Share for Money: Types of Data in Statistics

===How to READ this Article: For Bloggers or anyone wishing to learn how to make money online through sharing tips    about statistics: START FROM INTRODUCTION

===For Researcher wanting to read about statistics (Types of data in Statistics, scroll down to point V; it’s a well-documented articles though it serves as a teaching material for blogger statistics-skilful folkes!

Introduction

Would like to learn how work from home and earn money sharing statistics related tips? You’re at the right hub. This article “Share for Money: Types of Data in Statistics” contains tips on how to make easy money online teaching statistics online. By teaching, I just mean mere sharing what you know online! I’m going to share a sample post about the types of data in statistics, demonstrating to you how you will be writing and sharing those tips!

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Tips to Make Money Sharing Statistics Tips

Contents

I. Introduction

II. Make money online/on internet: A scam? 

III. Make Money by Sharing Information: What to Share Online? 

IV. How to make easy money teaching statistics online

V. Types of Data in Statistics

5.1. Daily Life Examples

5.2. Some constraints for in Social Science Data

5.3. Classification of data 

     5.3.1. Nominal Data

5.3.2. Ordinal Data

5.3.3. Interval Data

5.3.4. Ration Data

5.3.5. Data types: More on the four data types.

Make money online/on internet: A scam?

Making money online is not a scammy thing though many people label it so. But the world of the so-called easy scheme to make money online is full of scams and pitiless predators ready to tear their dupes apart (I have been scammed too, so I know what it looks like).

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In few words, YOU can work from home and earn money online/internet in 21st century by sharing useful information to those who need it!

Make Money by Sharing Information: What to Share Online?

This is like asking me “Tell me something I don’t know”

===Read on, please!

Here is how it goes…. You create useful/helpful/valuable information for a particular section of people (audience). You simply share what you know and people who want to resource themselves on that come and READ/WATCH what you’ve shared!

As you can understand, it’s through getting people to COME and SEE WHAT you’ve created that you can earn easy and decent money online! So, you can create text, audio, video or image information and attract people (who need to know what you’ve shared)!

If you’re serious enough, have a look by Click on this article: you will discover:

  • The different simple settings required for you to start;
  • Learn how to monetize your information: monetizing what you share online!

How to make easy money teaching statistics online

As I have said, it’s not teaching “the classical and regular way of teaching.” It’s just sharing but having in mind that you’re guiding people who know less in than you do in what you’re sharing.

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If you want to share about statistics: you can just dedicate yourself to sharing exercises and their solutions, sharing tutorials or video on some data analysis computer programmes/software, testing hypothesis, etc. So, you to look like a person who knows what he/she’s doing make sure:

  • Your information is organized;
  • Not vague, be specific…talk about one thing and its directly related aspects;
  • Be a real human being talking to another human being (this will set a sort of closeness between you and your reader);
  • Be a problem solving-oriented when sharing information (find a problem and then offer documented solution to it)

===Think of a person having the problem that you’re trying to bring solution to

  • Use simple/plain/non sophisticated language

By observing these recommendations, you stand a great chance to earn easy money/ free money online, by doing what you like most!

Information sharing is now a big industry that is generating billions of dollars daily! Share what you know online and monetize it!

Example of a post about statistics/data analysis/scientific research

Types of Data in Statistics

Dear data analyst/student/teacher/researcher,

Did you know that solving a problem in a research using scientific method is “to perform systematic measures?”

For your information, the scientific method is a pursuit of relative truth which is governed by logical considerations. Because the existence of science is to gain a systematic interrelation of facts, then the scientific method is seeking answers to the problem (research problem).

Nazir (2006) clarifies that the important step before the stage of data analysis and determination of the model is when we do data collection and manipulation so it can be used for hypothesis testing purposes.

It should also be noted that data manipulation means transforming raw data from scratch into a form that can easily demonstrate relationships between phenomena.

The crucial aspect then of behaviour/phenomena/features/characteristics quantification cannot be done well if one does not know the type of data! You should know whether your data are nominal, ordinal, interval or ratio to do the right data analysis!

Are able to differentiate them?

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Daily Life Examples

Scales cannot be used to measure a person’s height. Conversely a meter/yard is not the right device/instrument to measure a person’s weight. Because each instrument has its own uses, doesn’t it?

Some constraints for in Social Science Data

For the purpose of research analysis of social sciences, the technique of sorting something into a scale means that it is important to remember that some data in the social sciences has a qualitative nature.

People nowadays push it that far…. They no longer easily adhere to attributes of “good” or “bad”, “agree” or “disagree”, but they also want something that is between good and bad or between agree and disagree. Actually, this sort of graduation is the one that is required in order uncover in detail the research object.

Nazir (2006) stipulated that scale the making technique is a way of altering qualitative facts (attributes) into a quantitative sequence (variable). Which means there is a series of skills needed to convert qualitative facts into quantitative sequences. There is also a scientific reason to do that:

  • firstly, mathematics have serve as a tool, which if well-handled in one’s research, justifies that such a research is really scientific (which qualitative researchers do not agree with 100%);
  • secondly, besides the accuracy of data, there is need for precision, which explains why people are not necessarily satisfied with the attributes of good or bad (but would rather see to which extent “bad” or “good”. Some researchers want to measure the qualities that exist between the “good” and “bad” categories, henceforth the need of scaling.

Classification of data

Data can be classified in different ways, but for the sake of brevity, let’s talk about nominal, ordinal, interval and ration data.

A. Nominal Data

The nominal data is the simplest size, where the number given to the object has meaning as a label only, and does not indicate any degree. The feature of nominal data is to have only an attribute, or a name, or discrete. Nominal data is discrete and has no order. When an object is grouped into sets, and to all members the set is assigned a number, those sets should not overlap and whistle.

