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4 Types of Data: Nominal, Ordinal, Discrete, Continuous

Table of Contents

Summary:

In this Article, you will learn about what are the 4 Types of Data in Statistics.

Qualitative Data Type

  • Nominal
  • Ordinal

Quantitative Data Type

  • Discrete
  • Continuous

The two types of data are qualitative and quantitative. Qualitative data is descriptive and conceptual, while quantitative data is numerical and can be measured statistically.

Read more to know each in detail.

Introduction

Data science is all about experimenting with raw or structured data. Data is the fuel that can drive a business to the right path or at least provide actionable insights that can help strategize current campaigns, easily organize the launch of new products, or try out different experiments.

All these things have one common driving component and this is Data. We are entering into the digital era where we produce a lot of Data. For instance, a company like Flipkart produces more than 2TB of data on daily basis. 

In simple terms, data is a systematic record of digital information retrieved from digital interactions as facts and figures. Types of statistical data work as an insight for future predictions and improving pre-existing services. The continuous data flow has helped millions of organizations to attain growth with fact-backed decisions. Data is a vast record of information segmented into various categories to acquire different types, quality, and characteristics of data, and these categories are called data types.

When this Data has so much importance in our life then it becomes important to properly store and process this without any error. When dealing with datasets, the category of data plays an important role to determine which preprocessing strategy would work for a particular set to get the right results or which type of statistical analysis should be applied for the best results. Let’s dive into some of the commonly used categories of data.

Qualitative Data Type

Qualitative or Categorical Data describes the object under consideration using a finite set of discrete classes. It means that this type of data can’t be counted or measured easily using numbers and therefore divided into categories. The gender of a person (male, female, or others) is a good example of this data type.

These are usually extracted from audio, images, or text medium. Another example can be of a smartphone brand that provides information about the current rating, the color of the phone, category of the phone, and so on. All this information can be categorized as Qualitative data. There are two subcategories under this:

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Nominal

These are the set of values that don’t possess a natural ordering. Let’s understand this with some examples. The color of a smartphone can be considered as a nominal data type as we can’t compare one color with others.

It is not possible to state that ‘Red’ is greater than ‘Blue’. The gender of a person is another one where we can’t differentiate between male, female, or others. Mobile phone categories whether it is midrange, budget segment, or premium smartphone is also nominal data type.

Nominal data types in statistics are not quantifiable and cannot be measured through numerical units. Nominal types of statistical data are valuable while conducting qualitative research as it extends freedom of opinion to subjects.

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Ordinal

These types of values have a natural ordering while maintaining their class of values. If we consider the size of a clothing brand then we can easily sort them according to their name tag in the order of small < medium < large. The grading system while marking candidates in a test can also be considered as an ordinal data type where A+ is definitely better than B grade. 

These categories help us deciding which encoding strategy can be applied to which type of data. Data encoding for Qualitative data is important because machine learning models can’t handle these values directly and needed to be converted to numerical types as the models are mathematical in nature.

For nominal data type where there is no comparison among the categories, one-hot encoding can be applied which is similar to binary coding considering there are in less number and for the ordinal data type, label encoding can be applied which is a form of integer encoding.

Difference Between Nominal and Ordinal Data

Aspect Nominal Data Ordinal Data
Definition Categories data into distinct classes or categories without any inherent order or ranking. Categories data into ordered or ranked categories with meaningful differences between them.
Examples Colors, gender, types of animals Education levels, customer satisfaction ratings
Mathematical Operations No meaningful mathematical operations can be performed (e.g., averaging categories). Limited mathematical operations can be performed, such as determining the mode or median.
Order/ Ranking No natural or meaningful order exists. Categories have a specific order or ranking, but the magnitude of differences between ranks may not be uniform.
Central Tendency Mode (most frequent category) Mode, median (middle category), but mean is not typically used due to lack of uniform interval between ranks.
Example Use Case Classifying objects, grouping data Rating scales, survey responses, educational levels

Quantitative Data Type

This data type tries to quantify things and it does by considering numerical values that make it countable in nature. The price of a smartphone, discount offered, number of ratings on a product, the frequency of processor of a smartphone, or ram of that particular phone, all these things fall under the category of Quantitative data types.

