Course Syllabus

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SPCN V-506: Statistical Analysis for Effective Decision-Making, sections 10548 and 10549

  • Instructor Name: Ashlyn Aiko Nelson, Ph.D., Associate Professor, O'Neill School of Public and Environmental Affairs (pronouns: she/her/hers); see detailed syllabus linked in Canvas Modules for contact information
  • Teaching Assistant: Boy Purba
  • Lab Manager: Mohammed Tadipatri

Bulletin Description

Noncalculus survey of concepts in probability, estimation, and hypothesis testing. Applications of contingency table analysis and analysis of variance, regression, and other statistical techniques. Computer processing of data emphasized.

Additional Description

Statistical inference is one of the most powerful tools we have for understanding most social processes and is essential for the public affairs practitioner because of its centrality to program evaluation and the assessment of public and organizational policy outcomes.  In today’s world, the ability to command, analyze and interpret data—and perhaps more importantly, the ability to evaluate critically other people’s analyses of data—is fundamental to the practice of public affairs. 

This course is designed to provide graduate-level instruction in the application of statistical analyses to issues in public and environmental affairs and related fields.  In short, the course will help you to arrive at systematic observations about populations based on data.  You will learn how to conduct statistical analyses, as well as the basic theory that enables and constrains the application of statistics to real world data.  The course emphasizes practical aspects of applying such methods, appropriately interpreting the results of these statistical analysis tools, and gaining a meaningful understanding of how statistical analysis can be misused or erroneously executed (either intentionally or unintentionally).  As such, the course will address sampling theory and distributions, descriptive statistics, statistical inference, the nature of random variables, sampling distributions, point and interval estimation of parameters (means, standard deviations, etc.), hypothesis testing, analysis of variance, and bivariate and multivariate regression.  The emphasis will be on appropriately applying these techniques and extracting meaningful information from unstructured data.  Use of computer tools for carrying out statistical analysis (using R Studio) also is a major emphasis of the class.

 

 Learning Outcomes 

I have a few important goals for you in this course.  At the end of this course I expect you to understand the following:

  • How statistical tools can be applied to problem solving
  • How decision-making can be improved through statistical analysis
  • The nature and use of distributions
  • How sample data can be used to generalize to larger populations
  • The properties and limitations of estimators and hypothesis tests
  • The nature and strength of relationships between variables
  • How to transform raw data into useful information, and
  • How to use computer tools (in this course, R) to carry out statistical analysis

More broadly, I expect that you will be able to interpret your own statistics (and others’ statistical analyses) intelligently and to frame them and analyze them in an intelligent, policy-relevant framework.  In statistical analyses, the interpretation and presentation of the analysis can be as important as the analysis itself.  In order for a statistical analysis to have an impact, it must be articulated and presented clearly and reflect due regard for its intended audience.  

 Required Texts

  • AGRESTI, A., & FINLAY, B.  Statistical Methods for the Social Sciences. 4th Ed.  (ISBN 978-0130272959). You also may purchase the fifth edition online if you prefer, though please note that the assignment questions differ between editions. For all homework assignments, I use the 4th edition but type out all of the questions in full so there is no confusion about which question to answer.
  • If you prefer to purchase the 5th edition as an e-text, you may find it online here.

 Grades

Final grades will be calculated as follows:

Class preparation and participation               10%

Homework assignments  (five, each worth 10% of the final grade)         50%

Final policy memo     40%

Course Summary:

Course Summary
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