1. Merging data using Excel & Stata Mark Bruyneel & Matthijs de Zwaan
Research Data Services
Merging data using Excel & Stata
Mark Bruyneel & Matthijs de Zwaan
- Welcome to the course/ this web lecture on Data retrieval skills
- My name is . . .

2. Program: Background (30 min.) - Getting your data - Working with data
Exercise 1: Excel (30 min.)
Exercise 2: Stata (40 min.)

3. think now, download later
Getting your data
Rule 1:
think now, download later

4. Getting your data
Formulate a research question
What was the influence of using governance reporting standards on earnings management of companies?
What data do I need?
Variables?
Sample: Geography? Time period?
Is the data available?

5. What data do I need? Variables Sample Research question:
What was the influence of using governance reporting standards on earnings management of companies?
Variables
What reporting standards?
How to measure earnings management?
Control variables: firm size, board members, …
Sample
Which countries: USA, Europe, the Netherlands
Time period: recent, historical?
Company type: public, SMEs?
Model ? Relationships ?

6. Remarks: Is the data for each database in a single currency ?
What control variables do I need?
Do I need to download components to calculate variables I could not download? (Ratios etc.)
Is the data for each database comparable in time?
If you need more than 1 database: Do you need company identifiers? (see: Blackboard)

7. Which databases are relevant?
Do I need several databases ?
Do I need to combine datasets ?
Do I need just one database ?

8. Which databases are available?

9. Research Data Services

10. Data Center on Blackboard

11. Research Data Services on Blackboard

12. Research Data Services on Blackboard

13. Research Data Services on Blackboard
Help on software: manuals/websites

14. Research Data Services on Blackboard

15. Using several databases
Company identifiers
Search 1
Data 1
Data 2
Search 2
Company identifiers: codes that uniquely identify a company in 1 or more databases

16. Combining data(sets) Company identifiers Data 1 Data 2
Find out which (Company) identification codes are
available in all relevant databases !
Examples: ISIN, Sedol, CUSIP, Tickers.

17. Research Data Services on Blackboard
Additional information or tools

18. Research Data Services on Blackboard

19. Blackboard file:

20. Working with data Many different ways to organize data For analysis:
One line (row) = one observation
One column = one variable
“Tidy” data
Different ways to organize data. Best way depends on what you want to do. For example: clearest way to present data in thesis is not best way to organize data for analysis.
Software requires certain structure in the data. Can be different for different software packages or even versions.
Try to keep actual data/observations separate from comments etc
Stata expects data as one observation in each line, and variables in columns: ‘tidy data’.

21. “Untidy” data: example 1
Name
y2001
y2002
Alphabet - 2
Johnson & Johnson 16 11
Pfizer 3 1
Name
Y(ear)
Result
Alphabet
2001 -
Johnson & Johnson 16
Pfizer 3
2002 2 11 1
Headers have names and data in one cell:
‘y’ = the variable year
2001 and 2002 are values

22. “Untidy” data: example 2
Company
Result
T-2001 -
MSFT-2001 16
GOOG-2001 3
T-2002 2
MSFT-2002 11
GOOG-2002 1
Company
Year 2001
Year 2002 T - 2
MSFT 16 11
GOOG 3 1
Company
Year
Result T
2001 -
MSFT 16
GOOG 3
2002 2 11 1
One column, two separate variable values:
Name and Year

23. Working with data Basics of data merges: Merging data ≠ Appending data
Merging = Adding variables
Appending = Adding observations
Merge data on key variables (ID / Codes)
Must be available in all data files / datasets
Uniquely identify observations (can be a combination of items)

24. A tidy dataset: example 1
‘Name’ and ‘Year’ together uniquely identify a single observation
The ‘Result’ column gives variable values
Name
Year
Result
Alphabet
2001 -
Johnson & Johnson 16
Pfizer 3
2002 2 11 1
N.B.: Unique Company ID codes are often better than the name !

25. Working with data Warning: make sure to keep key ID codes in tact ! ID ID
1324
21234

26. Restoring the ID Restore original length with Excel: REPT & LEN
REPT() = Repeat LEN() = Length number of characters in a cell

27. Merging data Auditor Year GRI Score John 2001 - Jane 16 Mary 3 2002 211 1
Auditor
Year
Big4 ?
John
2001
“Yes”
Jane
Mary
“No”
2002

28. Working with data: merging
Auditor
Year
GRI Score
John
2001 -
Jane 16
Mary 3
2002 2 11 1
Auditor
Year
Big4
John
2001
“Yes”
Jane
Mary
“No”
2002
“1-to-1”
Auditor
Year
GRI Score
Big4
John
2001 -
“Yes”
Jane 16
Mary 3
“No”
2002 2 11 1

29. Working with data: merging
Auditor
Year
GRI Score
John
2001 -
Jane 16
Mary 3
2002 2 11 1
Name
Gender
John
“M”
Jane
“F”
Mary

30. Working with data: merging
Auditor
Year
GRI Score
John
2001 -
Jane 16
Mary 3
2002 2 11 1
Name
Gender
John
“M”
Jane
“F”
Mary
“many-to-1”
Auditor
Year
GRI Score
Gender
John
2001 -
“M”
Jane 16
“F”
Mary 3
2002 2 11 1

31. Working with data: merging
Auditor
Year
GRI Score
John
2001 -
Jane 16
Mary 3
2002 2 11 1
Name
Gender
John
“M”
Jane
“F”
Mary
“1-to-m”
Auditor
Year
GRI Score
Gender
John
2001 -
“M”
Jane 16
“F”
Mary 3
2002 2 11 1

32. Exercise 1: Combining data using Excel
Compustat Global data
Preparing the Datastream data
Combining both datasets

33. Exercise 2: Combining data using Stata
Introduction
Exercise: Compustat Global & Datastream

34. Exercise 2: Combining data using Stata

36. Exercise 2: Stata – Command line

37. Exercise 2: Stata – Scripts / Do files
Basics about .do files:
Text files with the .do file extension
Commands are handled as if they were typed in on the Command line interface
Typing “doedit” calls up the do-file editor.
Advantages of scripting:
Documents what you have done
It makes finding mistakes and repairing them easier
Add comments to your script(s)
(your future self & your supervisor will be grateful)

38. Exercise 2: Stata – Combining the data
Let’s get to work!
- Go to the Data Center Blackboard course
- Download the data files
- Start up the program Stata

39. Need help? The library is there for you !
Website: Blackboard: