Summarization of Qualitative data & Quantitative

Qualitative data

data is a categorical measurement expressed not in terms of numbers, but rather by means of a natural language description. In statistics, it is often used interchangeably with "categorical" data. 

For example: favourite colour = "blue"  height = "tall"

Although we may have categories, the categories may have a structure to them. When there is not a natural ordering of the categories, we call these nominal categories. Examples might be gender, race, religion, or sport.
When the categories may be ordered, these are called ordinal variables. Categorical variables that judge size (small, medium, large, etc.) are ordinal variables. Attitudes (strongly disagree, disagree, neutral, agree, strongly agree) are also ordinal variables, however we may not know which value is the best or worst of these issues. Note that the distance between these categories is not something we can measure.

Quantitative data

data is a numerical measurement expressed not by means of a natural language description, but rather in terms of numbers. However, not all numbers are continuous and measurable. For example, the social security number is a number, but not something that one can add or subtract. 

 

For example: favourite colour = "450 nm" height = "1.8 m"

Quantitative data always are associated with a scale measure.Probably the most common scale type is the ratio-scale.

A more general quantitative measure is the interval scale. Interval scales also have a equidistant measure.