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# Data Collection & Organization(Methods, Tools, Types & Techniques)

October 17, 2022

This post is also available in: हिन्दी (Hindi)

In today’s world knowledge is power, information is knowledge, and data is information in raw form. But before you can use that data for any purpose, you need to gather and organize it. This initial stage in data handling and statistics is called data collection and data organization.

Let’s understand these two terms and their significance.

## What is Data?

Statistics is a branch of mathematics. It involves gathering information, summarizing it, and deciding what it means. They can help to predict such things as the weather and how sports teams will perform. They also can describe specific things about large groups of people—for example, the reading level of students, the opinions of voters, or the average weight of a city’s residents.

Data is the word used to describe information. This could be facts, observations, numbers, graphs, or measurements – any kind of information that has been collected and can be analyzed.

Data can be classified into two types.

• Primary Data
• Secondary Data

### What is Primary Data?

Primary data is the data that is collected for the first time through personal experiences or evidence, particularly for research. It is also described as raw data or first-hand information. The mode of assembling the information is costly, as the analysis is done by an agency or an external organization, and needs human resources and investment. The investigator supervises and controls the data collection process directly.

The data is mostly collected through observations, physical testing, mailed questionnaires, surveys, personal interviews, telephonic interviews, case studies, focus groups, etc.

### What is Secondary Data?

Secondary data is second-hand data that is already collected and recorded by some researchers for their purpose, and not for the current research problem. It is accessible in the form of data collected from different sources such as government publications, censuses, internal records of the organization, books, journal articles, websites and reports, etc.

This method of gathering data is affordable, readily available, and saves cost and time. However, the one disadvantage is that the information assembled is for some other purpose and may not meet the present research purpose or may not be accurate.

### Difference Between Primary and Secondary Data

These are the differences between primary and secondary data.

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Country
• Afghanistan 93
• Albania 355
• Algeria 213
• American Samoa 1-684
• Andorra 376
• Angola 244
• Anguilla 1-264
• Antarctica 672
• Antigua & Barbuda 1-268
• Argentina 54
• Armenia 374
• Aruba 297
• Australia 61
• Austria 43
• Azerbaijan 994
• Bahamas 1-242
• Bahrain 973
• Belarus 375
• Belgium 32
• Belize 501
• Benin 229
• Bermuda 1-441
• Bhutan 975
• Bolivia 591
• Bosnia 387
• Botswana 267
• Bouvet Island 47
• Brazil 55
• British Indian Ocean Territory 246
• British Virgin Islands 1-284
• Brunei 673
• Bulgaria 359
• Burkina Faso 226
• Burundi 257
• Cambodia 855
• Cameroon 237
• Cape Verde 238
• Caribbean Netherlands 599
• Cayman Islands 1-345
• Central African Republic 236
• Chile 56
• China 86
• Christmas Island 61
• Cocos (Keeling) Islands 61
• Colombia 57
• Comoros 269
• Congo - Brazzaville 242
• Congo - Kinshasa 243
• Cook Islands 682
• Costa Rica 506
• Croatia 385
• Cuba 53
• Cyprus 357
• Czech Republic 420
• Denmark 45
• Djibouti 253
• Dominica 1-767
• Egypt 20
• Equatorial Guinea 240
• Eritrea 291
• Estonia 372
• Ethiopia 251
• Falkland Islands 500
• Faroe Islands 298
• Fiji 679
• Finland 358
• France 33
• French Guiana 594
• French Polynesia 689
• French Southern Territories 262
• Gabon 241
• Gambia 220
• Georgia 995
• Germany 49
• Ghana 233
• Gibraltar 350
• Greece 30
• Greenland 299
• Guam 1-671
• Guatemala 502
• Guernsey 44
• Guinea 224
• Guinea-Bissau 245
• Guyana 592
• Haiti 509
• Heard & McDonald Islands 672
• Honduras 504
• Hong Kong 852
• Hungary 36
• Iceland 354
• India 91
• Indonesia 62
• Iran 98
• Iraq 964
• Ireland 353
• Isle of Man 44
• Israel 972
• Italy 39
• Jamaica 1-876
• Japan 81
• Jersey 44
• Jordan 962
• Kazakhstan 7
• Kenya 254
• Kiribati 686
• Kuwait 965
• Kyrgyzstan 996
• Laos 856
• Latvia 371
• Lebanon 961
• Lesotho 266
• Liberia 231
• Libya 218
• Liechtenstein 423
• Lithuania 370
• Luxembourg 352
• Macau 853
• Macedonia 389
• Malawi 265
• Malaysia 60
• Maldives 960
• Mali 223
• Malta 356
• Marshall Islands 692
• Martinique 596
• Mauritania 222
• Mauritius 230
• Mayotte 262
• Mexico 52
• Micronesia 691
• Moldova 373
• Monaco 377
• Mongolia 976
• Montenegro 382
• Montserrat 1-664
• Morocco 212
• Mozambique 258
• Myanmar 95
• Namibia 264
• Nauru 674
• Nepal 977
• Netherlands 31
• New Caledonia 687
• New Zealand 64
• Nicaragua 505
• Niger 227
• Nigeria 234
• Niue 683
• Norfolk Island 672
• North Korea 850
• Northern Mariana Islands 1-670
• Norway 47
• Oman 968
• Pakistan 92
• Palau 680
• Palestine 970
• Panama 507
• Papua New Guinea 675
• Paraguay 595
• Peru 51
• Philippines 63
• Pitcairn Islands 870
• Poland 48
• Portugal 351
• Puerto Rico 1
• Qatar 974
• Romania 40
• Russia 7
• Rwanda 250
• Samoa 685
• San Marino 378
• Saudi Arabia 966
• Senegal 221
• Serbia 381 p
• Seychelles 248
• Sierra Leone 232
• Singapore 65
• Slovakia 421
• Slovenia 386
• Solomon Islands 677
• Somalia 252
• South Africa 27
• South Georgia & South Sandwich Islands 500
• South Korea 82
• South Sudan 211
• Spain 34
• Sri Lanka 94
• Sudan 249
• Suriname 597
• Svalbard & Jan Mayen 47
• Swaziland 268
• Sweden 46
• Switzerland 41
• Syria 963
• Sao Tome and Principe 239
• Taiwan 886
• Tajikistan 992
• Tanzania 255
• Thailand 66
• Timor-Leste 670
• Togo 228
• Tokelau 690
• Tonga 676
• Tunisia 216
• Turkey 90
• Turkmenistan 993
• Turks & Caicos Islands 1-649
• Tuvalu 688
• U.S. Outlying Islands
• U.S. Virgin Islands 1-340
• UK 44
• US 1
• Uganda 256
• Ukraine 380
• United Arab Emirates 971
• Uruguay 598
• Uzbekistan 998
• Vanuatu 678
• Vatican City 39-06
• Venezuela 58
• Vietnam 84
• Wallis & Futuna 681
• Western Sahara 212
• Yemen 967
• Zambia 260
• Zimbabwe 263
• Less Than 6 Years
• 6 To 10 Years
• 11 To 16 Years
• Greater Than 16 Years