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The key thing is that there can be an infinite number of values a feature can take. For instance, the price of a smartphone can vary from x amount to any value and it can be further broken down based on fractional values. The two subcategories which describe them clearly are:

Discrete

The numerical values which fall under are integers or whole numbers are placed under this category. The number of speakers in the phone, cameras, cores in the processor, the number of sims supported all these are some of the examples of the discrete data type.

Discrete data types in statistics cannot be measured – it can only be counted as the objects included in discrete data have a fixed value. The value can be represented in decimal, but it has to be whole. Discrete data is often identified through charts, including bar charts, pie charts, and tally charts.

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Continuous

 The fractional numbers are considered as continuous values. These can take the form of the operating frequency of the processors, the android version of the phone, wifi frequency, temperature of the cores, and so on. 

Unlike discrete data types of data in research, with a whole and fixed value, continuous data can break down into smaller pieces and can take any value. For example, volatile values such as temperature and the weight of a human can be included in the continuous value. Continuous types of statistical data are represented using a graph that easily reflects value fluctuation by the highs and lows of the line through a certain period of time. 

Difference between Discrete Data and Continous Data

Aspect Discrete Data Continuous Data
Definition Consists of distinct, separate values. It can take any value within a given range.
Examples Number of students in a class, coin toss outcomes (1, 2, 3), customer count. Height, weight, temperature, time.
Nature Usually involves whole numbers or counts. Involves any value along a continuous spectrum.
Gaps in values Gaps between values are common and meaningful. Values can be infinitely divided without gaps.
Measurement Often measured using integers. Measured with decimal numbers or fractions.
Graphical representation Typically represented with bar charts or histograms. Represented with line graphs or smooth curves.
Mathematical Operations Typically involves counting or summation. Involves arithmetic operations, including fractions and decimals.
Probability Distribution Typically represented using probability mass functions Typically represented using probability density functions.
Example Use Case Counting occurrences, tracking integers. Measuring quantities and analyzing measurements.

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Importance of Qualitative and Quantitative Data

Qualitative types of data in research work around the characteristics of the retrieved information and helps understand customer behavior. This type of data in statistics helps run market analysis through genuine figures and create value out of service by implementing useful information. Qualitative types of data in statistics can drastically affect customer satisfaction if applied smartly.

On the other hand, the Quantitative data types of statistical data work with numerical values that can be measured, answering questions such as ‘how much’, ‘how many’, or ‘how many times’. Quantitative data types in statistics contain a precise numerical value. Therefore, they can help organizations use these figures to gauge improved and faulty figures and predict future trends.

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Can Ordinal and Discrete type overlap?

If you pay attention to this, you can give numbering to the ordinal classes, and then it should be called discrete type or ordinal? The truth is that it is still ordinal. The reason for this is that even if the numbering is done, it doesn’t convey the actual distances between the classes.

For instance, consider the grading system of a test. The respective grades can be A, B, C, D, E, and if we number them from starting then it would be 1,2,3,4,5. Now according to the numerical differences, the distance between E grade and D grade is the same as the distance between the D and C grade which is not very accurate as we all know that C grade is still acceptable as compared to E grade but the mid difference declares them as equal.

You can also apply the same technique to a survey form where user experience is recorded on a scale of very poor to very good. The differences between various classes are not clear therefore can’t be quantified directly. 

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Different Tests

We have discussed all the major classifications of Data. This is important because now we can prioritize the tests to be performed on different categories. Now it makes sense to plot a histogram or frequency plot for quantitative data and a pie chart and bar plot for qualitative data.

Regression analysis, where the relationship between one dependent and two or more independent variables is analyzed is possible only for quantitative data. ANOVA test (Analysis of variance) test is applicable only on qualitative variables though you can apply two-way ANOVA test which uses one measurement variable and two nominal variables.

In this way, you can apply the Chi-square test on qualitative data to discover relationships between categorical variables.

