Results of a test were given as very satisfied, satisfied, dissatisfied it represents. (LQ)
In the question above individuals have been ranked on the basis of their level of satisfaction, hence this is an example of ordinal variable. Also remember,
Data used in statistics can be of 2 types.
a. Qualitative data
b. Quantitative data
- Data which "Cannot be measured" is termed qualitative
- This denotes some characteristics or quality of the variables according to which they can be categorized.
Examples of qualitative data - sex, color. This kind of data can be placed under 2 scales:
(i) Nominal scale
Data cannot be placed in any meaningful order so it is divided into qualitative groups such as black/ white, male/ female etc.
(ii) Ordinal scale
Date can be placed in a meaningful order e.g. ranking of runners in a race, hardness scale for water etc.
But in this case, there is no information regarding the size of "interval" i.e. the difference between 1st and 2nd or 2nd and 3rd cannot be judged.
Data which "can be measured" e.g. - BP, serum glucose, age etc It can be measured on 2 scales.
(i) Interval scales:
Data is placed in meaningful order with intervals between the variables which" can" be measured.
e.g. - Temperature on Celsius scale (difference between 10° and 20° is same as that between 30 and 40 degree Celsius but 40 C temperature cannot be said as twice as hot as 20°C because O°C does not indicate complete absence of heat.
(ii) Ratio scale:
Similar to interval scale but it has an absolute zero & hence meaningful ratios exist.
e.g. - temperature on Kelvin scale, heart rate, BP etc.
Here a zero indicates absence of heart/pulse/ BP respectively. Hence - a temperature of 200k is twice as hot as l00k, a pulse of 120 is twice as fast as 60/ min and a systolic BP of 200 mmHg is twice of a SBP of 100mmHg.