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Methods of Data Collection: A Fundamental Tool of Research

Authors:
  • Rajasthan Unani Medical College and Hospital Jaipur India

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For conducting research, it is a must to collect data. Data is basically the information that is required for investigating a research problem after proper designing. The importance of data collection lies in the fact that without gathering the particular information the research could not be carried out. The data may be primary or secondary. Usually, the methods of primary data collection in behavioural sciences include observation methods, interviews, questionnaires, and through database. The sources of secondary data include the previously published books, magazines, journals, etc. and unpublished autobiographies and biographies, etc. Thus, data collection is mandatory to accomplish the research process and therefore, it is the fundamental tool of research. This paper reviews, in detail, the various methods and different ways of gathering the information for undertaking research.
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Review Arcle
Journal of Integrated Community Health (ISSN: 2319-9113)
Copyright (c) 2021: Author(s). Published by Advanced Research Publicaons
Journal of Integrated Community Health
Volume 10, Issue 1 - 2021, Pg. No. 6-10
Peer Reviewed & Open Access Journal
Corresponding Author:
Syeda Ayeman Mazhar, Department of Tahauzi
wa Samaji Tib (PSM), Faculty of Unani Medicine,
Aligarh Muslim University, Aligarh, Uar Pradesh,
India.
E-mail Id:
syedaayeman@gmail.com
Orcid Id:
hps://orcid.org/0000-0003-2589-2116
How to cite this arcle:
Mazhar SA, Anjum R, Anwar AI, Khan AA.
Methods of Data Collecon: A Fundamental Tool
of Research. J Integ Comm Health. 2021;10(1):6-
10.
Date of Submission: 2021-01-16
Date of Acceptance: 2021-03-12
INFO ABSTRACT
For conducng research, it is a must to collect data. Data is basically the
informaon that is required for invesgang a research problem aer
proper designing. The importance of data collecon lies in the fact that
without gathering the parcular informaon the research could not be
carried out. The data may be primary or secondary. Usually, the methods
of primary data collecon in behavioural sciences include observaon
methods, interviews, quesonnaires, and through database. The sources
of secondary data include the previously published books, magazines,
journals, etc. and unpublished autobiographies and biographies, etc.
Thus, data collecon is mandatory to accomplish the research process
and therefore, it is the fundamental tool of research. This paper reviews,
in detail, the various methods and dierent ways of gathering the
informaon for undertaking research.
Keywords:
Research Problem, Behavioural Sciences, Research
Process, Quesonnaire, Interview, Database, Observaon, Research
Design/ Plan
Methods of Data Collection: A Fundamental
Tool of Research
Syeda Ayeman Mazhar1, Rubi Anjum2, Ammar Ibne Anwar3, Abdul Aziz Khan4
1PG Scholar, 2Professor & Chairperson, 3,4Assistant Professors (Stage II), Department of Tahauzi wa Samaji Tib (PSM), Faculty
of Unani Medicine, Aligarh Muslim University, Aligarh, Uar Pradesh, India.
DOI: hps://doi.org/10.24321/2319.9113.202101
Introduction
The responsibility of data collecon starts aer dening the
research problem and outlining the research design/ plan.
There are two major strategies for collecng informaon
about circumstances, parcular problems or for any other
phenomena. Occasionally, the facts required are previously
accessible and need only be extracted. The researcher
would have to decide which type of data they would be
using for their study and accordingly they will have to select
one or the other method of data collecon.1 By denion,
data collecon is dened as the process of gathering,
evaluating and analysing precise understandings for
research using typical authencated methods. Assessment
of the hypothesis by a researcher can be done on the basis
of collected data. In almost all cases, data collecon is the
most signicant step for research, regardless of the eld
of research. The method of data collecon varies as per
the dierent elds of study, depending on the essenal
informaon. The most vital objecve of data collecon is
to safeguard that informaon-rich and unswerving data
that is collected for stascal analysis and further helps
in making data-driven decisions for research.2
Definition of Data
Data is a plural form of datum meaning “a piece of
informaon”. Informaon may be collected during a study,
or as a result of an experiment, or during an observaon,
or through census or survey. The informaon may also be
gathered by invesgators on their own. Data may be of
two types:1,3
Primary Data: Primary data include the data that are
collected for the rst me, and are original and fresh.
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J. Integ. Comm. Health 2021; 10(1)
ISSN: 2319-9113
DOI: hps://doi.org/10.24321/2319.9113.202101
Secondary Data: Secondary data include the data which
have previously been collected by someone else and have
already undergone the stascal process.
Methods of Collection of Primary Data1,3-5
Primary data is collected while undertaking experiments
in experimental research, but, primary data in the case of
descripve type research and surveys (including sample
surveys or census surveys), are obtained either by means of
observaon or via direct communicaon with respondents
in one form or another.
