Your first Step in Data Analysis

Just as a team analyzes their opponent before the game, or an Army General examines the field before the battle, you should know what you are facing before undertaking your Data Analysis.

The nature of Data can be very diverse.

  • Some data are Subjective, others are Objective, some data can be  measured perfectly while others are very difficult, etc

 

And you can’t use any data in any way.

 

Behind a number there is always much more than it seems at first glance.

If you are going to develop a Data Analysis, the first thing you should do is find out which type of data you’ve got.

Types of Data

There are 2 Types of Data:

  • Quantitative Data:
    • The data related to objective measures.
      • Quantitative data can be: Continuous or Discrete.

 

  • Qualitative Data:
    • The data related to subjective descriptions or states.
      • Qualitative data can be: Binary, Nominal or Ordinal data.

 

Let’s see them in more detail:

Quantitative Data

Quantitative  Data is that you can measure and compare Objectively.

    • Length.
    • Weight.
    • Sound (decibels).
    • Number of people
    • etc.

 

Depending on whether this “measurable data” can be divisible or not, we have:

  • Continuous Data.
  • Discrete Data.

Continuous Data

Continuous Data comprises all Quantitative divisible Data.

  • This Data can be infinitely divided.

 

For example:

You can divide a length infinitely (maybe Planck would not agree) :

  • 1 meter = 100 cm = 1,000 mm = 1,000,000 micrometers…

You can also divide a weight infinitely.

  • 1 kilo = 1,000 grams = 1,000,000,000 nanograms…

Discrete Data

This Quantitative Data is that you can count, measure objectively and compare, but it is not infivitely divisible.

 

For example:

You can’t infinitely divide the number of people in a room.

  • It is impossible to have 1.534272 people.

You can’t infinitely divide objects.

  • Either you have 1 computer or you have 2, but you can’t have 1.5 computers.

Averages make people get confused

 

Many people get confused with Discrete Data because of Averages.

We all have heard that, on average, married couples have 1.3 children (for example).

 

Then, can we divide people or not? 

  • Wasn’t people a Discrete Variable?

 

You can make averages of all the Data you want.

  • No matter if it is Discrete, Continuous Data, Quantitative or Qualitative.

 

That doesn’t change the nature of the analyzed Data .

Let’s see some examples:

Continuous and Discrete Data examples

Continuous Data examples

 

Weight.

  • You can infinitely divide weight (kg).

Length.

  • You can infinitely divide length (m).

Velocity.

  • You can have infinitely small increases in velocity (m/s, km/h, mph).

Sound.

  • You can infinitely divide Sound volume (db).

Temperature.

  • There are infinite degrees of Temperature (ºC or K).

Other Physical Quantities.

  • Voltage, Intensity, Pressure, Angle, Acceleration, etc, etc.

 

Discrete Data examples

 

People.

  • You can’t infinitely divide the number of people.

Objects: Cars, Books, Pencils, etc.

  • You can’t infinitely divide objects; there is always a minimum quantity of 1.

Money:

If you think carefully about it, you will realize that it is impossible to have half a penny.

  • Money (euros, dollars, pounds…) is a Quantitative Discrete variable.

Let’s now look at the other main category of Data: Qualitative Data.

Qualitative Data

Unlike quantitative data, Qualitative data is the data that serve to classify or categorize something Subjective, or describe the state of something.

  • Instead of measuring, its goal is to describe something.

 

You may not agree on how good a dish is (Qualitative data) but not on how much it weights (Quantitative data).

Everything can be quantified: here is the confusion

 

Everything can be quantified in some way.

  • And, Qualitative Data is usually quantified for commercial or Marketing purposes.

 

For example:

  • You can describe a person with words.
  • Or you can develop a 100-question test that measures from 1 to 100 every aspect of his personality.

 

This makes people think that all Data is Quantitative Data.

  • But it is not.

 

Why?

Because when you quantify certain Qualitative Data, you are quantifying Subjective Data.

  • What a person thinks about an object or other person, etc

 

Quantifying some data doesn’t make that data Quantitative Data.

  • In any case, it would be Quantified data.

 

For example:

If we evaluate how handsome someone is, what is a 9 out of 10 for you, can be a 1 out of 10 for me.

  • Nobody is right.
  • It is purely subjective.

There are 3 types of Qualitative Data:

  • Binary Data.
  • Nominal Data.
  • Ordinal Data.

Binary Data

Variables (Data) that can have only 2 possible values.

