How to Analyse Ordinal Data

How to Analyse Ordinal Data

Ordinal data is information that can be organized into ordered categories. This type of data is less precise than other types of information in the world of statistical analysis, but it is still useful, particularly in more informal contexts. Find out more about ordinal data and how to analyse it.


What Is Ordinal Data?

Ordinal data is a type of categorical data that is broad but still follows a natural order. To collect this type of data, statisticians will frequently use a Likert scale (a metric that groups together various options for participants to select as their preference). While ordinal data is most useful in descriptive statistics, it can also be used in inferential statistics.


A Brief Overview of Levels of Measurement

Compare ordinal data to the various levels of measurement used in statistics to determine what makes it unique. The following are the most common broad categories:

1. Interval data: The space between each category in this type of quantitative data is equal. An interval scale of measurement can thus capture numeric values better than an ordinal scale, given that the latter frequently employs much more nebulous categories.

2. Nominal data: Nominal data is a set of categorical variables that lack numerical values. On the nominal scale, you have a list of options from which interested parties can choose without any inherent ordering ability. To understand the distinction between nominal and ordinal data scales, imagine a statistician asking people to choose their favorite movie from a list rather than rating a movie.

3. Ordinal data: Each category in ordinal data has an inherent numeric value. In other words, you take nominal variables and imbue them with an innate sense of ordering. Assume you want a large and independent sample size of people to rate a movie on a scale of one to five stars. You would use an ordinal dataset to accomplish this.

4. Ratio data: You can use much more precise quantitative variables when using the ratio scale for statistics. Ratio scales have a true zero at the bottom and equal intervals between each data point. You could, for example, rank people based on their actual income level. In an ordinal scale of data, however, you would only be able to do so in broader categories.


3 Ordinal Data Examples

Ordinal data has a wide range of real-world applications. Consider the following pragmatic ordinal data examples:

1. Customer satisfaction surveys: Assume you want to collect qualitative information about customer satisfaction. You could provide respondents with a sheet on which they could check the boxes "dissatisfied," "less than satisfied," "satisfied," "more than satisfied," and "extremely satisfied." In this case, you can also assign a numeric value (from one to five) to each variable in the ordered list.

2. Education level data: Assume you want to ask each member of a sample group about their level of education. You intend to select "high school graduate," "high school with additional certifications," "some college," "college graduate," and "graduate school graduate" as your options. This serves as an ordinal data set. Assume you wanted to rank data on standardized test performance percentiles. Because those are on a more quantitative scale, you'd probably use interval or ratio analysis instead of an ordinal approach.

3. Political questionnaires: Assume you want to know how people feel about a particular political issue. You could send out a survey asking them to rate the personal importance of the issue on a scale of one to five (one being not at all important and five being extremely important).


How to Analyse Ordinal Data

Ordinal data analysis can provide valuable insight into an ordered set of information. Consider the following suggestions as you strive to excel at this type of statistical analysis:

1. Recognize your own limitations. Remember that ordinal data is more difficult to quantify and experiment with than other, more precise types of information. Advanced regression analysis, for example, will be both unnecessary and impossible with an ordinal dataset. Instead, use statistical methods that are appropriate for this type of data. Nonparametric tests, such as the Spearman's rank correlation coefficient metric, are especially helpful.

2. Make use of statistical tests. Certain standard statistical tests, such as ANOVA and t-tests, are ineffective when analyzing ordinal data. Instead, for this type of information, opt for custom-made tests. One of the most common is the Mann-Whitney U test, which is followed by the Kruskal-Wallis one-way test and the Wilcoxon signed-rank test.

3. Make a data visualization. Choose a visual representation of an ordinal scale to help you understand it. Bar charts are a simple and popular choice, but you can use any type of statistical graph that suits your specific needs and preferences.


3 Uses for Ordinal Data

Ordinal data is a useful metric, particularly for small datasets. Here are a few applications for this type of data analysis:

1. Gathering qualitative data: Ordinal variables are frequently very useful qualitative points of information in data collection. While you may have to avoid using ordinal data in more quantitative cases, using ordinal scales makes gathering information about qualitative categories very simple.

2. Depicting basic statistical data: Ordinal scales may be all you need to represent basic categorical or numerical data. For example, if all you need is a bar graph to represent responses to a five-option questionnaire, you might end up overcomplicating things by using an interval or ratio scale.

3. Finding the median and mode: When assessing measures of central tendency, ordinal data can be used to find the median and mode in an abstract sense. However, keep in mind that this type of dataset makes it impossible to determine the mean average of your data without including more quantifiable information.