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Statistics and Quantitative Analysis U4320

This course provides an overview of how to describe numerical data, make inferences about populations from samples, and evaluate the relationship between variables, factors, or events. Examples from education and the environment are used to illustrate these concepts. The course includes hands-on learning with software such as SPSS and Excel.

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Statistics and Quantitative Analysis U4320

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  1. Statistics and Quantitative Analysis U4320 Segment 1: Introduction Prof. Sharyn O’Halloran

  2. Preliminaries • Professor Sharyn O’Halloran • Office Hours: Wednesday 9:00 to 11:00 • Contact information: • Office: 727 IAB • E-MAIL: SO33@columbia.edu • PHONE: X4-3242

  3. Overview • How to describenumerical data. • How to make inferences about populations from samples. • How to evaluate the relation between variables, factors or events.

  4. Example 1: Education • Main Finding: • Black and Hispanic students are far less likely to attend college than are white students.

  5. Example 1: Education (cont.) • Dependent Variable: • What is the phenomenon to be explained? • Here, it is the percent of minorities enrolled in college • Independent Variable: • What factors might help explain this phenomenon? • We can think of many…

  6. Example 1: Education (cont.) • Evidence: • 34% of Whites 18-24 were enrolled in college in 1991. • 24% of Blacks 18-24 were enrolled in college in 1991. • 18% of Hispanic 18-24 were enrolled in college in 1991.

  7. % Attending Percent Attending College by Category Example 1: Education (cont.) • How would you represent this data graphically?

  8. Example 1: Education (cont.) • Measurement Problems • What does 34% represent? • What is the reference group? • What is the sample population?

  9. Example 1: Education (cont.) • What is the causal relation between race & education? • Education is the dependent variable or the thing to be explained. • Race is the independent variable or the causing factor. • This is a causal model or a path diagram. • Simplest model: Race Education (Independent) (Dependent)

  10. Example 1: Education (cont.) • What does the article suggests? Race Income Education Independent Intervening Dependent • Income is an intervening variable. • Because minorities tend to have lower incomes they are less able to afford education. • Implication • Race affects education via income

  11. Example 1: Education (cont.) • Policy Prescription: • Article argues that to improve educational attainment, need to ensure funding for minorities. • But what if income is not the problem? • What if the relation between race and higher education is due to discrimination or cultural factors? • How should we redirect government policy?

  12. Example 1: Education (cont.) • A second hypothesis postulated is that: •  RaceIncomeDropouts (Independent) (Intervening) (Dependent) Course type Income Opportunities

  13. Example 1: Education (cont.) • Policy Implication: • Raising the minimum wage will increase dropout rates. • Moral: • Different models of the world lead to different policy predictions.

  14. Example 2: Environment Incinerator Health Problems Independent Dependent • What other factors might intervene here? • How would these interpretations change the implied policy prescriptions?

  15. Harper’s Index • http://www.harpers.org/harpers-index/listing.php3 • Numbers are present as facts, as if they speak for themselves • But numbers rarely speak for themselves • As we have seen, they can have many different interpretations and causes • This course will teach you how to speak for the numbers (or else someone will do it for you)

  16. Goals of the Course • The purpose of the course is introduce professional students to basic data analysis skills. • Develop techniques to test and evaluate competing models of how the world works. • Approach • Hands on / learning by doing

  17. Materials • Text • Wonnacott and Wonnacott (4th Edition) • Course Packet • Software • SPSS for Windows (Also available at the CU Bookstore) • Excel • SIPA Skills Course • Data • 1998 GSS Data set available on the SIPA server • See “Why Take Statistics” in Course Packet

  18. Teaching Assistants • One TA • Head Sections and Office Hours • Weekly Labs Mandatory • Meet in classroom, then move to lab • One PRA • Grading • Office Hours

  19. Support • Website • http://www.columbia.edu/itc/sipa/U4320y-003 • Newsgroup • Class bulletin board • Check regularly • Be-Nice Policy • Class Notes • PowerPoint slides available on website after class • Not a substitute for attending lecture

  20. Grading • Weekly Assignments (40%) • No late work • Presentation counts • Midterm (30%) • In class • 1 3x5 index card • Final Paper (30%). • Can work in pairs

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