## What is Data Handling?

Data handling is the method of performing statistical analysis on the given data. It is the process that comprises data collection, data organization, data analysis, and finally its depiction with the help of graphs or charts.

The numbers representing the speed of the wind, its direction, temperature, and humidity are the data collected by the meteorological department. But how does this data help you? It helps in predicting the weather of a place. The data that the temperature is $40^{\circ}$ becomes information when it leads to a realization that the weather is very hot.

Information is the interpretation and understanding of data. What you handle in your day-to-day life is called raw data, this kind of data by itself does not have any meaning. It’s only after it’s organized and structured properly that it is of any use or meaning to us.

The two initial stages in data handling are

• Data Collection
• Data Organization

## Data Collection

In Data Handling or Statistics, data collection is a process of gathering information from all the relevant sources to find a solution to a problem. It helps to evaluate the outcome of the problem. The data collection methods allow a person to conclude an answer to the relevant question. The next step after the data is collected, is data organization.

Depending on the type of data, the data collection method is divided into two categories namely,

• Primary Data Collection methods
• Secondary Data Collection methods

### Primary Data Collection Methods

Primary data or raw data is a type of information that is obtained directly from a first-hand source through experiments, surveys, or observations. There are several methods to collect this type of data.

Observation Method: The observation method is used when the study relates to behavioural science. This method is planned systematically. It is subject to many controls and checks. The different types of observations are:

• Structured and unstructured observation
• Controlled and uncontrolled observation
• Participant, non-participant, and disguised observation

Interview Method: The method of collecting data in terms of verbal responses. It is achieved in two ways, such as

• Personal Interview: In this method, a person known as an interviewer is required to ask questions face-to-face to the other person. The personal interview can be structured or unstructured, direct investigation, focused conversation, etc.
• Telephonic Interview: In this method, an interviewer obtains information by contacting people on the telephone to ask questions or views, verbally.

Questionnaire Method: In this method, the set of questions is mailed to the respondent. They should read, reply and subsequently return the questionnaire. The questions are printed in the definite order on the form. A good survey should have the following features:

• Short and simple
• Should follow a logical sequence
• Avoid technical terms
• Should have a good physical appearance, and quality of the paper to attract the attention of the respondent

Schedules: This method is similar to the questionnaire method with a slight difference. The enumerations are specially appointed for the purpose of filling the schedules. It explains the aims and objectives of the investigation and may remove misunderstandings if any have come up. Enumerators should be trained to perform their job with hard work and patience.

