Data that can be arithmetically combined in meaningful ways, that is,
added, subtracted, multiplied, divided, or averaged. E.g. number of children, age,
number of years of experience, salary, sales, acreage
Discrete numerical data
This type of numerical data takes on whole number values and
usually represents a count of some kind. ”In-between” values do not, therefore, do not
make sense. E.g. number of children, age, number of years of experience. Note: This is
numerical data because adding, for example, numbers of children, ages, or years makes
sense. It is discrete because we usually round off age or years of experience to a whole
number of years for data collection in business
Continuous Numerical Data
Apart from rounding, this type of numerical data could
theoretically take on any number of in-between values because it is not counting
discrete things; rather it measures things whose magnitudes fall on a continuous scale.
E.g. salary, sales, weight, acreage. Note: This is numerical data because ”averaging”
salaries, sales, or weights makes sense. Weight and acreage are probably the only
data that clearly fall on a continuous scale, depending of course on the accuracy
of the scale (tenths, hundredths, thousandths, etc). Salary and sales are considered
continuous for all practical purposes, because, theoretically, they could be broken down
into hundredths of a dollar (cents), which are not whole numbers.
Categorical data
Data that is used to classify, type, or categorize groups of individual
things. E.g. Preference rankings (1, highest preferred, 5, least), Gender (male, female);
State (NY, WI, TN); Marriage status (M, U, D). Such data may be recorded (or
coded) using any kind of symbol: numbers, words, or letters.
Ordinal categorical data
In addition to classifying or categorizing, this type of data also
has an inherent order that provides additional information. E.g. The numbers 1 through
5 in an opinion poll where 1 is the most preferred and 5 the least preferred Note: This
is categorical data because adding ”most preferred” to ”least preferred” does not make
sense. Also, the integers 1-5 are not used to ”count” data and hence do not constitute
discrete numerical data
Nominal categorical data
This type of categorical data contains no inherent order but
merely classifies or categorizes information. E.g. Gender (male, female); State: NY,
WI, TN; Marriage status (M, U, D)
Quantitative data
Categorical data is often referred to as qualitative.
Quantitative data
Numerical data is often referred to as quantiative.