1. Andrew Ho Harvard Graduate School of Education Tuesday, January 22, 2013 S-052 Shopping – Applied Data Analysis

2. Disciplined Perception: Experts vs. Novices

3. A single outcome variable Continuous, interval scaled (noncategorical) A single predictor variable… May be transformed to meet regression assumptions of normally distributed residuals Independent and identically normally distributed residuals centered on 0 May be transformed to achieve linearity May be dichotomous or polychotomous Multiple predictor variables Interactions: Products of predictors Quadratic/Polynomial Regression for nonlinear relationships What You’ve Learned

4. Multiple Regression Analysis Multiple Regression Analysis Do your residuals meet the required assumptions? Test for residual normality Use influence statistics to detect atypical datapoints Are the data longitudinal? Use Individual growth modeling If your residuals are not independent, replace OLS by GLS regression analysis Specify a Multilevel Model If time is a predictor, you need discrete- time survival analysis… If your outcome is categorical, you need to use… Discriminant Analysis Multinomial logistic regression analysis (polychotomous outcome) Binomial logistic regression analysis (dichotomous outcome) If you have more predictors than you can deal with, Create taxonomies of fitted models and compare them. Conduct a Principal Components Analysis Form composites of the indicators of any common construct. Use Cluster Analysis Transform the outcome or predictor If your outcome vs. predictor relationship is non-linear, Use non-linear regression analysis What you will learn: The S-052 Roadmap

5. 8 Units 1.Taxonomies of Regression Models 2.Nonlinear Regression 3.Nonindependent Residuals 4. Logistic Regression 5. Discrete-Time Survival Analysis 6. Forming Composites 7. Cluster Analysis 8. Factor Analysis

6. Disciplined Perception: Gender in Math Instruction http://www.edweek.org/ew/articles/2013/01/16/17gender.h32.html http://ftp.iza.org/dp6453.pdf

7. Disciplined Perception: Massively Open Online Courses

8. Scared! This sounds familiar! Logistic regression isn’t so ba- Ack, Discrete Time what now? Whoa, fixed and random effects? Clustering… seems intuitive Principal components?! Final project The Flow of S-052. Two steps forward. One step back.

9. I. Research Questions and Data Sets What predicts attrition in massively open online courses? Do teacher qualifications have a particularly strong impact when female teachers teach girls? What are the common characteristics of Academy Award winning actors and movies over their competition? Lectures with your questions: Active participation is encouraged, time permitting 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, PowerPoints? How do we communicate results to both technical and non-technical audiences? Each unit has a three-part structure Note-taking: On laptops (in laptop zones at the edges or the back of the lecture hall) or printouts of handouts Please be courteous: No cellphones, email, websurfing, IM, texting or other electronic distractions during class How you’ll spend your time in S-052, Part I: What we’ll do in class

10. Assignments Six homework assignments, consisting of one or more datasets & questions that guide you through a complete analysis (1/2 of your grade). Submitting assignments in pairs is mandatory for all assignments! One final exam, completed individually, will give you a chance to review all the material in a comprehensive series of analyses (1/2 of your grade). Individual and group work Our strong emphasis on collaboration is a reflection our philosophy that learning statistics is like learning a language and must therefore be spoken actively and in a participatory context. Also reflects the realities of today’s team- driven statistical practice. Work in study groups as you’d like, but write and submit HWs as pairs. The final exam must be completed individually. Course website: http://isites.harvard.edu/icb/icb.d o?keyword=k92522 http://isites.harvard.edu/icb/icb.d o?keyword=k92522 Instructor Office Hours: http://andrew-ho-office- hours.wikispaces.com How you’ll spend your time in S-052, Part II: What you’ll do outside of class Weekly Sections All students will have a “homeroom” section and TF on Tuesday, Wednesday, or Thursday afternoon, to be scheduled via a doodle poll. Sections both reinforce and supplement lecture content. There will be Stata labs, additional examples, and opportunities for questions. Attendance is not mandatory but strongly, strongly encouraged.

11. 1. Make sure you have the prerequisites A solid regression class (S- 030, S-040, or equivalent) Experience fitting regression models with statistical software (Stata or other) 5. Decide how you want to access Stata Visit the LTC on Gutman 3 Google “HGSE ordering Stata” Think about whether it makes sense for you to purchase a Stata license. 4. Familiarize yourself with the S-052 website Bookmark the site: http://isites.harvard.edu/ic b/icb.do?keyword=k92522 http://isites.harvard.edu/ic b/icb.do?keyword=k92522 Read the syllabus—it includes many more details and represents our learning contract. 6. Get used to accessing the handouts before class. I’ll be posting the 1 st handout to the website before class next week. You don’t have to read it; but you may find it helpful to bring it. 3. Read the School’s policy on plagiarism All written work submitted is to be in your own words or those of your partner. 2. Register for the course: http://www.gse.harvard.edu/a bout/administration/registrat ion/cross_registration.html Note that GSAS, HBS, HLS, HMD, HSDM, GSD, HDS and HPSH students must fill out a new online cross-registration form. Hope to see you next Tuesday, 10AM, in Larsen G08! Six things you should do before the first class meeting, next Tuesday