Types of Data in Statistics: Nominal, Ordinal, Discrete & Continuous

Data comes in various forms and it is important to properly classify different types of data in order to analyse and extract meaningful insights from it effectively. There are four main categories that data can typically fall under – nominal, ordinal, discrete and continuous. Proper identification and distinction between these four types of data is crucial for statistical analysis and making data-driven decisions. In this blog, we will discuss these four types of data in detail, including the differences between them.

Qualitative vs. Quantitative Data Distinction

The following are the main areas of distinction between qualitative and quantitative data:

• Quantitative data is measurable and numerical, while qualitative data is descriptive, narrative, or based on observation.
• Quantitative data focuses on areas like how many and how much. On the other hand, qualitative data focuses on how and why, as well as the nature of the experience.
• Quantitative data provides objective, precise, and standardized measurements, while qualitative data provides subjective, contextual, and interpretive measurements. 
• Quantitative data is collected through surveys, formal measurements, and experiments. On the contrary, qualitative data is collected through interviews, direct observation, focus groups, and case studies.
• Quantitative data focuses on statistical analysis, while qualitative data emphasizes thematic analysis, summarizing narratives, and content analysis.

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Nominal and Ordinal Data

Qualitative data describes by categorizing characteristics and attributes without specific numbers attached. It groups similar features together.

Nominal Data

Nominal data, also known as categorical data, assigns objects or variables into categories without any numerical value or order. The categorical labels themselves do not reflect any ranking or hierarchy among the categories.

For example, gender (Male, Female), colour (Red, Blue, Green), country of origin, etc., are all examples of nominal data since they categorise objects into labels without any inherent ordering. Names of each category represent nominal data as pie charts or bar graphs.

Ordinal Data

Ordinal data differs from nominal in the sense that while it also assigns categories, there exists a logical order or ranking among the categories. However, the ranking is subjective and does not denote the exact magnitude or difference between categories.

For instance, popularity/rating of movies (Blockbuster, Hit, Flop) and performance evaluation ratings (Exceeds Expectations, Meets Expectations, Needs Improvement) are all types of ordinal data since the categories can be logically ranked or ordered. Ordinal data is usually represented through bar or line graphs.

The key difference between nominal and ordinal data is that while nominal data categories do not imply any ordering or ranking, ordinal data categories do follow a hierarchy or ranking according to some attribute, even if the scale of difference is not equitably measured.

Discrete and Continuous Data

Discrete and continuous data fall under the umbrella of quantitative data. Quantitative data refers to numerical attributes that can be measured, counted or continuously variable.

Discrete Data

Discrete data consists of separate, distinct, discontinuous values or points that can be counted. Discrete variables take on integer values, such as the number of children in a family or the number of books sold by an author.

Some key properties of discrete data include:

• Can be counted but not measured continuously
• Represented through bar graphs as individual data points are separated
• Examples include a number of customers, light bulbs burnt out, coins in your pocket, etc.

Continuous Data

Continuous data consists of measurements that can take any real number value within a given range. Unlike discrete data, continuous variables can be measured precisely and may have an infinite set of possible values within a fixed interval.

Key aspects of continuous data include:

• Can be measured precisely as a real number
• Represented through line graphs or histograms
• Examples include temperature, time, weight, height, test scores on a scale, etc.

The main difference between discrete vs continuous data is that discrete can only take on integer values, whereas continuous can be measured precisely as real numbers within a range.

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Nominal vs Ordinal vs Discrete vs Continuous

To summarise the key differences between these four data types:

• Nominal data categorises objects without implying ordering. Ordinal does imply ordering but not scale.
• Discrete variables take on integer values and points; continuous are real numbers within ranges.
• Nominal and ordinal are qualitative, while discrete and continuous are quantitative.
• Nominal is shown by names, ordinal by rank. Discrete by separate points, continuous as smooth curves.

The proper identification and distinction between these four types is important for determining appropriate statistical tests and analysis techniques on any given dataset.

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Real-World Examples from Singapore Industries

The following are real-world examples of different types of statistical data from Singapore industries:

1. Nominal Data 

• The Kind Of Public Transport Used By Public Commuters
• Patients’ Blood Type Data on the Records of the National University Hospital 

2. Ordinal Data 

• Customer Satisfaction Rating For Services at Changi Airport 
• Ministry Of Education Data on Education Levels Of Students

3. Discrete Data

• The Number of People at a Mass Rapid Transit (MRT) Station As Monitored By Sensors at a Given Time
• The Number of COVID-19 Cases Reported by the Ministry of Health 

4. Continuous Data

• Real-Time Traffic Speed on the Central Expressway (CTE) As Measured By Internet of Things (IoT) Sensors 
• Real-Time Air Quality Index (AQI) Readings

Also read: Power BI vs. Tableau: Which Tool Do Singapore Companies Prefer?

Why Are The Different Types Of Data In Statistics Important?

In the sphere of policy making and government in Singapore, the different kinds of statistical data help with the following activities:

• Economic Forecasting and Planning 
• Infrastructure and Urban Planning 
• Pandemic Response and Public Health
• Social Planning and Manpower 

The government analyzes economic data to forecast future trends and formulate policies to maintain fiscal balance and drive sustainable economic development.  

Such statistical data is also used widely in the various private industries in Singapore, such as the following:

• Financial Services 
• Logistics and Transportation 
• Marketing and Electronic Commerce (E-Commerce)
• Manufacturing 

The banking sector in Singapore heavily relies on real-time data analytics to prevent and detect fraudulent transactions, develop targeted financial products for customers, and assess credit risks.

the integrity of your research and analysis. This also makes sure that you draw only meaningful and sound conclusions from the data you are working with.

Conclusion

Understanding how to distinguish between and classify different data is a crucial starting point for any data science project. It guides subsequent steps like wrangling, cleaning, modelling and interpreting results. With a firm grasp of data types, analysts can efficiently analyse any dataset.

Explore these online data science courses through upGrad Singapore:

• Master of Science in Data Science – Liverpool John Moores University
• Executive Diploma in Data Science and AI -IIIT Bangalore
• Post Graduate Certificate in Data Science & AI (Executive) – IIIT Bangalore

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