1. Getting Started with Stata 2/11/2010 Tom Tomberlin Nealia Khan Learning Technologies Center Harvard Graduate School of Education

2. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

3. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

4. Overview Why use Stata?  Availability  Can self-program, or use menus  Cutting –edge statistical methods (including user-defined functions)  Publication-quality graphics

5. Stats and Graphics

6. Getting Started A word about programming in and using Stata Stata is case sensitive, so Myvar is different from myvar All commands in Stata are lower-case “and’ = &, “or” = |, “not”= ! Assignment is “=“, value equivalency is “==“

7. Windows in Stata

8. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

9. Getting Started Opening Stata Opening Data: –Stata formatted data  “use” command –Comma-separated variables  “insheet using” –Tab-delimited variables  “insheet using” –Flat-files  Create a dictionary

10. Apply Your Knowledge Exercise 1: Open Stata Using the insheet command, open the comma- separated variables data file located in – F:\workshops\SATdata.csv  (HINT: all Stata commands must be written in lower case.  Don’t forget to put pathnames in quotes!)

11. Examining Data Look at your data – did our data import correctly? –How are our data measured? –What kinds of variables do we have? How would we describe the distribution of our data? –Graphs  Histograms  Scatterplots –Charts/Tables  Frequency tables  Cross-tabs

12. Looking at Data There are several ways to look at our data in Stata –Editor –Browser –Stata commands  codebook  des  Tables of frequency and distribution  Graphs of distribution

13. Examining Data Let’s look at how the variable ‘csat’ is distributed –hist csat –tab csat

14. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

15. Do files What are do-files? ‘Do’ files are essentially a syntax list of all of the commands that you wish to run, and the setting that you would like to set –Why use them?  Replication  Collaboration  Audit trail  Help –How to create and run one

16. Do-files Creating and running a do-file

17. Do files –EXERCISE 2: Create a simple do-file from the commands that you have already entered. (HINT: you must clear the data in memory before opening a new dataset.)

18. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

19. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

20. Basic Data Cleaning –Labeling –To label a variable: label var varname label –To label values:  label define labelname 1 ‘high’ 0 ’low’  Label val varname labelname –Renaming  ren varname1 varname2 –Recoding  recode varname oldvalue=newvalue –Generating a new variable  gen newvarname=somevalue –Replacing values of an already generated variable  replace newvarname=somevalue

21. Basic Data Management Subsetting –keep –drop –if Merging merge must sort both files by the linkage variable! ex: merge linkage_var using “F:\workshops\newfile”

22. Basic Data Cleaning EXERCISE 3: generate a dichotomous variable called hi_score from the csat variable, where a value of 1 indicates a score of greater than 922 and a 0 is less than or equal to 922. label it as 0=low and 1=high.

23. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

24. Beginning Analysis Univariate analysis  summarize  histogram  Table Bivariate analysis tabulate pwcorr ttest

25. Apply Your Knowledge  EXERCISE 4:  Generate a histogram of the expense variable  generate a two-way table to see if distributions are the same or different for the values of expense by the different values of your newly created hi_score variable  If you have time, see if there is a significant correlation between scores on SATs and the average amount of money that each state spends on education.

26. Beginning Analysis Multivariate models –Linear regression regress depvar indepvar1 indepvar2 … indepvarN –Logistic Regression  logit depvar indepvar1 indepvar2 … indepvarN

27. Apply Your Knowledge Exercise 5: Generate two scaterplots – one to look at the relationship between expense and csat, one to look at expense and hi_score. Depending on your estimation of the relationship (linear or not), run the appropriate regression to test for the relative effect of expense on either csat scores or hi_scores

28. Agenda I.Overview of Stata II.Getting Started III.‘Do’ files IV.Basic data cleaning V.Basic data management VI.Beginning analysis VII.Special topics (time permitting)

29. Thanks Questions? Gutman Library, room 323a&b StatHelp@gse.harvard.edu http://www.isites.harvard.edu/icb/icb.do?keyword=ltc