1. Introduction
Introduction to Stata 2016

2. And how will we do this?
I introduce and demonstrate a topic and a set of commands
You try the same commands on your computer
I (sometimes) give you small assignments to complete
You report your experience and we discuss any problems that occured
New softwares can only be learnd by ”learning by doing”

3. What do we Find? A result window (Stata speaks to us)
A review window (shows executed commands)
A variable window (shows the variables in the data set) and
A command interface (where we tell Stata what to do)

5. Using the Do-files
The do-files in Stata is a text file with commands that can be run directly from Stata
The do-files stores your commands
By using do-files you always have a good documentation of your work such as codings etc.
It also makes it easy for you to repeat or modify your analyses
By using do-files you never have to make any changes in your data.

6. Data for exercise Download :exercise 1 and 2 to your computer

7. Using the Do-files
A do-file should initially look something like this:
.clear
.set more off
.use c:/exercise2.dta

8. Using the Do-files
.clear – clears the data in memory . Otherwise no new data can be opened.
.set more off – Tell Stata to execute all commands inspite of screen size
Dots before any commands are standard in most books on Stata (I use it as well so you get used to it)
More over, Stata is sensitive for capital- and lower case letters.

9. Using the help command
By typing .help command in the ci many problems can be solved.
Try any of the following:
.help desc
.help lab
.help list

10. renaming the data Changing variable names Labeling variables
The rename command in the ci:
.rename old_varname new_varname
Labeling variables
.label variable varname [”label”]
e.g. label variable sex ”Gender”

11. Examining your data II
Some helpful commands to examine your data more carefully
.tabulate/tabulate1
.tabstat
.summarize
.list
.browse
.order
.sort/gsort
.inspect
.describe
.codebook

12. Examining your data II
Let us now go through each command and see what it can do for us using the ” exercise1.dta”.
.tabulate (tab) – tabulates our variables. The command requires a variable list .tab vars
.tab v39
If you want to tab several variables:
.tab1 v39 v35x v40

13. Examining your data II .summarize (sum) – summarize our variables.
(If no variables in varlist=all variables).
.sum
.sum vars
.sum vars, d
(d=detailed – gives more information such as median values etc.)
For example:
.sum v39
.sum v39, d (d=detailed)
.sum v39 v35x v40

14. Examining your data II
.list – list variables. The command shows the values for a specific observation on a certain variable or all variables
.list
shows the values for a specific observation
.list vars
shows the values for a specific observation on a specific variable
For example:
.list v39
.list v39 v35x v40
Type set more off for long outputs

15. Examining your data II
.order – order the variables. The command hence requires a variable list .order vars
For example:
.order v39 v35x v40

16. Examining your data II
.sort –arranges the observations of the current data into ascending order based on the values of the variables in varlist. (.sort vars)
For example:
.sort v39
.sort v39 v35x v40

17. Examining your data II
Even better is .gsort [-|+] that arranges the observations into ascending or decending order such as:
.gsort –var1
.gsort +var1
.gsort +var1 var2 var3 etc

18. Examining your data II
.inspect –Display simple summary of data's attributes. It is a bit more detailed compared to sum or tab and is useful for numerical vars.
For example:
.inspect
.inspect vars
. inspect v39 v35x v40

19. Examining your data II .describe – Describe data in memory or in file
For example:
.describe
.describe vars
.describe v39 v35x v40

20. Examining your data II
.codebook – describe data contents and the output is often useful for printing. It also gives information of variable characteristics such as numeric or string
For example:
.codebook
.codebook vars
.codebook v39 v35x v40

21. . command varlist, [options]
Stata options
Stata’s general grammar is very straight forward and most commands can be executed with different options.
. command varlist, [options]

22. Stata options
Stata’s general grammar is very straight forward and most commands can be executed with different options.
To see which options that are available – type: .help command

23. Stata options . sum varlist, [d]
Let’s try the some of the commands we learnt with their options (we have already tried one)
. sum varlist, [d]
[d] is here our option

24. Useful options for tabulate :
Stata options
Let’s try the some of the commands we learnt with their options (we have already tried one)
Useful options for tabulate :
, sort
, nolabel
, missing
. tab varlist, []

25. Stata options .tab varlist, [] .tab v39, sort .tab v40, nolabel
For example, type:
v39 v35x v40
.tab v39, sort
.tab v40, nolabel
.tab1 v39 v40 , missing
(underscore means abbreviations for the ci)

