When working with data, it becomes essential to know the kind of data that you are managing. Knowing the data that you are dealing with allows you to determine how it can be organised, interpreted, and even analyzed. One of the most common forms of data that is used by researchers is Ordinal data. Now, if you are also someone who is surrounded by different kinds of data, then this guide will tell you what is Ordinal data in simple terms, how it is collected, and the top ways to analyse it.
Understanding the Meaning of Ordinal Data
Ordinal data is a specific type of data that is used to represent different categories with a meaningful order or ranking. However, the distances between categories is not equal or known in Ordinal data. In simple terms, Ordinal data is basically used to showcase the relative position of items, but not the exact distinctness between them. Let’s assume that there is a customer satisfaction survey with the following responses.
- Very Dissatisfied
- Dissatisfied
- Neutral
- Satisfied
- Very Satisfied
Here, these responses are categorized from highly satisfied to least satisfied, which shows an order. But, in reality, we cannot say what the difference is between ‘Satisfied’ and ‘Neutral’ or others. Hence, this data establishes an order, but there are no consistent measurements involved. There are different places where ordinal data is used.
- Educational levels (High school, Bachelor’s, Master’s, Doctorate)
- Rating scales (Poor, Fair, Good, Excellent)
- Socioeconomic status (Low, Middle, High)
- Class positions (1st, 2nd, 3rd, etc.)
How is Ordinal Data Collected?
Ordinal data is a common type of data collected through surveys, questionnaires, interviews, and ranking tasks. It is usually in the form of Likert scales or ordered categories. When creating the collection instruments for ordinal data, take the following points into account:
- Define unambiguous and well-sequenced categories: Respondents must have no doubts about the ranking, e.g., the scale is from low to high or least to most.
- Reliability: Only by maintaining the same order and meaning across all questions can reliability be secured.
- No category should cover another: The options for response should indicate a different level each.
- Conduct a pilot study of your tool: To know how the participants interpret the scale as one intends, the data collection on a large scale should be deferred.
For example, in a survey for feedback from customers, you might ask: “How satisfied are you with our service?” and give a 5-point scale from “Very Dissatisfied” to “Very Satisfied.”
Analysis of Ordinal Data
Non-parametric tests are preferred over the parametric ones since the latter assume numeric equality. In non-parametric approaches, however, the ordinal data is processed. However, there are certain ways of dealing with Ordinal Data and using it.
- Frequency tables: Indicate the number of responses for every category.
- Median and mode: These measures are more reliable indicators of central tendency than mean.
- Bar charts or histograms: Represent how data is distributed among different categories.
- Non-parametric statistical tests: Such as the Mann-Whitney U test, Kruskal-Wallis test, and Spearman’s rank correlation used to find relationships or differences.
When concluding the results, look at the direction and pattern of the responses, not just the numerical differences. Ordinal data is nothing but one of the most effective means in terms of ranking, but not precisely measuring, of understanding opinions, preferences, and perceptions. The website Datarecovee will tell you more about the different kinds of data that are used for analysis and research.