Types of Primary Data Collection:1,3-5
Observaon method
Interview method
Through quesonnaire
Through schedule
Other methods include warranty cards, pantry audits,
distributary audits, consumer panels, using mechanical
devices, through projecve technique, depth interviews
and content analysis
Observation Method1,3-5
This is the most frequently used pracce, parcularly in
studies relang to behavioural sciences.
Every one of us, more or less observe things around us, but
this is not considered scienc observaon. Observaon is
said to be a scienc tool and a means of data collecon
for the researcher, when it serves a formulated research
purpose, is systemacally planned and recorded and is
subjected to checks and controls on validity and reliability.
Under the observaon method, the informaon is sorted
by invesgators’ direct observaon without asking from the
respondent. This method is parcularly suitable in studies
that deal with subjects who aren’t capable of giving verbal
reports of their feelings for any reason.
Observation
Data collecon by means of observaon does not require
personal contact. A good example of observaonal data
gathering is counng the number of automobiles crossing
an intersecon every hour.
Types of Observation Methods
Structured Observaon: It involves a careful denion of
the units to be observed, along with the style of recording
the obtained informaon, selecon of pernent data of
observaon, and the standardised condions of observaon.
Unstructured Observaon: This is performed without
considering any structured characteriscs in advance.
Controlled Observaon: It involves observaon as per the
pre-arranged strategies including experimental processes.
Uncontrolled Observation: In this, the process of
observaon takes place in natural sengs.
Parcipant Observaon: It is the process in which the
observer shares the experiences, being a member of the
group.
Non-parcipant Observaon: It is the process in which
the observer is a detached parcipant.
Disguised Observaon: It refers to the process in which
the observaons are made without people knowing that
they are being observed.
Interview Method1,3-5
For eecve execuon of the interview method, the
interviewers have to be sensibly nominated, skilled, and
updated. They have to be authenc, genuine, diligent,
unbiased and ought to retain the praccal competency and
essenal applied understanding. In actual fact, interviewing
is a skill administered by denite scienc ethics. It involves
the presentaon of oral-verbal smuli and replies in terms
of oral-verbal responses.
Personal Interview
Telephone Interview
Interview
This generally takes place amongst two individuals, one is
called the interviewer and another is the interviewee or
respondent. This is typically preferred if it is convenient to
talk directly to the respondents. For example, if a researcher
desired to conclude whether individuals stayed happy with
the way they were treated by sales sta hospitality.
Personal Interview Method
This method entails a person as an interviewer asking
quesons mostly in a face to face interacon with other
people. At mes, the interviewee might also probe certain
interrogaons and interviewer’s responses, but generally,
the interviewer starts the interview and assembles the
facts. This method is quite appropriate for thorough
invesgaons.
Structured Interview: The informaon collected by this
method is usually processed in a structured way, such
interviews involve the use of a set of predetermined
quesons and highly standardised techniques of recording.
In this method, the interviewer follows a rigid procedure.
Unstructured Interview: It doesn’t follow a system of
predetermined quesons and standardised techniques of
recording informaon.
Focused Interview: It is intended to put emphasis and
consideration on the respondent’s certain attained
capabilies and their eects. In this method, the interviewer
has the freedom to decide the manner and sequence in
which the quesons would be asked and has also the
freedom to explore reasons and moves. The main task of
the interviewer in case of a focussed interview is to conne
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Mazhar SA et al.
J. Integ. Comm. Health 2021; 10(1)
ISSN: 2319-9113
DOI: hps://doi.org/10.24321/2319.9113.202101
the respondent to a discussion of issues with which he
seeks conversance. Such interviews are generally used in
the development of a hypothesis and constute a major
type of unstructured interview.
Telephone Interview Method
In this method, the informaon is collected by contacng
respondents on a telephone. It is not a very widely used
method but plays an important role in industrial surveys.
Collection of Data through Questionnaires1,3-5
This type of data collecon procedure is very popular
in the case of big surveys. This method is adopted by
research workers, private personalies, private and public
organisaons and even by governments. In this method, a
quesonnaire is conducted to the individuals concerned
with an appeal to respond to the quesons and give back
the quesonnaire. A quesonnaire consists of a number of
quesons printed or typed in a denite order on a form or
set of forms. The quesonnaire is mailed to respondents
who are expected to go through the quesons, comprehend
them, and provide their response to the quesons in the
space alloed for that purpose in the quesonnaire itself.
The parcipants are required to provide the answers to
the quesons by themselves.
Questionnaire
A quesonnaire is a predetermined set of quesons given
to a number of respondents. This instrument is good for
geng informaon from many people. Quesonnaires
are also appropriate for geng informaon from people
that are spread over a wide area and that are not easy to
contact face-to-face. A quesonnaire should have a short
explanaon of what your research is about. As with all
data collecon methods, quesonnaires should always
adhere to ethical and moral codes of conduct. An example
of a quesonnaire in use is the naonal populaon census
for India, which takes place every ten years (the last one
was in 2011).
