Getting Started with Stata

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

2. Agenda • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • Special topics (time permitting)

3. Agenda • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • 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 • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • 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 • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • 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 • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • Special topics (time permitting)

19. Agenda • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • 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 • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • 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 • Overview of Stata • Getting Started • ‘Do’ files • Basic data cleaning • Basic data management • Beginning analysis • 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