Types of data and levels of measurement explained in a simple way. Useful for lecture classes.

2. DEFINITION
Data can be defined as a collection of facts or information
from which conclusions may be drawn.

3. TYPES OF DATA
QUANTITATIVE QUALITATIVE

4. QUANTITATIVE DATA
• Quantitative or numerical data arise when the observations are counts
or measurements.
• The resulting data are a set of numbers
Eg:
• Cholesterol level in mg/dl
• Height in cms
• Blood sugar in mg/dl
• Number of children in family (numbers)
• Number of diarrheal episodes (numbers)

5. QUANTITATIVE DATA CLASSIFICATION

6. Quantitative data . . .
• Discrete: The data are said to be discrete if the measurements are integers
• Only whole numbers are possible
• There are gaps between numbers
• Eg:
• Number of children in family
• Number of cigarettes smoked per day

7. • Continuous: if the measurements can take on any value, usually within some
range
• Theoretically, no gaps between possible values
• Eg:
• Cholesterol (mg/dl)
• Weight (Kgs)
• Blood sugar fasting (mg/dl)
• Most biological variables are continuous.
Quantitative data . . .

8. QUALITATIVE DATA
• The objects being studied are grouped into categories based on
some qualitative trait.
• Data that is not given numerically
• Eg:
• Religion
• Blood pressure levels
• Smoking status

9. QUALITATIVE DATA CLASSIFICATION

10. •Nominal data
• A type of categorical data in which objects fall into unordered
categories.
• Nominal data have no order and thus only gives names or labels to
various categories.
Eg:
• Color of eyes
• Religion
• Gender
Qualitative data . . .

11. Ordinal data
•A type of categorical data in which natural order is
important.
•Eg:
• socio-economic status – High, Medium, Low
• stage of cancer – 1,2,3,4
• exam results (grade) – C, C+, B, B+, A, A+
Qualitative data . . .

12. Assessment
Identify the type of data
Gender
Exact age
Number of fillings in tooth
Self reported level of pain
Stages of cancer
Height (in cms)
Qualitative nominal
Quantitative discrete
Quantitative discrete
Qualitative ordinal
Qualitative ordinal
Quantitative continuous
QUANTITATIVE
OR
QUALITATIVE
DISCRETE /CONTINUOUS
OR
NOMINAL/ORDINAL

13. DATA MEASUREMENT

14. Measurement
• Process of assigning numbers to various aspects of
objects/events according to a rule.
• The aim of measurement is to provide accurate, objective,
sensitive and communicable descriptions of event.

15. Levels of measurement
• Proposed by S. S. Stevens (1946)
• Each scale of measurement satisfies atleast one of the four properties:
• Identity – each value has a meaning
• Magnitude – values have an ordered relationship to other
• Equal intervals – values are spaced equally
• Absolute zero – scale has a true zero point
NOMINAL ORDINAL INTERVAL RATIO

16. Nominal scale
• This scale only satisfies the identity property.
• Values assigned to variables represent a descriptive category,
but have no inherent numerical value with respect to
magnitude.
• They are usually coded
• Eg: Gender, religion, political affiliation

17. Ordinal scale
• Has both identity and magnitude
• Ordinal data codes can be ranked
• Distance between codes is not meaningful
• Eg: Results of a running race, Staging of cancer

18. Interval scale
• Has the properties of identity, magnitude, and equal intervals.
• Data can not only be ranked, but also have meaningful
intervals between scale points
• Eg: Temperature in celsius and farhenheit

19. Ratio
• Satisfies all properties of measurement
• Eg: Weight of an object, temperature in kelvin
• Minimum value is zero (cannot be negative)

21. Which scale is appropriate?
• To classify and categorize subjects – NOMINAL
• To rank people according to any characteristic – ORDINAL
• To quantify a trait where distance between rank is same – INTERVAL
• To quantify a trait which has an absolute zero - RATIO

22. Assessment
Ordinal
Identify scale of measurement
Tooth mobility
Temperature in kelvin
Height
Temperature in celsius
Blood group
Number in sports jersey
Horse racing
Time
Ratio
Ratio
Interval
Nominal
Nominal
Ordinal
Ratio

23. WHY DATA IS IMPORTANT ?
Qualitative and quantitative data behave differently and therefore studied
differently
The level of measurement helps you decide how to interpret data from that
variable
The level of measurement determines the type of statistics that is appropriate
for its analysis
Statistical analysis should be planned along with data collection procedures
so that they match