Nominal versus Ordinal Data: A Comparison of Nominal and Ordinal Data

Statistics

Difference Between Nominal and
Ordinal Data
•Ordinal-certain ranking or position for instance
• Pastry satisfaction of consumer utilizing a ranking scale of 1 to 5 .
Where 5 signifies the greatest satisfaction level.
• Rating the confidence of a consumer in which brands of sporting
goods are most wanted or required
•Nominal-evaluating categorized answers for instance
• Place of residence, gender and preferred color

Five Rating Scale: Ordinal Attributes

The Association between Ordinal
and Nominal Data Utilizing a Scale
Rating
• Nominal Data :
• Nominal data is data whose observations or values can be given a number where the
code are just but For example, the number (code) one could denote female while
the number zero could denote male.
• An individual can tally data that is nominal, nevertheless, but he cannot measure or
put data in order.
• Ordinal data
• Ordinal data is data whose observations or values can be positioned through fixing to
it a rating sale
• This data can be placed and counted similar to what is displayed on the earlier slide
instance satisfaction of the consumer, however ordinal data cannot be quantified.

Qualitative Characteristics
• Qualitative data is statistical
• Utilizing numerical data scientifically ( that is Organization W would
need to contemplate):
• Evaluating food moisture of snacks.
• Value for calories for instance trans fat, fat, vitamin content and
sugar etc.

Ratio and Interval Data
Differences
•Interval:
• Statistical.
• Unvarying understanding all along.
• Zero and not perfect.
•Ratio
• Most informative and numerical.
• It possess a point that is true where the position of zero denotes the lack
measured quantity.

Variance Between Sample and
Population
•Sample: “n” represents the population sample.
• City –n=Fifty
• Nationally-n= Five thousand
• State- n=Five hundred
•Population: “N” signifies the entire population
• City-N=Fifty thousand
• Nationally-N= Six million
• State –N=Five hundred thousand

References