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Philips, Andrew Q

Associate 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, machine learning 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, machine learning

Publications

selected publications

Teaching

courses taught

  • ARSC 5040 - Arts and Sciences Special Topics
    Primary Instructor - Spring 2019
    Same as ARSC 4040. May be repeated up to 3 total credit hours.
  • PSCI 2075 - Quantitative Research Methods
    Primary Instructor - Fall 2018 / Spring 2020 / Spring 2021
    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 / Summer 2020 / Summer 2022
    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 / Spring 2020 / Spring 2021 / Spring 2022
    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 / Fall 2020 / Fall 2021
    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.
  • PSCI 7255 - Time Series and Pooled Time Series
    Primary Instructor - Spring 2022
    Time series models�models which take advantage of variation over time in a single unit�and pooled time series (sometimes called cross-sectional time series or time series cross-sectional) models�which utilize variation across both time and spatial units�are very common in political science. While these models offer substantial leverage over important social science problems that use purely cross-sectional data, there are a number of pitfalls that are necessary to avoid during estimation. Recommended prerequisite: students should have a background in advanced regression statistics (such as PSCI 7085, PSCI 7095 and PSCI 7155).

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