Along with counting, we can calculate percentile, quartile, median, rank-order correlation or other summary statistics from ordinal data.ģ. It indicates the relative position but it doesn’t indicate the magnitude of the difference between the objects. Ordinal Scale : An ordinal scale is a ranking scale in which numbers(ranks) are assigned to objects to indicate the relative extent to which the objects posses some characteristic. The only mathematical operation we can do is counting on nominal scale.Ģ. For example a person with higher SSN number is not superior to those with lower value SSN number. The numbers in a nominal scale do not reflect the amount of the characteristic possessed by the object. Another example could be Social Security Number. Here each number is assigned to only one runner and the numbers are unique. For example, the number assigned to the runner in a race is nominal. Nominal Scale : This is a figurative labeling scheme in which the numbers serve only as labels or tags for identifying and classifying objects. These scales are summarized in Fig – 2.ġ. There are four primary scales of measurement : nominal, ordinal, interval and ratio. Now that we understand types of data, lets understand types of scales used to measure these data types. They can also be “Heat map” showing volume or concentration on a map. Charts that utilize locational data are often called “measles charts” or “concentration chart”. Locational data simply answers the question “where”. So, it becomes very important for us to know the types of data before we move into statistics, data science, marketing research or related field. If both Y and Xs are continuous then Regression can be used. For example, if Y (dependent variable) is continuous and Xs (independent variables) are discrete then we can use ANOVA to test means. What we measure is not the object but some characteristic of itīasically we use two types of data in our statistical analysis:īelow table illustrates how data type determines which statistical test can be applied in a given scenario.Data is objective information that everyone can agree on.If our data is discrete then we cannot apply some of the analysis types which work with continuous data only(Please refer to Fig-2). Basically application of any analysis type is linked with type of data, we have to first understand the type of data points available. When we plan to apply any particular analysis to test a hypothesis, we have to first make sure that required data types are available. In our data analysis we mostly use continuous and discrete type of data. There are three types of data, discrete, continuous and locational data. Once we have clarity on this, we will check 4 types of widely used scales for these data types.
Please don’t get confused with scales and data types, first we will understand what are the different types of data. We will cover following items in this module: You go through this module and I promise that you will not face any problem in identifying data types in your future data analysis work. Get complete understanding of Data types and their scales in Statistics/Math with easy to understand examples.ĭata type is a simple but very important topic as this forms the foundation of data analysis and hypothesis testing.
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