 

For example:

  • In/ Out.
  • Yes/ No.
  • True/ False.
  • etc.

 

* No need to mention that, if some data had 3 possible states, this data would belong to this category.

Nominal Data

Descriptive data: Everything described with Adjectives and Adverbs.

 

For example:

  • Colors.
  • Sensations.
  • Feelings.
  • etc.

Ordinal Data

Ordinal data refers to a Subjective Classification or a certain Order.

 

This type of data is commonly misunderstood because it is usually associated with Quantitative Data.

 

For example:

If you tell a group of people to rank from 1 to 5 the height of some people, they will Subjectively judge a Quantitative Data.

  • If someone is 1.80 m… Is he tall or short?
    • If the observer is 1.50 m, he would be considered tall.
    • On the other hand, if the observer is 2 m, he would be considered short.
  • Depends completely on the Observer.
  • However, nobody would dare to question that he is 180 cm tall.
    • That is objective.

 

However, in other situations, there is no possible confusion.

 

For example:

A race classification is no debatable.

    • If you are 2nd, you are 2nd for everybody.

 

Let’s see some examples of Qualitative data:

Binary, Nominal and Ordinal Data examples

Binary Data examples

 

  • Yes/ No.
  • True/ False.
  • On/ Off.
  • In/ Out.
  • 1 / 0.
  • Child/ Adult.
  • National/ Foreigner.

 

Nominal Data examples

 

Colors.

    • Blue, Yellow, Orange, etc…

Sensations.

    • Scary, Funny, Boring, Exciting, etc.

Flavors.

    • Acid, Sweet, Sour, etc.

 

Ordinal Data examples

 

Similarity.

    • Evaluate from 1 to 4 how similar X is to Y.

Likability.

    • From 1 to 10, what you like most?

Subjective size.

    • From 1 to 10, is he tall or short?

Subjective weight.

    • Evaluate how heavy this object is.

Ranking:

  • What position did he end up in?

Now, you may be asking yourself: “but why is this important?”.

Use Useful and Quantifiable Data

If you know which type of data you have, you can develop a much better analysis.

 

You will give data the importance it deserves.

  • If you are designing a new comfortable chair, you’ll use :
    • Objective data for calculating average weights.
    • Subjective data for assessing whether people liked it or not.

 

And more important: You will be able to Quantify Data properly and make it useful for your analysis.

How?

As we mentioned earlier, everything can be Quantified (even colors can be classified according to their wavelength).

  • But remember, that doesn’t make all variables to be Quantitative data.

 

When developing any Data Analysis, you have to evaluate the variables you’ll handle and Quantify them in the most useful way for you.

 

The best Quantification possible, always depends on the variable.

 

However, we’ll give you 3 Tips for handling your Data properly:

 

1. Handle continuous variables whenever you can.

 

2. Group non-quantifiable variables and study them independently.

  • For example: Don’t mix Children and Adults; study them separately.

 

3. Don’t mix Subjective and Objective variables.

  • It can cause confusion and misunderstandings.

Let’s see a Real example:

Dates - Example of Quantified Data

Imagine that you are studying how some variable evolves during a whole year.

 

You could use dates for developing this study.

…. Or you could number the days from 1 to 365.

  • This is a much better option.

 

Think about how tricky a date-base study would be:

  • It would be a mess in Excel.
  • If you wanted to study sub-periods of time;weeks, 5-days… It would be a chaos.
  • Each month is different in number of days.
  • It is not very intuitive.

 

For example: What feels more intuitive?

  • From 01/01/2020 to 03/03/2020 the sales increased a 20%.
  • Sales increased a 20% the first 62 days of the year.
    • And immediately, you can calculate: 62/365 = 17% of the year.

 

If you handled days instead of dates, you would develop a much more efficient analysis.

By picking the right change of variable you’ll be able to develop much better analysis.

 

And to do so, you must know what kind of data you’ve got in your hands.

Summarizing

You should always know which type of data you’ve got in your hands before developing any Data Analysis.

 

There are 2 Types of Data:

  • Quantitative Data: data related to objective measures.
    • This data can be:
      • Continuous: data that can be infinitely divided.
      • Discrete: data that can’t be infinitely divided.

 

  • Qualitative Data: data related to subjective descriptions or states.
    • This data can be:
      • Binary: data that can only have 2 values.
      • Nominal: descriptive non-numerical data.
      • Ordinal data: subjective classification or a certain order.

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