Ordinal Data Examples and 3 More Types of Data

Ordinal data is a type of categorical data in which the information is broad but still follows a natural order. Statisticians will often use a Likert scale (a metric grouping together various options for participants to select as their preference) to collect this type of data. While ordinal data is useful primarily in descriptive statistics, it has applications in inferential statistics as well.

Compare ordinal data against the different levels of measurement common in statistics to see what makes it unique. Here are the most common broad categories:

• Interval data: This type of quantitative data has an equal level of space between each category. An interval scale of measurement is thus able to better capture numeric values than an ordinal one, given the latter often utilizes much more nebulous categories.
• Nominal data: Devoid of any numerical values, nominal data is a set of categorical variables. On the nominal scale, you have a list of options interested parties can choose from without any innate ordering potential. To clarify the difference between nominal vs. ordinal scales of data, think of a statistician who wants people to choose their favorite movie from a list, as opposed to rating a movie.
• Ordinal data: Each of the different categories in ordinal data has an innate numeric value. In other words, you take nominal variables but give them a sense of innate ordering. For instance, imagine you want a broad and independent sample size of people to rank a movie from a scale of one to five stars. To do so, you would utilize an ordinal dataset.
• Ratio data: When using the ratio scale for statistics, you can use much more precise quantitative variables. Ratio scales have a true zero element at their base as well as equal intervals between each data point. For example, you could rank people based on their actual income level. By contrast, in an ordinal scale of data, you would only be able to do so in broader categories.

Ordinal data has many different real-world applications. Consider these examples of pragmatic ordinal data:

1. 1. Customer satisfaction surveys: Suppose you want to gather some qualitative data about customer satisfaction. You could give respondents a sheet with the boxes “dissatisfied,” “less than satisfied,” “satisfied,” “more than satisfied,” and “extremely satisfied” available to check. You can also attach a numeric value (from one to five in this case) for each variable in the ordered list.
2. 2. Education level data: Imagine you want to survey a sample group about each member’s level of education. You plan to use the options “high school graduate,” “high school with additional certifications,” “some college,” “college graduate,” and “graduate school graduate.” This would function as an ordinal data set. By contrast, suppose you wanted to rank data about standardized test performance percentiles. Since those fall along a more quantitative scale, it’s likely you would use interval or ratio analysis instead of an ordinal approach.
3. 3. Political questionnaires: Suppose you want to get a gauge on how people feel about a given political issue. You could send out a questionnaire asking them to rank the personal importance of that issue on a scale of one to five (one being not at all important and five being extremely important).

Analyzing ordinal data can grant you valuable insight into an ordered set of information. Take these tips into consideration as you strive to excel at this form of statistical analysis:

• Recognize limitations. Remember ordinal data is harder to quantify and experiment on than other, more precise forms of information. For example, advanced regression analysis will be both unnecessary and impossible with an ordinal dataset. Instead, opt for statistical methods suited to this type of information. Nonparametric tests like the Spearman’s rank correlation coefficient metric are particularly useful.
• Use statistical tests. Certain standard statistical tests—like ANOVA and t-tests—are ineffective when it comes to analyzing ordinal data. Opt for tailor-made tests for this type of information instead. The Mann-Whitney U test is one of the most common, as are the Kruskal-Wallis one-way test and the Wilcoxon signed-rank test.
• Visualize the data. To make sense of an ordinal scale, choose a way to represent it visually. Bar charts are a simple and common choice, although you can use any type of statistical graph suited to your own innate purposes and preferences.

Ordinal data is a helpful metric, especially for simple datasets. Here are just a few ways you can put this type of data analysis into use:

1. 1. Collecting qualitative variables: In terms of data collection, ordinal variables are often very useful qualitative points of information. While you might have to forgo using ordinal data in more quantitative cases, gathering information about qualitative categories is very simple when using ordinal scales.
2. 2. Depicting basic statistical data: If you want to represent basic categorical or numerical data, ordinal scales might be all you need. For instance, you might end up overcomplicating things with an interval or ratio scale if all you need is a bar graph to represent responses to a five-option questionnaire.
3. 3. Finding the median and mode: When it comes to assessing measures of central tendency, you can use ordinal data to find the median and the mode in an abstract sense. Still, keep in mind this type of dataset does, unfortunately, make it impossible for you to discover the mean average of your data without including more quantifiable information.

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