1. Types of Data
Qualitative data: consist of attributes, labels, non-numerical values
(examples: hair color, political party, zip code, favorite pizza)
Quantitative data: consist of numerical measurements or counts (age, length of forearm, number of Facebook friends)

2. Examples Qualitative? Quantitative? - salaries of teachers
marital status of graduate students
social security numbers
number of cars in household
color of family car

3. Qualitative? Quantitative?
- salaries of teachers (quantitative)
marital status of graduate students (qualitative)
social security numbers (qualitative)
number of cars in household (quantitative)
age of cars in household (quantitative)
color of family car (qualitative)

4. Qualitative? Quantitative?
City Population
Baltimore 636,919
Jacksonville 807,815
Memphis 669,651
Pasadena 143,080
San Antonio 1,351,305
Seattle 598,541

5. Levels of Measurement
Nominal : qualitative only. Data are categorized using names, labels, or qualities. No mathematical computations. (names of baseball teams, social security numbers)
Ordinal: qualitative or quantitative. Data are ordered or ranked, but differences between data are not meaningful (final standings of NFC West conference football teams)

6. Levels of Measurement
Interval : can be ordered; meaningful differences between data values. However a “zero” value does not imply absence of the attribute (temperature) – [no inherent zero]
Ratio: like interval data, but also:
- “zero” value means absence of attribute
[inherent zero] (e.g. wind speed)
- one data value can be expressed as a
multiple of another (i.e., as a ratio)
(a dog weighing 20 pounds is twice as
heavy as a dog weighing 10 pounds)

7. Example
The following items appear on an employment application. Identify the level of measurement for each.
- highest previous salary
- gender
- year of college graduation
- number of years at last job

8. Example
The following items appear on an employment application. Identify the level of measurement for each.
- highest previous salary (ratio; quantitative, makes sense that $45000/yr is three times $15000/yr)
- gender (nominal)
- year of college graduation (interval; makes sense to say that 2010 is 5 years later than 2005)
- number of years at last job (ratio)

9. A sports writer plans to list the winning times for all the swimming events in the 2012 Olympics.
The writer wants to simply organize the data and compile a list (describe!) the medal winners of the Olympics
descriptive study

10. A survey conducted among 1017 men and women found that 76% of women and 60% of men had a physical examination with the previous year.
Inferential? Descriptive? Both!
76% women, 60% men  Descriptive (simply describes the data sample which was collected)
More women than men will have physical exams during the year  Inferential (use the data sample to say something about the population)