26. Stata options Let’s continue with the options
Stata allows for a wide range of different options or pre-post commands that can be used with the main commands.
. [by varname] command varlist [in] [if], [options]

27. Stata options
Let’s continue with the options and introduce the if-statements
An if-statment means that the command only is executed for those observations who fulfill the condition you specify
. command varlist [if], [options]

28. Stata options Try the following statements:
If-statments should be specified before the comma and can be combined with other options such as:
.command varlist if var1==x, [options]
Try the following statements:
. tab u39 if v35x==1
. tab u39 if v35x==1, m
(shows var values with missing included)
Here we are simply tabulating the values of satisfaction with life for all men

29. Stata options
What’s the level of life satisfaction among young and old people?
.tab u39 if v42x <1973 or
.tab u39 if v42x >1970

30. Stata options – common Stata operators

31. Stata options – common Stata operators
Some noteworthy operators:
| or
== equal to (as comprison)
!= not equal to (as comprison)
^ exponent (eg. 2^2=4)

32. Stata options – common Stata operators
Lets try the OR operator
.tab varlist if varX==Z | varX==Y
For example:
tab w39 if v42x>1970 & v35x==1 & v40==1 | v40==4

33. Stata options – common Stata operators
Now we have introduced the IF-statement.
. [by varname] command varlist [in] [if], [options]
Let’s look at the IN-statement.
. command varlist [in], [options]

34. Stata options – common Stata operators
Specifics for the in qualifier:
f the first observation in the data set
l the last observation in the data set
Such as:
.command varlist in f/l

35. Stata options – common Stata operators
Examples of in-statements:
command meaning
list in 1/10 list the first ten observations
list in f/10 list the first ten observations
list in 5/15 list observation nr 5 to 15
list in 5/l list from observation 5 to the end
list in -5/l list the last five observations
list in 10 list only observation nr ten

36. Stata options – common Stata operators
If you want to keep or drop variables
Example:
.drop/keep var3 – var5
Or labels
.label drop labelname
Or observations
.drop in 45/65

37. Stata options – common Stata operators
Lets say you (for some reason) want to find the level of education among the first 20 respondents that are very satisfied with their lifes
.gsort +v39
.list v40 v39 in 1/20
Or the last 20 respondents
.list v40 v39 in -20/l

38. Stata options And finally, let’s check out the by option.
. [by varname] command varlist [in] [if], [options]
With the by command you can receive the values of variable x for every value of variable x, such as:
.by x: tab z
However, the by command only works for sorted data…
There are always several ways to do things in Stata

39. Stata options
Solutions (There are always several ways to do things in Stata):
1, sort the variabel and then use the by command (the long way)
.sort x
.by x: tab z
2, sort directly after the by command such as:
.by x, sort: tab z
3, or even better, use the bysort command
.bysort x: tab z

40. Stata options Let’s try the bysort function For example Etc.
.bysort v40: tab v39
.bysort v40: sum v39
Etc.

41. Conclusively – what have we learnt
Working with do-files
More on data examination
Creating simple univariate tables
Sorting your observations
Re-ordering your variables
Stata logical operators
Using command qualifiers, if, in and by statements

42. Exploring data
Before we move over to data management (which is the next subject), let’s practice what we’ve learnt so far… with a new (and more interesting) data set.
so…..
Clear Stata
Create a new do-file.
Type in neccessary set-commands
Load the data file ” exercise2.dta”
(Make all this in the do-file)

43. .clear
.set more off
.use exercise2.dta
This data is based on QoG with country/years as units of analysis.

44. Examining your data II Take a look at the data, explore the variables
use the commands below combined with the if/in/by options
.tabulate/tabulate1
.summarize
.list
.browse
.order
.sort/gsort
.inspect
.describe
.codebook

45. Question 1
Which are the top ten countries_year observations in terms of having most GDP per capita?

46. Solution(s) Or .clear .set more off .tab cname_year in 1/10
use "C:\exercise2.dta", clear
gsort -mad_gdppc
list cname_year in 1/10 Or
.tab cname_year in 1/10

47. Question 2
What is the mean value of GDP per capita among countries that have a religious Fractionalization below or above the 25:th and 75:th percentile values?

48. Solution: sum al_religion, d al_religion <= 25:th perc. = .232
.sum mad_gdppc if al_religion<=3.02
.sum mad_gdppc if al_religion>=4.18