Data science revolves around the processing and analysis of data utilizing a range of tools and techniques. In today’s data-driven world, we come across types of data each requiring handling and interpretation. It is important to understand different types of data for proper data analysis and statistical interpretation. The type of data determines the proper statistical methods and operations that should be used. Various data types need different analysis and interpretation methods to draw significant conclusions. In this article we will explore the concept of data, and its significance provide real-world examples, and guide you through ways to work with it.
Levels of Measurement
Before analyzing a dataset, it is crucial to identify the type of data it contains. Luckily, all data can be grouped into one of four categories: nominal, ordinal, interval, or ratio data. Although these are often referred to as “data types,” they are actually different levels of measurement. The level of measurement reflects the accuracy with which a variable has been quantified, and it determines the methods that can be used to extract insights from the data.
The four categories of data are not always straightforward to distinguish and instead belong to a hierarchy, with each level building on the preceding one.
There are four types of data: categorical, which can be further divided into nominal and ordinal, and numerical, which can be further divided into interval and ratio. The nominal and ordinal scales are relatively imprecise, which makes them easier to analyze, but they offer less accurate insights. On the other hand, the interval and ratio scales are more complex and difficult to analyze, but they have the potential to provide much richer insights.
- Nominal Data – Nominal data is a basic data type that categorizes data by labeling or naming values such as Gender, hair color, or types of animal. It does not have any hierarchy.
- Ordinal Data – Ordinal data involves classifying data based on rank, such as social status in categories like ‘wealthy’, ‘middle income’, or ‘poor’. However, there are no set intervals between these categories.
- Interval Data – Interval data is a way of organizing and comparing data that includes measured intervals. Temperature scales, like Celsius or Fahrenheit, are good examples of interval data. However, interval data doesn’t have a true zero, meaning that a measurement of “zero” can still represent a quantifiable measure (like zero degrees Celsius, which is just another point on the scale and doesn’t actually mean there is no temperature present).
- Ratio Data – The most intricate level of measurement is ratio data. Similar to interval data, it categorizes and arranges data, utilizing measured intervals. But, unlike interval data, ratio data includes a genuine zero. When a variable is zero, there is no presence of that variable. A prime illustration of ratio data is height measurement, which cannot be negative.
What is Nominal Data?
Categorical data, also known as nominal data, is a crucial type of information utilized in diverse fields such as research, statistics, and data analysis. It comprises of categories or labels that help in classifying and arranging data. The essential feature of categorical data is that it does not possess any inherent order or ranking among its categories. Instead, these categories are separate, distinct, and mutually exclusive.
For example, Nominal data is used to classify information into distinct labels or categories without any natural order or ranking. These labels or categories are represented using names or terms, and there is no natural order or ranking among them. Nominal data is useful for qualitative classification and organization of information, enabling researchers and analysts to group data points based on specific attributes or characteristics without implying any numerical relationships.
- Eye color categories like “blue” or “green” represent nominal data. Each category is distinct, with no order or ranking.
- Smartphone brands like “iPhone” or “Samsung” are nominal data. There’s no hierarchy among brands.
- Transportation modes like “car” or “bicycle” are nominal data. They are discrete categories without inherent order.
Characteristics of Nominal Data
- Data that is classified as nominal is comprised of categories that are completely separate and distinct from one another.
- Data that falls under the nominal category is distinguished by descriptive labels rather than any numeric or quantitative value
- Nominal data cannot be ranked or ordered hierarchically, as no category is superior or inferior to another.
Example
Here are a few examples of how nominal data is used to classify and categorize information into distinct and non-ordered categories:
1. Colors of Car: Car colors are nominal data, with clear categories but no inherent order or ranking. Each car falls under one color category, without any logical or numerical connection between colors.
2. Types of Fruits: Fruit categories in a basket are nominal. Each fruit belongs to a specific category with no hierarchy or order. All categories are distinct and discrete.
3. Movie Genres: Movie genres are nominal data since there’s no ranking among categories like “action” or “comedy.” Each genre is unique, but we can’t say if one is better than another based on this data alone.
What is Ordinal Data?
Ordinal data is a form of qualitative data that classifies variables into descriptive categories. It is characterized by the fact that the categories it employs are ranked on some sort of hierarchical scale, such as from high to low. Ordinal data is the second most complicated type of measurement, following nominal data. Although it is more intricate than nominal data, which lacks any inherent order, it is still relatively simplistic.
For example, Ordinal data is a type of data used to categorize items with a meaningful hierarchy or order. These categories help us to compare and rank different achievements, positions, or performance of students, even if the intervals between them are not equal. Ordinal data is useful for understanding ordered choices or preferences and for assessing relative differences.
- School Grades: Grades like A, B, C are ordinal data, ranked by achievement, but intervals between them vary.
- Education Level: Levels like high school, bachelor’s, master’s are ordinal data, ordered by education, but gaps between levels differ.
- Seniority Level: Job levels like entry, mid, senior are ordinal data, indicating hierarchy, but the gap varies by job and industry.
Characteristics of Ordinal Data
- Ordinal data falls under the category of non-numeric and categorical data, but it can still make use of numerical values as labels.
- Ordinal data are always ranked in a hierarchy (hence the name ‘ordinal’).
- Ordinal data may be ranked, but their values are not evenly distributed.
- With ordinal data, you can calculate frequency distribution, mode, median, and range of variables.
Example
Here are a few examples of how ordinal data is used in fields and domains:
1. Educational Levels: Ordinal data is commonly used to represent education levels, such, as ” school,” “bachelors degree,” “masters degree,” and “Ph.D.” These levels have an order.
2. Customer Satisfaction Ratings: Another application of data is in customer satisfaction surveys. These surveys often ask respondents to rate their experience on a scale, from “poor” to “excellent.”
3. Economic Classes: classes including ” class ” “middle class,” and “upper class ” can be classified as ordinal data based on their ranking.
These examples demonstrate the ways in which ordinal data is utilized across fields and domains.
Nominal Vs Ordinary Data
|
Characteristics |
Nominal data |
Ordinal Data |
|---|---|---|
|
Nature of Categories |
Distinct and Discrete |
Discrete and Distinct |
|
Order/Ranking |
No inherent order |
Has a clear order or ranking |
|
Numerical Values |
No meaningful numerical values |
No meaningful numerical values |
|
Analysis Techniques |
Frequency counts, percentages, bar charts |
Ranking, median, non-parametric tests, ordered bar charts, ordinal regression |
|
Example |
Colors, gender, types of animals |
School grades, education level, seniority level |
|
Interpretation |
Used for classification and grouping based on category |
Used for assessing ordered preferences, hierarchy, or rankings |