concentration. For example, CSSS currently offers courses in hierarchical models, Bayesian methods, event history analysis, analysis of social networks, survey research methods, and others. In addition, relevant courses in Public Affairs, Statistics, Biostatistics, Anthropology, Economics, Political Science, and Sociology may be considered so long as they help form a coherent set of social statistics courses. Students are encouraged to seek advice from their advisor and the Ph.D. faculty program coordinator in developing their concentration.

Students pursuing approval of a course plan that includes a course not offered by CSSS and not included on the list of approved courses must provide the Ph.D. faculty program coordinator with recent syllabus and a rationale for including the course in their plan.

List of approved courses:

  • CS&SS 510: Maximum Likelihood Methods for the Social Sciences
  • CS&SS 512: Time Series and Panel Data for the Social Sciences
  • CS&SS 526: Structural Equation Models for Social Sciences
  • CS&SS 527: Survey Research Methods
  • CS&SS 529: Sample Survey Techniques
  • CS&SS 533: Research Methods in Demography
  • CS&SS 536: Analysis of Categorical and Count Data
  • CS&SS 544: Event History Analysis
  • CS&SS 554: Statistical Methods for Spatial Data
  • CS&SS 560: Hierarchical Modeling in the Social Sciences
  • CS&SS 564: Bayesian Statistics for the Social Sciences
  • CS&SS 565: Inequality: Current Trends and Explanations
  • CS&SS 566: Causal Modeling
  • CS&SS 567: Statistical Analysis of Networks
  • CS&SS 568: Statistical Analysis of Game-Theoretic Data
  • CS&SS 589: Multivariate Data Analysis for the Social Sciences

The Ph.D. faculty program coordinator will be responsible for periodically updating the list of approved courses.

Minimum Grade Point Average

Students must obtain a minimum grade point average of 3.3 for their four approved courses.

Evaluation By The Statistics Concentration Committee

When a student has completed all four courses, the student will submit to the Ph.D. faculty program coordinator a packet including grades received and any written papers completed for the courses. The Ph.D. faculty program coordinator will evaluate the performance in the course. In most cases this will be a pass (if the student has met the 3.3 grade point average, and the courses taken form a coherent set). The Ph.D. faculty program coordinator can also give evaluations consistent with certifying the concentration, such as a pass with distinction. Finally, the Ph.D. faculty program coordinator can use its discretion to deal with grading in different departments that may use different standards.

The Center for Statistics and the Social Sciences will provide a document certifying that the student completed the Concentration in Statistics.

Additional Information