### Secondary Data Collection Methods

Secondary data is data collected by someone other than the actual user. It means that the information is already available, and someone analyses it. The secondary data includes magazines, newspapers, books, journals, etc. It may be either published data or unpublished data.

Published data are available in various resources including

• Government publications
• Public records
• Historical and statistical documents

Unpublished data are available in various resources including

• Diaries
• Letters
• Unpublished biographies, etc.

## Data Organization

Data organization is the way to arrange the raw data in an understandable order. Organizing data include classification, frequency distribution table, picture representation, graphical representation, etc.

Data organization helps us to arrange the data in order that we can easily read and work. It is difficult to work or do any analyses on raw data. Hence, we need to organize the data to represent them in a proper way.

For example, if we want to find the median of a data set, the first step is to arrange the data in ascending or descending order.

### Why Data Organization is Important?

There are a lot of benefits to organizing data. Some of these include

• Reduces Time for Processing: Disorganized data has many bottlenecks in terms of data structuring. Suppose you have data on the results of 1000 students in a school, and you need to find out how many students scored a percentage greater than 90. If your data is unorganized, it will take a lot of time and resources to gather the required information, but suppose you have organized the data in descending order of percentages, and then it will be very quick and easy to sort out the required information.
• Reduces Errors in the Decision-Making Process: Organizing data also helps in reducing data loss and reduces errors. Suppose you have confusion in different sets of data, then the only solution to such problems is to organize the data properly.

### Types of Data Organization

Data organization can be of various types, depending on the requirement of the user. Sometimes, the repeated values in the data are collected together to know the mode of the data or sometimes the data is organized in increasing or decreasing order, to find the median of the given set of data.

The different types of data, based on which they are organized are given below:

• Chronological Data: Chronological data are grouped or classified according to the time, such as days, weeks, months, and years. For example, the growth of population with time in years.
• Spatial Data: Spatial data are classified based on geographical locations or areas such as cities, states, countries, etc.
• Qualitative Data: Qualitative data are categorized under different attributes like nationality, gender, religion, marital status, etc. Such data cannot be measured but can be classified based on their presence and absence of qualitative characteristics. For example, categorizing the population of males and females in a city.
• Quantitative Data: Quantitive data is the type of data when the above attributes (in the case of qualitative classification) are further categorized into number-based data such as height, age, marks of students, salary, etc.

### Ways of Organisation of Data in Statistics

The tools and the ways help us to organize the data efficiently. There are two ways to organize data

• Frequency Distribution Table: A frequency distribution table is a comprehensive way of representing the organization of raw data of a quantitative variable. This table shows how various values of a variable are distributed and their corresponding frequencies. There are two types of frequency tables.
• Discrete Frequency Distribution: In a discrete frequency distribution, the values of the variable are determined individually. The number of times each value occurs denotes the frequencies of the particular value or observation. Discrete frequency distribution is also known as ungrouped frequency distribution.
• Continuous Frequency Distribution: A continuous frequency distribution is a series in which the data are classified into different class intervals without gaps and their respective frequencies are assigned as per the class intervals and class width.
• Graphical Method: Graphical Representation is a way of analyzing numerical data. It exhibits the relation between data, ideas, information, and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:
• Line Graph: A line graph or linear graph is used to display continuous data and it is useful for predicting future events over time.
• Bar Graph: Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histogram: The graph that uses bars to represent the frequency of numerical data that are organized into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot: It shows the frequency of data on a given number line. $sx$ is placed above a number line each time when that data occurs again.
• Circle Graph: Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered 100% and the categories occupied are represented with that specific percentage like 15%, 56%, etc.

## Practice Problems

• What is meant by data?
• What are the two types of data?
• What is the frequency distribution table?
• What are the most common types of graphical representation of data?
• What is data handling?

## FAQs

### What is data collection with example?

Collection of data from information services providers and other external data sources; tracking social media, discussion forums, reviews sites, blogs, and other online channels; surveys, questionnaires, and forms, done online, in person, or by phone, email, or regular mail; focus groups and one-on-one interviews, etc.

### What is data organization in statistics?

Data organization refers to the systematic arrangement of collected figures (raw data) so that the data becomes easy to understand and more convenient for further statistical treatment.

### What are the two methods of data organization?

The two methods of data organization are
Frequency Distribution Table: A frequency distribution table is a comprehensive way of representing the organization of raw data of a quantitative variable. This table shows how various values of a variable are distributed and their corresponding frequencies.
Graphical Method: Graphical Representation is a way of analyzing numerical data. It exhibits the relation between data, ideas, information, and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain.

## Conclusion

Data handling is the method of performing statistical analysis on the given data. It is a process that comprises two main activities – data collection and data organization. There are two methods of data collection – primary data collection and secondary data collection. The data organization helps us in two important ways – reducing the time of accessing information and reducing the error in the decision-making process.