" You know that "Agree" is more positive than "Disagree," but you cannot assume the psychological distance between "Agree" and "Neutral" is the same as between "Neutral" and "Disagree. Ordinal data introduces a meaningful sequence or ranking, but the intervals between those ranks are not necessarily equal.
Understanding the Defining Characteristics of Ordinal Data
A common example is survey responses on a Likert scale, such as "Strongly Disagree," "Disagree," "Neutral," "Agree," and "Strongly Agree. Understanding the distinction between ordinal and ratio data is fundamental for anyone working with quantitative information, from researchers and analysts to students and business professionals.
Classic examples include physical measurements: height, weight, age, temperature in Kelvin, and time duration. Similarly, an age of 20 years is exactly half of 40 years, and a length of 0 meters means there is no length at all.
Understanding the Defining Characteristics of Ordinal Data
You can calculate the mean, median, mode, standard deviation, and perform a vast array of parametric statistical tests like the t-test or ANOVA. However, calculating a mean is generally inappropriate because you cannot reliably add or average the ranks.
More About Ordinal vs ratio data
Looking at Ordinal vs ratio data from another angle can help expand the discussion and give readers a second clear paragraph under the same section.
More perspective on Ordinal vs ratio data can make the topic easier to follow by connecting earlier points with a few simple takeaways.
