Statistics Concepts and Descriptive Measures
Quantitative data is always in numerical form; hence, mathematical operations such as
addition, subtraction, division, multiplication etc. can be performed on such data. While,
qualitative data is mostly comprised of descriptions and is not numerical. Numbers can be
allocated in qualitative data for identification purpose but those numbers do not reflect any
value. Mathematical operations cannot be performed on qualitative data. In the given
consumer food dataset, the data presented in column of Annual Household Income, Annual
Food Spending and Non mortgage household debt is quantitative data. While, the data
presented in column of Region and Location is qualitative data (Grbich, 2013).
There are following four levels of measurement proposed by S. S. Stevens.
1. Nominal measurements deal with categories and names but do not allow ordering.
2. Ordinal data can be ordered but does not provide meaningful differences between the
data.
3. Interval data provides meaningful inter-data differences, but it lacks a starting point or
a zero value; hence does not offer to calculate the ratio between the data (example,
temperature scale).
4. Ratio level data hold all the features of interval data along with a zero value; hence it
allows ratio between the data. (Salkind, 2012)
The level of measurement for each column in the selected dataset of consumer food is
described below:
1. Annual Household Income: Ratio
STATISTICS CONCEPTS AND DESCRIPTIVE MEASURES
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