DEFINITION

Data can be defined as a collection of facts or information

from which conclusions may be drawn.

TYPES OF DATAQUANTITATIVE QUALITATIVE

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)

QUANTITATIVE DATA CLASSIFICATION

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

• 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 . . .

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

QUALITATIVE DATA CLASSIFICATION

•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

• Race

Qualitative data . . .

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 . . .

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

DATA MEASUREMENT

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.

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

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

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

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

Ratio

•Satisfies all properties of measurement

•Eg: Weight of an object, temperature in kelvin

•Minimum value is zero (cannot be negative)

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

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

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