1. Shopping class: Unit 0/Slide #1 © Judith D. Singer, Harvard Graduate School of Education Shopping for S-030: Intermediate Statistics: Applied Regression and Data Analysis

2. Shopping class: Unit 0/Slide #2 © Judith D. Singer, Harvard Graduate School of Education A simple test to determine whether S-030 is right for you (actually, the only test you’ll take in S-030) Thinking back over all the journal articles & research reports you read last semester, in what percentage of papers did you carefully read the methods section?  All (100%)  Most (75-99%)  Many (50-74%)  Some (25-49%)  A Few (1-24%)  None (0%) And across all the methods sections you read, in what percentage did you understand what the researchers did well enough to critically evaluate the credibility of the results?  All (100%)  Most (75-99%)  Many (50-74%)  Some (25-49%)  A Few (1-24%)  None (0%)

3. Shopping class: Unit 0/Slide #3 © Judith D. Singer, Harvard Graduate School of Education Is ABC News Really Beating NBC News in the Ratings Race?

4. Shopping class: Unit 0/Slide #4 © Judith D. Singer, Harvard Graduate School of Education Do All-Nighters Really Not Improve Grades?

5. Shopping class: Unit 0/Slide #5 © Judith D. Singer, Harvard Graduate School of Education Are More Attractive Parents Really More Likely to Have Daughters? 52% combined

6. Shopping class: Unit 0/Slide #6 © Judith D. Singer, Harvard Graduate School of Education What you’ll learn in S-030: The science and art of data analysis, regression and analysis of variance (ANOVA) Unit 6: The basics of multiple regression Unit 7: Statistical control in depth: Correlation and collinearity Unit 10: Interaction and quadratic effects Unit 8: Categorical predictors I: Dichotomies Unit 9: Categorical predictors II: Polychotomies Unit 11: Regression modeling in practice Unit 1: Introduction to simple linear regression Unit 2: Correlation and causality Unit 3: Inference for the regression model Building a solid foundation Unit 4: Regression assumptions: Evaluating their tenability Unit 5: Transformations to achieve linearity Mastering the subtleties Adding additional predictors Generalizing to other types of predictors and effects Pulling it all together

7. Shopping class: Unit 0/Slide #7 © Judith D. Singer, Harvard Graduate School of Education How you’ll spend your time in S-030, Part I: What we’ll do in class I. Research Questions and Data Sets Are college students from integrated high schools more—or less—comfortable with minorities? Did Al Gore really lose the 2000 Presidential Race in Florida? … and many more Lectures with your questions: Active participation is encouraged!!! II. Delve into the new statistical content that the RQs (and the unit) demands What aspect of the model do we need to learn more about? How do we represent this aspect of the model algebraically & graphically? What assumptions are we making (and how do we evaluate whether these make sense?) III. Interpreting & presenting results How do we interpret computer output? What conclusions can we draw—and what conclusions don’t necessarily follow? How do we write up our results—in words, graphs, tables? How do we communicate results to both technical and non-technical audiences? Each unit has a three-part structure Note-taking: On laptop or printouts of handouts, but if you use a laptop, please sit on the side (they’re noisy)! Please be courteous: No cellphones, email, websurfing, IM, texting or other electronic distractions during class

8. Shopping class: Unit 0/Slide #8 © Judith D. Singer, Harvard Graduate School of Education How you’ll spend your time in S-030, Part II: What you’ll do outside of class Assignments Six homework assignments, each consisting of a RQ, data set & questions that guide you through a complete analysis (~ ⅔ grade) One final project that gives you a chance to pull together all your component skills into a polished professional product (~ ⅓ grade) Statistical computing with structured tutorials Individual and group work Work in study groups as you’d like, but write and submit HWs individually The final project may be submitted on your own or with one partner TFs will hold over 20 hours of office hours each week, using a sign up system Course website: http://my.gse.harvard.edu/course/gse-s030a/2009/springhttp://my.gse.harvard.edu/course/gse-s030a/2009/spring No required reading, but we’ve ordered: …and placed other books on reserve…..

9. Shopping class: Unit 0/Slide #9 © Judith D. Singer, Harvard Graduate School of Education Eight things you should do before the first class meeting, next Tuesday 1. Make sure you have the prerequisites An intro statistics class (S- 012, S-010Y or equivalent) Some experience with statistical computing (not necessarily PC-SAS) 7. Decide how you want to access PC-SAS Visit the LTC on Gutman 3 Printout your copy of the S-030 PC-SAS manual Think about whether it makes sense for you to purchase a license 5. Familiarize yourself with the S-030 websiteS-030 website Bookmark the site Read the syllabus—it includes many more details and is our learning contract. 8. Bring the first handout to class We’ll be posting the 1 st handout to the website by Monday You don’t have to read it; just be sure to bring it 3. Register in one of the two identical sections 4. Read the School’s policy on plagiarism All written work submitted is to be in your own words 6. Read “Best Practices in S-030” Helpful advice from former students and TFs 2. Complete and submit the on-line course sign up sheet Fill out the on-line poll to give us some background information and let us know you will be taking the class http://poll.icommons.harvard. edu/poll/taker/pollTaker.jsp? poll=1-8379-77386