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What Are The Two Main Types of Data?

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The two main types of data include qualitative and quantitative data.

Collecting good data is the foundation for gathering evidence and making sense of it.

Decide what data you need when you design your project, then you can gather the right information from the start, and throughout the project.

There are two general types of data – quantitative and qualitative and both are equally important. You use both types to demonstrate effectiveness, importance, or value.

These two data classes in statistics can subdivide into four statistical data types.

These types of classification are important to machine learning, artificial intelligence, and market research because they help users choose the correct data for the analysis method.

If you’re using Minitab Statistical Software, you can access the assistant to guide you through your analysis step-by-step, and help identify the type of data you have.

But it’s still important to have at least a basic understanding of the different types of data, and the kinds of questions you can use them to answer.

This blog will provide a basic overview of the types of data you’re likely to encounter. The data knowledge will allow you to perform proper exploratory data analysis.

What Is Data Used For?

The data are the individual pieces of factual information recorded, and used for the analysis process.

The two processes of data analysis are interpretation and presentation. Statistics are the result of data analysis.

Data classification and data handling are important processes as they involve a multitude of tags and labels to define the data, its integrity, and confidentiality.

Data types work great together to help organizations and businesses build successful data-driven decision-making processes.

Working in the data management area and having a good range of data science skills involves a deep understanding of various types of data and when to apply them.

Data Collection

In machine learning, knowing the appropriate data types of independent and dependent variables provides the basis for selecting the right data analysis method.

Data types incorrectly identified can lead to incorrect modeling, which can produce wrong or unhelpful information.

Data collection is an essential part of the research process, and it’s important to start your project with experienced professionals.

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What Is Qualitative Data?

When you classify or judge something, you create qualitative data.

Qualitative data can’t be expressed as a number and can’t be measured. Qualitative data consists of words, pictures, and symbols, not numbers.

Qualitative data, also called categorical data, is the information that can be sorted by category, not by number.

It delivers information about the qualities of things in data. The outcome of qualitative data analysis can come in the type of featuring keywords, extracting data, and ideas elaboration.

Qualitative data deals with characteristics and descriptors that can’t be easily measured, but can be observed subjectively—such as smells, tastes, textures, attractiveness, and color.

Qualitative data can answer questions such as “how this has happened” or “why this has happened.”

For example:

  • Hair color – black, brown, red

  • Opinion – agree, disagree, neutral

Notice that qualitative data could be much more than just words or text.

Photographs, videos, sound recordings, and so on, can be considered qualitative data.

When researchers collect qualitative data, they seek to add extra details and include a human element to their survey results.

Researchers do not use qualitative data for statistical analysis. Remember that qualitative research primarily addresses the question “Why?”

Quantitative data may tell researchers that 75% of their respondents preferred one product design over another, but the qualitative data helps them understand why that statistic exists.

Sometimes categorical data can hold numerical values (quantitative value), but those values do not have a mathematical sense.

Examples of the categorical data are birthdate, favorite sport, and school postcode.

The birthdate and school postcode hold the quantitative value, but it does not give numerical meaning.

Nominal Data

Nominal data is one type of qualitative information that helps label the variables without providing the numerical value. Nominal data is also called the nominal scale.

Nominal Data are not measured but observed, and they are unordered, non-equidistant, and have no meaningful zero.

Nominal data has no numerical value; instead, it names a variable without applying for any particular order.

But sometimes, the data can be qualitative and quantitative. Examples of nominal data are letters, symbols, words, gender, etc.

You can’t organize nominal data, so you can’t sort them. Neither would you be able to do any numerical tasks as they are for numerical data.

You can calculate frequencies, proportions, percentages, and central points with nominal data.

The nominal data are examined using the grouping method. In this method, the data are grouped into categories, and then the frequency or the percentage of the data can be calculated.

Pie charts represent this data.

Ordinal Data

Ordinal data/variable is a type of data that follows a natural order. The significant feature of the nominal data is that the difference between the data values is not determined.

This variable is in surveys, finance, economics, questionnaires, etc.

You can calculate the same things as nominal data with ordinal data like frequencies, proportions, percentages, and central points. 

Still, one more point adds in ordinal data: summary statistics and, similarly, bayesian statistics.

As ordinal data are ordered, marketers can arrange them by making basic comparisons between the categories, such as greater or less than, higher or lower, etc.

Additional examples of ordinal data include a person’s education level, the letter grading system, and customer satisfaction survey scales of 1 to 10.

A survey scale of 1 to 10 shows that ordinal data can have numerical values.

However, the difference is that you can’t do any numerical activities with the values because they only show sequences. ‍

However, you can’t do any numerical activities with ordinal data, as they are numerical data.

What Is Quantitative Data?

