1. EHS 655 Lecture 3: Types of data, basic Stata commands

2. What we’ll cover today The basics Types and scales of data
Stata – basic commands

3. THE BASICS: UNDERSTANDING EXPOSURE
White E, Armstrong BK, Saracci R. 2008

4. Exposure assessment approaches
Nieuwenhuijsen M, Paustenbach D, Duarte-Davidson R. 2006

5. Exposure data examples

6. Exposure data examples
Ignacio and Bullock, AIHA, 2006

7. TYPES AND SCALES OF DATA Binary and nominal
Values only used as labels
Binary = two categories
Nominal >= two categories
Tells nothing about individual’s ranking
Examples?
Binary: gender
Nominal: occupation (if not ordered in some way related to exposure)
White E, Armstrong BK, Saracci R. 2008

8. Scales of data - ordinal
Values used indicate rank ordering
Differences between numbers do not indicate actual size of differences
Examples?
Socioeconomic status
Crude categories of exposure (e.g., exposed daily, weekly, monthly, yearly)
White E, Armstrong BK, Saracci R. 2008

9. Scales of data - interval
Relative values of assigned numbers reflect true differences in underlying values
Zero point is arbitrary
Examples?
Fahrenheit or Celsius temperature scale
Year of birth or any other variable using calendar time
White E, Armstrong BK, Saracci R. 2008

10. Scales of data - ratio
Allows valid comparison of measurements by calculation of true difference and ratio
Zero point of scale is true zero point
Examples?
Someone who ingests 500 mg of vitamin C daily consumes 2X as much as someone who ingests 250 mg daily
White E, Armstrong BK, Saracci R. 2008

11. Exercise
Imagine a study of exposure to 1,4 dioxane in drinking water among community members in Ann Arbor
Think of an example of potentially relevant exposure information for each of following data scales:
Binary
Nominal
Ordinal
Interval
Ratio

12. Data types associated with exposure assessment methods
Typical data type
Personal interviews
Nominal or ordinal
Self-administered questionnaires
Proxy respondents
Diaries
Nominal, ordinal, interval, ratio
Observations
Nominal, ordinal, interval
Records evaluations
Measurements (personal or environmental)
Ratio

13. Ramifications of data scales
When exposures can be quantified, best to capture as quantitative rather than categorical
E.g., #cigarettes per day instead of <14, 15-24, 35-34, etc.
Even if categories equally sized, mean value in each category cannot be computed
Can always collapse quantitative to categorical – but not vice versa
May even want to do this, as ordinal is easy to understand, makes no dose-response assumptions
White E, Armstrong BK, Saracci R. 2008

14. On to Stata

15. On to Stata

16. On to Stata Basic file commands Basic dataset summary commands
Open/start do-file
Clear memory (“clear all”)
Import file (“import”)
Open file (“use”)
Export file (“export”)
Save file (“save”) with “, replace” option
Basic dataset summary commands
Table of all variables (“describe”)
Summarize all variables (“summarize”) with “, detail” option
Examine all variable names, labels, data (“codebook”) with “, compact” and “, problems” option

17. On to Stata Basic data manipulation commands
Define label/name for a variable (“label variable”)
Create labels (“label define”)
Assign labels to variable (“label values”)
Rename variable (“rename”)
Generate a new variable (“generate”)
Replace an existing variable (“replace”)
How can you use M+Box to store your datafile?
What commands did you use or try for the homework assignment?

18. On to Stata Homework assignment file
What did you find in the homework?
Anyone have to use the “Break” button?
Anyone start a do-file? Figure out how to run it?