Analysis of Variance (ANOVA) Test

Utilizing ANOVA as a statistical method, we can analyze the variances among group means within a sample, its particular value lies in comparing three or more groups. This type of data allows us to determine if there exist statistically significant differences between them. Here’s an overview of the ANOVA test:

1. Purpose

  • Employing ANOVA, we can assess for statistically significant differences between the means of three or more independent (unrelated) groups. This is achieved by a comparison – the variation between group means versus that within the groups.

2. Assumptions

  • Independence Observations within each group must be independent.
  • Homogeneity of Variance: The variances within each group should be roughly equal.

3. Types of ANOVA

  • One-Way ANOVA: Compares means across three or more groups within a single independent variable.
  • Two-Way ANOVA: Analyzes the influence of two different independent variables on a dependent variable.

4. Hypotheses

  • Alternative Hypothesis (Ha): Suggests that at least one group means is different from the others.

5. Test Statistic

  • ANOVA produces an F-statistic, which is the ratio of the variance among group means to the variance within the groups. The larger the F-statistic, the more likely it is that the group means are different.

6. Procedure

  • Step 1: Collect and organize the type of data from the groups.
  • Step 2: Calculate the mean, sum of squares, and degrees of freedom for both within-group and between-group variations.
  • Step 3: Compute the F-statistic.
  • Step 4: Determine the critical region and compare the calculated F-statistic to the critical value.
  • Step 5: Make a decision about the null hypothesis.

7. Interpretation

  • A p-value lower than the selected significance level (typically 0.05) leads to rejection of the null hypothesis, signifying differences among group means.
  • Should the p-value surpass the significance level, we cannot reject the null hypothesis due to inadequate evidence.

8. Post-Hoc Tests

  • ANOVA signaling significant differences triggers the need for post-hoc tests. These may include Tukey’s HSD or Bonferroni correction. Their purpose is to pinpoint specific group disparities.

9. Use Cases

  • Various fields like psychology, medicine, finance and experimental sciences apply ANOVA to compare means across distinct groups. This application enables drawing population difference-based conclusions.

10. Limitations

  • Assumes normality and homogeneity of variances.
  • Sensitive to outliers.

Understanding and appropriately applying ANOVA is crucial for researchers and analysts aiming to compare multiple groups efficiently and draw meaningful conclusions from their data.

 

Why Are Data Types Important in Statistics? 

Data types play a crucial role in statistics for several reasons:

1. Data Understanding

Data types provide information about the nature of the variables and the kind of values they can take, aiding in understanding the dataset.

2. User Training and Adoption

Educating users on data types fosters better understanding and utilization of analytical tools. Users can make informed decisions about which analyses and visualizations are suitable for their data.

3. Analysis Selection

Different data types require different analysis techniques. Choosing the appropriate analysis method depends on the data types involved.

4. Data Exploration Efficiency

Efficient exploration of datasets is facilitated by understanding data types. Analysts can quickly identify key variables, assess their distributions, and gain insights into the characteristics of the data.

5. Statistical Tests

The choice of statistical tests depends on the data types of variables. Parametric tests are used for continuous data, while non-parametric tests are suitable for categorical or ordinal data.

6. Data Treatment

Understanding data types helps decide how to effectively handle missing values, outliers, and other data anomalies.

7. Visualization

Data types determine the visualizations most appropriate for conveying insights, such as bar charts for categorical data and histograms for continuous data.

8. Data Transformation

Data types influence the need for data transformation, such as normalizing or standardizing continuous variables for certain analyses.

9. Sampling Strategies

When designing sampling methodologies, understanding types of data in data science aids in creating representative samples. This is especially important in stratified sampling, where different strata may have distinct data characteristics.

10. Error Identification

Recognizing and addressing errors in data entry or measurement is easier when the expected data types are known. Inconsistencies can be identified by comparing actual data types with the anticipated ones.

11. Model Building

In machine learning and regression analysis, the type of dependent and independent variables affects the choice of algorithms and the model’s assumptions.

