Types of research data

The specific attribute that is being measured or observed is known as a variable. For example, height, weight, annual revenue or annual expenses could be variables. There are 3 common scales for measuring values of a variable:

Cardinal
This is the most common scale for measuring variables. Cardinal values may be ranked and compared in a meaningful way. Revenue and expenses are examples of cardinal data values because the amount of money can be ranked and the difference in values is meaningful in a financial or economic context.
Ordinal
These values are similar to cardinal values in that they may be ranked. However, the important difference is that ordinal values may not be compared in a meaningful way. For example, let's take 5 random companies and list them in order of lowest to highest annual profit. Each company will be assigned a rank of 1 to 5. The order of the rank values are meaningful to the extent that the company at rank 1 has less annual profit than the company at rank 2. However, it is not possible to estimate or predict that the annual profit of the company at rank 2 is twice as high as the company at rank 1. The difference in annual profit between each rank is not constant or predictable.
Nominal
These types of data have the most limitations for analysis. Nominal values are classified by categories and may be not be ranked or compared in a meaningful way. For example, values such as yes and no, heads and tails, male and female and Tory and Labour are nominal values. These values may not be ranked, e.g. yes does not necessarily come before or after no and male does not necessarily come before or after female. Additionally, these values may not be compared, e.g. heads are not necessarily higher or lower than tails and Tory is not necessarily higher or lower than Labour.

Numeric values may be described as continuous or discrete.

Continuous variables may take on any value within a range. For example, 20, -52 or 25.91 are all possible temperature values.
Discrete variables are limited to fixed values within a range. For example, the number of students in a room must be integer values. It does not make sense to have 25.91 students in a room!

Data values (sample) are collected from the larger group (population) containing all possible elements. Analysis of the sample is used to draw conclusions (inferences) about the larger population.

Lists of data variables can be represented in an R script by using the c command.