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Publications in VIVO
 

Philips, Andrew Q Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • Substantive research interests include political economy, especially the politics of distribution and the manipulation of budgets around elections. Methodological research interests include time series, pooled time series, compositional data, and the graphical presentation of results.

keywords

  • political budget cycles, distributive politics, political economy, time series, pooled time series, compositional data, graphical presentation of results

Publications

selected publications

Teaching

courses taught

  • PSCI 2075 - Quantitative Research Methods
    Primary Instructor - Fall 2018
    Introduces quantitative research methods used in political science. Focuses on basic tools of analysis: data collection, processing, and evaluation, with special attention to survey techniques. Includes elite and case study analysis; aggregate, cluster, and content analysis; and the use of computers in political research.
  • PSCI 3075 - Applied Political Science Research
    Primary Instructor - Spring 2019
    Introduces the types of research design and quantitative methodology used in applied political science research. Directly builds on the data analysis performed in Quantitative Research Methods (PSCI 2075).
  • PSCI 7095 - Advanced Political Data Analysis
    Primary Instructor - Spring 2018 / Spring 2019
    Provides advanced training in the statistical modeling of political relationships. Focuses on the properties and assumptions of the ordinary least squares regression model, building on material covered in PSCI 7085: Introduction to Political Science Data Analysis.
  • PSCI 7108 - Special Topics
    Primary Instructor - Spring 2018
    Various topics not normally offered in the curriculum. Topics vary each semester. May be repeated up to 12 total credit hours.
  • PSCI 7155 - Maximum Likelihood Estimation and Generalized Linear Models
    Primary Instructor - Fall 2018
    Introduces maximum likelihood estimation and extends the linear model to several generalized linear models. Provides students with the skills to analyze and understand a broad class of outcome variables and data structures such as dichotomous outcomes, counts, ordered and unordered categorical outcomes and bounded variables. Also examines several special topics such as multilevel models, causal inference and missing data.

Background

International Activities

global connections related to teaching and scholarly work (in recent years)

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