In statistics, ordinal data are the type of data in which the values follow a natural order. One of the most notable features of ordinal data is that the differences between the data values cannot be determined or are meaningless. Generally, the data categories lack the width representing the equal increments of the underlying attribute.
In some cases, the values of interval or ratio data can be grouped together to obtain the data’s characteristics. For example, the ranges of income are considered ordinal data while the income itself is the ratio data.
Unlike interval or ratio data, ordinal data cannot be manipulated using mathematical operators. Due to this reason, the only available measure of central tendency for datasets that contain ordinal data is the median.
Uses of Ordinal Data
Ordinal data are commonly employed in various surveys and questionnaires. The Likert scale that you may find in many surveys is one example. The Likert scale lists the categories of the psychometric scale such as “Strongly Agree,” “Agree,” etc.
Various examples of this data type can be frequently encountered in finance and economics. Consider an economic report that investigates the GDP levels of different countries. If the report ranks the countries according to their GDP figures, the ranks are examples of ordinal data.
How to Analyze the Data?
The simplest way to analyze ordinal data is to use visualization tools. For instance, the data may be presented in a table in which each row indicates a distinct category. In addition, they can also be visualized using various charts. The most commonly used chart for representing such types of data is the bar chart.
Ordinal data can also be analyzed using advanced statistical analysis tools such as hypothesis testing. Note that the standard parametric methods such as t-test or ANOVA cannot be applied to such types of data. The hypothesis testing of the data can be carried out only using nonparametric tests such as the Mann-Whitney U test or Wilcoxon Matched-Pairs test.
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