12. Interpretation

Data types impact how results are interpreted. The meaning of statistical measures like mean, median, and mode varies based on whether the data is continuous, discrete, or categorical.

13. Accuracy and Validity

Misidentifying data types can lead to incorrect analyses, invalid conclusions, and inaccurate predictions.

14. Customized Data Processing

Data and its types may require unique preprocessing steps. Tailoring data processing workflows to the specific characteristics of each type enhances the accuracy and relevance of analytical outcomes.

15. Data Governance

Establishing and enforcing types of data in computer governance policies involves defining and adhering to standards for data types. This ensures consistency, quality, and compliance within an organization.

16. Data Integration

Understanding data types ensures consistency and compatibility between datasets when combining data from different sources.

17. Data Validation

Properly identifying and assigning data and its types helps ensure data accuracy and validity. Validation processes rely on understanding the nature of variables, ensuring that data conforms to expected formats and ranges.

18. Feature Engineering

In machine learning, selecting and transforming features (variables) is crucial. Knowledge of types of data in management information system guides the creation of meaningful features, improving model performance and interpretability.

19. Data Privacy and Security

Sensitivity to data types helps preserve data privacy by ensuring that the appropriate anonymization techniques are applied based on the data’s nature.

20. Reporting and Communication

Accurate identification of data types ensures that findings are communicated clearly and accurately to stakeholders and decision-makers.

21. Efficient Storage

Understanding data types helps in efficient data storage and retrieval, optimizing database performance.

22. Data Cleaning

Different data types may require specific cleaning approaches. Handling missing values, outliers, and inconsistencies is more effective when considering the unique characteristics of each types of data in computer.

23. Resource Allocation

Data types affect memory and processing requirements. The efficient allocation of resources depends on accurate knowledge of data types.

24. Algorithm Compatibility

Certain algorithms are designed for specific data types. Matching the algorithm to the data type enhances computational efficiency and the overall performance of the analysis.

25. Cross-Domain Collaboration

In collaborative environments, where individuals from diverse domains work with data, a shared understanding of data types promotes effective communication and collaboration. It reduces ambiguity and ensures a common language for discussing data-related concepts.

26. Adaptation to Evolving Technologies

As data storage and processing technologies evolve, understanding data types becomes crucial for adopting and adapting to new platforms. It ensures seamless migration and utilization of emerging tools and frameworks.

27. Facilitating Data Integration

Integrating data from various sources becomes smoother when data types are well-defined. Consistent data types across sources enhance interoperability and prevent complications during the integration process.

28. Data Lifecycle Management

Throughout the data lifecycle, from collection to archiving, considering data types is essential. It influences decisions about retention periods, archival formats, and the overall management strategy for different types of data science.

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29. Enhancing Data Exploration Tools

Data exploration tools and platforms benefit from a clear understanding of data types. Features like automated visualizations, descriptive statistics, and recommendations can be optimized based on the characteristics of the data types present in a dataset.

Emerging Trends in Data Types

Some of the types of data in management information system are: –

1. Graph Data

Especially in social networks, recommendation systems, and fraud detection graph databases and graph data models are gaining traction. Representing relationships and connections graph data proves its worth by uncovering patterns within complex networks.

2. Experiential Data

As user interactions become more immersive, capturing experiential data beyond traditional metrics is crucial. This includes user sentiments, emotions, and experiences, providing a more holistic understanding of user behavior.

3. Exo-Structural Data

With data expanding beyond organizational boundaries, exo-structural data involves integrating external data sources like open data, social media feeds, and third-party APIs. This enhances the context and depth of analysis.

4. Explainable Data

AI is not the sole focus. Instead, an increasing emphasis is being placed on comprehensible data. To foster trust and compliance particularly in regulated industries understanding the reasons behind results, rather than merely accepting them, has become indispensable for us.

5. Synthetic Data

To address privacy concerns, synthetic data generation is on the rise. Simulated datasets that retain statistical properties of real data but without sensitive information are used for testing algorithms and models.