Features of a Good Questionnaire1,3,4
Quesons should be smaller and to the point
Quesons should be in sequence
Quesons should proceed in a logical sequence moving
from easy to more dicult quesons. Personal and
inmate quesons should be le to the end
Technical terms should not be used in quesonnaires
Quesons may be dichotomous, mulple-choice or
open-ended
Quesons that can aect the answer of the respondents
should be avoided
Quesons must include all aspects of the problem
Collection of Data through Schedule1,3
There is a slight dierence between the methods of data
collecon through schedule and through quesonnaire.
Schedules are the proforma that contain a set of quesons.
They are lled in by people who are specically selected
for this purpose. They carry schedules to the respondents,
administer the quesons from the proforma in a sequence
wise manner and note down their responses in the space
provided for the same in the proforma.
Other Methods of Data Collection1,3
Warranty Cards: They are also called feedback cards.
They are usually a postal size card with some quesons
along with a request to the consumers to ll and return
them
Distributor or Store Audit: This can be performed
by distributers or manufacturers through their sales
representaves commonly and seasonal purchasing
paern
Pantry Audit: It is applied to esmate consumpon of
basket goods at the consumer level
Consumer Panel: It is an extension of pantry audit. It
is approached on a regular basis
Use of Mechanical Devices: Eye camera, pupilometric
camera, psychogalvanometer, moon picture camera
Methods of Collection of Secondary Data1,3
Sources of Published Data
Various publications of central, state, or local
government
Publicaon of foreign government and internaonal
sociees
Business industries, banks, stock exchanges, and reports
Trade journals
Books, magazines, and newspapers
Reports prepared by research scholars, universies,
economists
Public record, stascal and historical document
Sources of Unpublished Data
Autobiographies and biographies that are not published
Diaries, manuscripts accepted for publicaon but sll
“in-press”
Data from an unpublished study, letters, manuscripts
in preparation, memos, other communications such
as e-mails, and raw data
Data available with scholars and research workers,
trade rms, labour agencies, and other public or private
personnel, and sectors
Sources of Secondary Data Collection1,3,4
Databases
Somemes we can use informaon that is already stored
in a database, so that we don’t actually have to nd the
data. Databases are simply organised lists of data - the list
9
Mazhar SA et al.
J. Integ. Comm. Health 2021; 10(1)
ISSN: 2319-9113
DOI: hps://doi.org/10.24321/2319.9113.202101
of learners at a school is a kind of database. Databases
can be computerised, books or paper ling systems. A big
advantage of these is that the data is already organised
and is easy to access.
The aim of the research inuences the way data will be
collected. Four methods of collecng data are.
Conclusion
The process of data collecon is very essenal in the eld
of research. It is a basic tool of good research. If the data
collected is unbiased, it will be very useful. The medical,
social, polical and economic scenarios can be very easily
seen through this process. Basically, there are two types of
data, primary and secondary. The most common methods
of primary data collecon in behavioural sciences are
the observaon methods, interviews, quesonnaires,
and database. The sources of secondary data include the
previously published books, magazines, journals, etc.,
and unpublished autobiographies and biographies, etc.
The method of data collecon is chosen as per the aim of
the research and its suitability for that parcular type of
research that is to be conducted.
Acknowledgement
The author is thankful to the Chairperson for providing us
the Library facilies in the Dept. of Tahauzi wa Samaji Tib
of Ajmal Khan Tibbiya College, AMU, Aligarh. The author
also acknowledges the Supervisor and Co-supervisors for
their generous support and guidance and is also grateful
to the Research Methodology teacher for the valuable
inputs and suggesons in the compilaon of this arcle.
Source of Funding: None
Conflicts of Interest: None
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Figure 1.Methods of Data Collection
Selection of the Method of Data Collection6-8
The steps for choosing the technique of parcular data
collecon in a parcular type of study:
Step 1: Consider the me required
Step 2: Calculate the number of interviews required
Step 3: Time to carry out the interviews
Step 4: Determine the available me for study
The method of collecting data must be
suitable for the type of research we are doing.
Let’s look at examples to see why.
Worked Examples: Deciding on the Best Way
to Collect Data9
Which method would be appropriate for collecng data
for each of the cases below?
Table 1
To study the
knowledge and
percepons of
tuberculosis (TB)
among many
learners at a
school
Anonymous quesonnaires would
be useful so that learners don’t
have to worry about answering
incorrectly. Interviews by a skilled
interviewer could be useful so
that the interviewer could nd
out more about what the learners
know and believe about TB.
Whether bank
clients feel that
they are treated
professionally or
not by the bank
sta?