Quantitative data deals with numbers and things you can measure objectively: dimensions such as height, width, and length.

Quantitative data is a bunch of information gathered from a group of individuals and includes statistical data analysis.

Quantitative research is a scientific method of collecting numerical data to measure variables in the form of numbers or statistics.

For example, a survey might conclude that 356 respondents out of 500 (71.2%) were in favor of a new product feature.

Numerical data is another name for quantitative data. Simply, it gives information about the quantities of items in the data and the items that can be estimated.

There are two general types of quantitative data: discrete data and continuous data.

Discrete Data

Discrete data can take only discrete values. Discrete information contains only a finite number of possible values. Those values cannot be subdivided meaningfully.

Here, you can count things in whole numbers.

An example of discrete data would be the number of children a person has. You can measure whole numbers, but a person wouldn’t have 2.6 children.

Researchers use pie charts, bar charts, or tally charts to graph discrete data.

Continuous Data

Continuous data is quantitative data that fluctuates or divides into infinitely smaller parts. An example of continuous data would be taking measurements.

It can be measured on a scale or continuum and have almost any numeric value.

An object measured in centimeters isn’t constrained to a whole number – the measurement could be divided into as many decimals as needed.

An excellent rule for defining if a data is continuous or discrete is that if the point of measurement can be reduced in half and still make sense, the data is continuous.

Examples of continuous data:

  • The amount of time required to complete a project

  • The height of children

  • The square footage of a two-bedroom house

  • The speed of cars

This is where the key difference between discrete types of data lies. The continuous variables can take any value between two numbers.

For example, between 50 and 72 inches, there are millions of possible heights: 52.04762 inches, 69.948376 inches, etc.

Interval Data

Interval data refers to information measured along a scale with equal distances. The distances or spaces in between the adjacent values are called intervals.

So, the interval scale represents information about the order, and it gives meaning to the difference between two values.

Even though interval data can show up fundamentally the same as ratio data, the thing that matters is their characterized zero points.

If the zero-point of the scale has been picked subjectively, the data can’t be ratio data and should be interval data.

Hence, with interval data, you can easily correlate the degrees of the data and add or subtract the values.

There are some descriptive statistics that you can calculate for interval data central point (mean, median, mode), range (minimum, maximum), and spread (percentiles, interquartile range, and standard deviation).

The central point of an interval scale is that the word ‘Interval’ signifies ‘space in between,’ which is a significant thing to recall. 

Interval scales not only educate us about the order but additionally about the value between every item.

Interval data can be negative, though ratio data can’t.

Ratio Data

Ratio data is fundamentally the same as interval data, aside from zero means none.

Unlike interval data, ratio data has an absolute zero. It means ratio variables can’t have negative values, and zero means none of that variable is present.

For instance, height measurement is considered ratio data, and it’s not applicable to have a negative number for height.

You also get a meaningful interpretation of the ratio of two values with ratio data.

The descriptive statistics which you can calculate for ratio data are the same as interval data which are central point (mean, median, mode), range (minimum, maximum), and spread (percentiles, interquartile range, and standard deviation).

Example of ratio data:

  • Age (from 0 years to 100+)

  • Temperature (in Kelvin, but not °C or F)

  • Distance (measured with a ruler or any other assessing device)

  • Time interval (measured with a stop-watch or similar)

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What Is Numerical Data?

Numerical data is another name for quantitative data. Simply, it gives information about quantities of items in the data and the items that can be estimated.

And we can formulate them in terms of numbers. Numerical data gives information about the quantities of a specific thing.

To put it simply, if you can assign a numerical value to an aspect of your study, it’s quantitative data.

Some examples of numerical data are height, length, size, weight, etc.

How To Combine Quantitative And Qualitative Data On A Survey

The best way to combine qualitative and quantitative data on surveys is to include multiple-choice and open-ended questions.

Multiple-choice questions generate fixed, structured results while limiting a respondent’s choices to many options.

Which Data Should You Use?

An average business spends between 25% and 50% of its annual marketing budget on research-related activities.

Quantitative and qualitative data both provide valuable insights, and they do not conflict with each other. Using both types of data provides a complete picture.

In most cases, qualitative research is an ideal starting and ending point. The qualitative proponents counter that their data is ‘sensitive,’ ‘nuanced,’ ‘detailed,’ and ‘contextual.’

In social research, this kind of polarized debate has become less than productive for many of us.

And it obscures the fact that qualitative and quantitative data are intimately related to each other.

All quantitative data is based upon qualitative judgments, and you can describe and manipulate it numerically.

We hope you understood about four types of data in statistics and their importance. 

Now you can learn how to handle data correctly, which statistical hypothesis tests you can use, and what you could calculate with them.

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