6. Dark Data

The term “dark data” denotes the amount of unstructured, untapped information that resides within organizations. As we focus on unlocking concealed value, applying advanced analytics and machine learning to extract insights from this dark data is becoming increasingly imperative.

7. Hybrid Data Types

Blurring the lines between traditional categories, hybrid types of data involve combinations of structured and unstructured data. This poses new challenges and opportunities in terms of storage, processing, and analysis.

8. Exogenous Variables

Analyses that incorporate external factors such as weather conditions, economic indicators, or social events as exogenous variables offer a more comprehensive understanding of the influences on outcomes.

9. Blockchain Data Structures

Blockchain data structures are being incorporated into more and more industries outside of cryptocurrency. The implementation of smart contracts and immutable ledgers has a big impact on supply chain management, healthcare, and finance. It produces tamper-proof, clear data records.

10. Biometric Data

Biometric authentication systems, which are increasingly used, generate biometric data such as fingerprints, facial recognition and voiceprints. This data crucial for security applications and user identification requires thorough analysis.

11. Augmented Reality (AR) Data

AR data involves information captured from augmented reality experiences. This could include user interactions in AR environments, contributing to personalized marketing strategies and user engagement analysis.

12. Spatial-Temporal Data Fusion

Combining spatial and temporal dimensions in data analysis is becoming more prevalent. This fusion is especially relevant in applications like smart cities, where understanding both location and time is vital.

13. Neurological Data

The progression of neuroscience generates neurological data, such as brain activity patterns. Analysis of this information aids in the understanding of cognitive processes, mental health, and development in brain-computer interfaces.

14. Robotic Sensor Data

As robotics and automation proliferate, data from sensors on robots provide insights into their movements, interactions, and operational efficiency. This is crucial for optimizing robotic systems in various industries.

15. Quantum Data

With the development of quantum computing, quantum data types are emerging. These types of data leverage the principles of quantum mechanics to represent and process information, potentially revolutionizing data processing capabilities.

Professionals dealing with a variety of datasets, including data scientists and analysts, need to stay up to date on these new developments in data types. In addition to creating opportunities for innovation, this approach improves decision-making skills and promotes a deeper comprehension of intricate data environments.

Conclusion

In this article, we discussed how the data we produce can turn the tables upside down, how the various categories of data are arranged according to their need. We also looked at how ordinal data types can overlap with the discrete data types.

What type of plot is suitable for which category of data was also discussed along with various types of test that can be applied on specific data type and other tests that uses all types of data examples. 

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Why is data science important?

The significance of data science lies in the fact that it brings together domain expertise in programming, mathematics, and statistics to generate new insights and make sense of large amounts of data. For companies, data science is a significant resource for making data-driven decisions since it describes the collecting, saving, sorting, and evaluating data. Highly experienced computer experts frequently employ it. When we ask ourselves why data science is essential, the answer rests because the value of data continues to increase. Data science is in great demand because it demonstrates how digital data alters organizations and enables them to make more informed and essential choices.

What is the scope of data science?

Data science can be found just about anywhere these days. That includes online transactions like Amazon purchases, social media feeds like Facebook/Instagram, Netflix recommendations, and even the finger and facial recognition capabilities given by smartphones. Data Science covers numerous cutting-edge technological ideas, such as Artificial Intelligence, the Internet of Things (IoT), and Deep Learning, to mention a few. Data science's effect has grown dramatically due to its advancements and technical advancements, expanding its scope. By learning Data science, you can choose your job profile from many options, and most of these jobs are well paying. A few of these job profiles are Data Analyst, Data Scientist, Data Engineer, Machine Learning Scientist and Engineer, Business Intelligence Developer, Data Architect, Statistician, etc.

How is nominal data different from ordinal data?

Nominal data includes names or characteristics that contain two or more categories, and the categories have no inherent ordering. In other words, these types of data don't have any natural ranking or order. An ordinal data type is similar to a nominal one, but the distinction between the two is an obvious ordering in the data. Overall, ordinal data have some order, but nominal data do not. All ranking data, such as the Likert scales, the Bristol stool scales, and any other scales rated between 0 and 10, can be expressed using ordinal data.

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