Associate Professor

Phil Murphy
McCone Building M224
(831) 647-4600

Professor Murphy holds a longstanding interest in human cognition and motivation. He initially pursued these interests within the field of history, which underscored the importance of context for understanding and interpreting how people understand, interpret, and interact with the world around them. However, he was frustrated by retrospective nature of the discipline and ultimately shifted his attention to the more proactive field of public policy. 

Dr. Murphy studied public policy under Professor William Dunn at the University of Pittsburgh, and quickly realized he had a penchant for research methods that relate to the field. A good portion of his doctoral training was spent in search of the approaches to research and analysis that help to bridge the gap between numeric and descriptive research, resulting in a fondness for mixing methodologies. 

This mixed-methods approach to understanding the world around us has similarly come to define the Mixed methods, Evaluation, Design, and Analysis (META) Lab, which Dr. Murphy directs. The META Lab is essentially a repository – and outlet – for anyone who is interested in applying the analytic skills that they either already have, or wish to grow. Students there are able to develop and hone their skills even beyond the curriculum available in regular courses, and frequently do so for clients and colleagues.

Courses Taught

Courses offered in the past two years.

  • Current term
  • Upcoming term(s)

This course is a guided introduction to conceptualizing problems and making sense of quantitative information in the policy sphere. The course begins by introducing the theory and practice of policy analysis. The stages of the public policy process and methods for structuring policy inquiry are introduced to provide a means for deconstructing policy problems and asking relevant and practical questions in a policy context.

Next the class is introduced to how such questions are addressed using quantitative tools. Topics to be covered include sampling, estimation, hypothesis testing, analysis of variance, and regression techniques. This will basically be a primer on applying inferential statistics to policy problems. The course will also include introductory training in the use of innovative statistical software, as well as Excel statistical functions.

Fall 2018 - MIIS, Fall 2019 - MIIS

View in Course Catalog

This course introduces students to the skills and concepts at the core of a dynamic and rapidly developing interdisciplinary field. Network analytic tools focus on the relationships between nodes (e.g., individuals, groups, organizations, countries, etc.). We analyze these relationships to uncover or predict a variety of important factors (e.g., the potential or importance of various actors, organizational vulnerabilities, potential subgroups, the need for redundancy, social and economic ties, growth within a network, …). Although the security field has received the greatest amount of recent attention (covert or terrorist networks), these tools can offer valuable insight into a variety of disciplines. The combination of – often stunning – visual analytic techniques with more quantitative measures accounts for much of the increasing worldwide popularity of this field.

Course Objectives

At the end of the semester, students will be able to:
Explain and apply a number of the concepts that underpin network analysis Apply concepts such as centrality, brokerage, equivalence and diffusion to network data Critically evaluate structures and substructures within a network Perform a variety of approaches to clustering and cohesion to networks Analyze networks using a variety of software packages

Fall 2018 - MIIS, Fall 2019 - MIIS

View in Course Catalog

The course is an introduction to inferential statistics with an emphasis on Policy Analysis applications. Topics to be covered include sampling, estimation, hypothesis testing, analysis of variance, and simple and multiple regression analysis. The course will also include an introduction to the use of the computer as a tool for data analysis using leading statistical packages, as well as Excel statistical functions.

Spring 2018 - MIIS

View in Course Catalog

This class builds on Data Analysis for Public Policy and covers advanced topics commonly used in very diverse areas of policy analysis, specifically data reduction techniques (factor analysis) and non-linear models (logistic regression). The course also includes minor sections on data manipulation, formatting of raw data (flat, text files); databases; and proprietary data formats.

Spring 2018 - MIIS

View in Course Catalog

non-standard grade, WTR

Winter 2016, Winter 2017

View in Course Catalog

Areas of Interest

The field of public policy covers two main aspects: governments’ roles in making policy; and the public’s reaction to that policy. When you keep in mind that public policy is essentially an attempt by a government to influence human behavior, the field quickly becomes interesting. Add to that the fact that people are involved in every aspect of the policy making process, and the policy field becomes all the more fascinating as the competition and interactions begin to become clearer. I’m especially interested in assessing social capital and ideological groups through the lens of social network analysis.

Academic Degrees

  • PhD, University of Pittsburgh Graduate School of Public and International Affairs

Professor Murphy has been teaching at the Institute since 2008.


Select Publications

  • Knowledge Hub and Inventory of Opportunities
  • Getting It Done: A Brief Overview of Critical Junctures in the Study of How Policy Translates into Practice
  • Public Administration Education in Macedonia: Accelerating the Process
  • Social Policy and International Interventions in South East Europe
  • Models, Methods, and Stereotypes: Efforts to Maintain, Reify, and Create Macedonia's Ethnopolitical Identities and How Research Can Move beyond Them
  • Public Policy Analysis and Its Importance to Public Administration Reform