Methods of Policy Analysis
Sample courses for Methods of Policy Analysis focus
Please note:
These are an example of the courses that would fulfill this complimentary policy requirement.
We cannot guarantee when or if these courses will be offered.
Political Science
511. Research Seminar in American Government and Politics. (3)
Course emphasizes investigation, evaluation, and discussion of areas of specialized knowledge or inquiry relevant to the profession or field of study.
Sem: State & Urban Policy Analysis
Sem: Good Government Reform
581. Statistics for Social Research. (4)
Provides intensive experience and lab instruction in quantitative techniques employed in political science research, including descriptive statistics, statistical inference, hypothesis testing, measures of central tendency, crosstabulation, differences between means, bivariate regression, correlation and multivariate analysis. Required of M.A. and Ph.D. students.
681. Advanced Statistical Analysis for Social Science Research. (3)
Focuses on a variety of advanced econometric methods. Beginning with a review of matrix algebra and math for the social sciences, the course provides an in-depth examination of multiple regression and more advanced econometric models. Required for Ph.D. students.
Sociology
581. Advanced Social Statistics I. (3)
Examines theory (assumptions, properties of estimators) and application of multiple regression. Introduces matrix notation and generalized least squares.
582. Advanced Social Statistics II. (3)
Additional methods for quantitative social research: regression diagnostics, logit and Poisson regression, principal components, correspondence analysis.
580. Methods of Social Research I. (3)
Analytical examination of traditional methodological issues including measurement, experimental design, sampling, theory construction, role of statistics and nature of probability.
585. Qualitative Research Methods. (3)
Intensive practicum on research fieldwork, including research design, human subjects review, the ethics/politics of fieldwork, and fieldwork implementation. Focuses on ethnographic and interview methods; some attention to focus groups and archives.
595. Special Topics in Sociology. (3)
A course exploring a topic not covered by the standard curriculum but of interest to faculty and students in a particular semester.
Sem: Social Networks
Economics
508. Statistics and Introduction to Econometrics. (3)
Discrete and continuous probability distributions; expectations; joint, conditional marginal distributions; hypothesis testing; least squares estimators; violation of the least squares principle. Econometric software with applications
509. Econometrics I. (3)
Theory and applications: ordinary and generalized least squares, hypothesis testing, dummy variable and distributed lag models; simultaneous equation and two stage least square models; forecasting. Emphasis on computer modeling.
Geography
586L. Applications of GIS. (3)
Selected applications of Geographic Information Systems, including anthropology, business, crime, ecology, engineering, health, planning, water resources and others. Covers analytical techniques specific to selected applications.
587L. Spatial Analysis and Modeling. (3)
Spatial analysis and modeling techniques using Geographic Information Systems. Includes a lab component that covers the use of GIS and other software to carry out analysis projects.
589. Qualitative Methods. (3)
This course is designed to expose students to the underlying theories, purpose, scope, and procedures of qualitative research, especially as applied to human geography.
Community & Regional Planning
511. Analytical Methods for Planning. (3)
Introduction to comparative analysis of social, economic and spatial data as integrated into a typical comprehensive plan. Building data sets, organization of information, use of survey research, preliminary forecasting methods. Descriptive statistics a prerequisite.
513. Qualitative Research Methods. (3)
Introduces students to the methods and techniques of qualitative inquiry. It focuses primarily on preparing students to conduct rigorous qualitative research, community based planning and analysis.
Chicana & Chicano Studies
552. Research Methods and Data Analysis. (3)
This course prepares students to survey a range of qualitative and quantitative approaches, the utility of different approaches depending on theoretical perspective, and the debates in and outside the field.
Public Administration
595. Research Design and Methods. (3)
This course is designed to learn basic concepts and methods in public and/or health administration research. The course introduces main steps in the research process and provides basic knowledge and skills of research design. We will pay particular attention to important themes in research designs and methods focused on data collection/analysis techniques in quantitative, qualitative, and mixed-methods research.
596. Data Analysis for Decision Making. (3)
This course focuses on data analysis prevalent in public administration, including descriptive and inferential statistics, confidence intervals, hypothesis testing, correlation, cross-tabulation, mean comparison with significance testing, regression, ANOVA.
597. Program Evaluation. (3)
This course is intended to provide an advanced introduction to the theory and practice of program evaluation, along with policy analysis and evaluation.
Management
635. Data Analytics. (3)
An understanding of key analytic techniques for manipulating, describing, and visualizing data. Students will learn how to apply these techniques to analyze data and create business intelligence reports to support business decision making.
Public Health
684. Advanced Health Policy Analysis. (3)
This course discusses and explores theoretically driven methods in applied policy analysis through equity and social justice lens. Students will learn the application of five-steps in policy analysis from the approach of evidence informed policy making.
Statistics
440 / 540. Regression Analysis. (3)
Simple regression and multiple regression. Residual analysis and transformations. Matrix approach to general linear models. Model selection procedures, nonlinear least squares, logistic regression. Computer applications.
Tools for multivariate analysis including multivariate ANOVA, principal components analysis, discriminant analysis, cluster analysis, factor analysis, structural equations modeling, canonical correlations and multidimensional scaling.