A quesonnaire that clients ll
in while vising a bank would be
a convenient way to collect this
informaon.
The symptoms of
hospital paents
with cancer.
Observaon (in the form of a
medical examinaon) would be
the best method.
The average age
of all learners in
Grade 10.
This informaon could be most
easily obtained from a database,
e.g. from the school’s register of
learners, which should have all the
learners’ dates of birth.
The number of
pens each learner
in a class has?
Quesonnaire OR Observaon
Weight of all
learners in a
class?
Quesonnaire OR Database (if this
info is recorded, e.g. for Physical
Educaon)
Customers’
opinion on the
new design of a
shop?
Interview
10
Mazhar SA et al.
J. Integ. Comm. Health 2021; 10(1)
ISSN: 2319-9113
DOI: hps://doi.org/10.24321/2319.9113.202101
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grade-10-mathemacal-literacy/.
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... Data primer dikumpulkan secara langsung dari subjek penelitian melalui observasi, penggunaan kuesioner, dan interaksi langsung dengan staf UD. Adi Jaya Mebel (Mazhar, 2021 Dari tabel 4 diatas, dapat dilihat bahwa hasil perbandingan berdasarkan kapasitas produksi menggunakan metode linear programing, diketahui jumlah kapsitas produksi yang mengalami peningkatan dari kondisi aktual dan optimal yaitu produk Copyright © 2024, Journal of Industrial Engineering Innovation Page | 69 Lisensi: cc-by-sa pintu utama dengan kondisi faktuan sebanyak 150 unit menjadi optimal dengan kapasitas produksi sebanyak 226 unit. Selanjutnya pada produk kusen pintu dengan kapasitas produksi faktual sebanyak 250 unit menjadi optimal dengan jumlah kapasitas yang diperoleh sebanyak 284 unit. ...
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... Developing AI models for food safety involves a series of critical steps to ensure the models are accurate, reliable, and effective. The process begins with data collection, which is foundational as the quality and variety of data directly influence model performance [202]. Data is gathered from various sources, including sensors that measure environmental factors, imaging systems that capture product quality, and textual data from regulatory documents and consumer feedback. ...
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Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.
... Additionally, transparent and Journal of South Asian Federation of Obstetrics and Gynaecology, Volume 16 Issue 6 (November-December 2024) accountable data collection fosters stakeholder engagement and validates a project. 52,53 In LMICs, data collection for quality improvement is complicated by the widespread use of paper-based systems. Hardcopy checklists and physical records pose several challenges; data gathering is laborious and time-consuming, requiring manual extraction from paper stacks, which increases error risk and delays, affecting data accuracy and timeliness. ...
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The problem statement outlines the complex challenges that organizations trying to optimize their international supply chain and logistics operations must overcome. Companies functioning in a globally networked environment face a complex terrain of opportunities and difficulties because of the globalization of supply chain management and logistics. The difficulties brought on by the globalization of supply chain management and logistics span several areas, such as cultural variety, regulatory inequalities, infrastructure constraints, and geopolitical unpredictability. Globalization in logistics offers many opportunities like market access, development of supply chain management, and technological advancements. On the other hand, it also causes challenges like supply chain complexities, and cross-broader regulations in logistics. The study used the process of secondary data collection and qualitative analysis through axial coding based on grounded theory. The main conclusions cover a synthesis of issues, from technological advances and legislative discrepancies to route optimization for transport and the function of technology in addressing these issues. The benefits of globalization also include expanded access to markets, economies of scale, and knowledge transfer. The conclusion emphasized the need for logistics and supply chain management to strike a balance between centralized control and localized response. Organizations must aggressively alter their approaches to take advantage of the advantages while navigating the obstacles in a constantly changing international marketplace as globalization continues to change the business landscape.
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Data collection is fundamental to behavioural research providing the foundation for understanding and interpreting human behaviour. While methodologies differ across disciplines, every research process relies on systematically collected data for analysis and interpretation. In the field of teacher education, research often examines the behaviours, practices and perceptions of teachers, students and other stakeholders, making data collection a critical component. However, researchers frequently encounter ethical, psychological, administrative and practical challenges during the data collection process, particularly in dynamic, real-world scenarios. This study explores the specific data collection challenges encountered in behavioural research with a focus on teacher education. Key issues include difficulties in selecting representative samples, designing appropriate tools, managing communication barriers, addressing gatekeeper-related concerns, ensuring ethical compliance, overcoming funding constraints and maintaining data security. Additionally, the unpredictable nature of real-world scenarios frequently introduces unanticipated obstacles, which can complicate even well-planned research processes. The findings of this study aim to guide early-career researchers in navigating these obstacles by offering strategies to address them. By enhancing understanding and preparedness, this study contributes to improve the precision and generalizability of research outcomes, enriching the broader field of behavioural research.
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