For example, about sports types such as tennis, basketball and swimming. Then each member set above let’s give it a figure, for example Tennis (1), Basketball (2) and swimming (3). It is apparent that the given number does not indicate that the basketball level is higher than that of tennis or the pool level is higher than that of tennis.

In the numbering of the sets above, the number does not mean anything if added. The given number only serves as a label only.

B. Ordinal Data

Another part of data that is often used is the ordinal data. An attribute, for this type of data, in addition to having a name also has a rank or sequence. The numbers assigned are levels. Such numbering or assigning of the numbers is used to sort objects from the lowest to the most high, or vice versa.

This size does not give an absolute value to the object, but only gives it a rating. If we have a set of objects numbered, from 1 to N, for example rank 1, 2, 3, 4, 5 and so on, when expressed in scale, then the distance between the data from one another is not the same. It will have a sequence ranging from the highest to the lowest.

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Classification of Data

A good illustration for this type of data is the Likert Scale: for example, when answers range from very agreed, agreed, hesitant, disagree until strongly disagree (for the research problem that rotated around tendency of society to attend the general meeting of regional head elections:

  • Let’s assign code 5 to “never absent;
  • code 4 to “sometimes just attending;”
  • “less attending” with code 3;
  • “never attending”, with code 2; and then
  • not want to attend at all, with code 1.

The results that will be obtaining following measurement that uses ordinal scale ordinal data.

C. Interval Data

Objects under this type of date have the properties of ordinal data plus one other trait, i.e., “the same distance” (same interval between data). In this type of data, there is the same distance between the characteristics or properties of the object measured. However, the interval scale does not provide the absolute number of measured objects.

For example, the exam scores of 4 students, namely A, B, C, and D are measured through interval scaling. On this sort of achievement scale there is equally distance between 1 and 2, 2 and 3, 3 and 4 so that it can be said that different achievement score between students C and A are 3 – 1 = 2. The different achievement score between students D and B is 4 – 2 = 2.

But it cannot be said that the achievement of student D is 2 times the achievement of student B or student achievement D is 4 times better than A student achievement. Moreover, the interval scale also has no absolute zero value as it would be measuring temperature on a thermometer.

D. Ratio Data

This scale is like the interval data type but has another trait in addition: the absolute zero value/zero point. Therefore, the distance interval is not expressed with the different average number of one group compared to the zero point above.

Because there is zero point, we can have some arithmetic operations like multiplication or division. A number on the ratio data can indicate the actual value of the object measured. If there are 4 drivers, A, B, C and D have each income per day Euro10.00, 30.00, 40.00 and 50.00. It’s understandable then that driver C’s income is 4 times the revenue of driver A. Driver C’s revenue is 4/3 times driver B’s. In other words, the ratio between drivers C and A is 4:1, that between D and A is 5:1, while the ratio between the C and B drivers is 4:3.

Another ratio data example is that of infant weight as measured on a scale/balance. Baby A weighs 3 Kg. Baby B weighs 2 Kg and the baby C weighs 1 Kg. If measured with on ratio scale, baby A has a weight ratio of 3 times the weight of infant C. Baby B has a ratio of weight twice of the weight of infants C and baby C has a weight ratio of A third time the baby’s weight, etc.

Data types: More on the four data types

It is convenient to give further comments on these four types of data:

  • In the nominal data, i.e. characteristics/objects/phenomena are categorized in a discrete and mutually separated from each other, e.g. marital status, gender, ethnic origin, a person’s job profession and so on.
  • Ordinal data feature characteristics that are arranged on the basis of rankings, such as one’s motivation to work, chess race rankings, difficulty level of a job and others;
  • While for interval data objects that are measured may be for example: students’ achievement scores, thermometer scales and so on;
  • whereas ratio data feature objects/characteristics: e.g. income, weight, and so on.
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E. Convert variable from ordinal to another type

Under certain constraints, a researcher can feel like wanting for example to transform his/her data from ordinal to interval. For example we want to do parametric statistical test of Pearson correlation Product Moment, Partial Correlation, Multiple Correlation, Partial Regression and Multiple Regression, whereas the data we have ordinal.

Since requirements or conditions for such parametric stats to be carried out include the fact that the data should be interval or ratio, (+data must have a normal distribution), may feel the need to convert our data from ordinal to interval: we apply what is termed Successive Interval Method (SIM). This is also termed “data transformation.”

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The purpose of data transformation is to increase data from the ordinal measuring scale to scale with interval measurement commonly used for parametric statistical analysis on the top of rendering those data in normal spread/distribution (Allen &Yen, 1979, pp.182-183 )

Wrapping Up

I hope I have explained for those who would like to learn how work from home and earn money sharing statistics related tips. In this article “Share for Money: Types of Data in Statistics,” I have given you hints on what to share online: you share what you know. For example, by sharing tips related to statistics or data, you can make easy money online!

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By sharing a sample website post about the types of data in statistics, I have demonstrated to you that sharing what you know in an organized and problem-solving manner is among the easy ways to make money for yourself sharing tips about the types of data in statistics.

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Oscar

(1) Career Goal: To develop my skills in teaching, training, consulting and couple this experience with research and program/project evaluation, assisted by cutting-edge data analysis programmes/applications to "Bridge the Gap between Research and Entrepreneurship" (2) Why Do I Qualify to Guide You? As Co-founder/Associate at Former ODS Consultancy (A Consulting Board specialized in Market Surveys, Program/Project Evaluation and Research-Data Analysis Training), Now Associate of KANTAS Consulting, and PhD Student, Oscar is in the Position to Guide and Foil for You Woes/Foes in the Scammy World of Online Marketers!

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