This document discusses data sampling and collection methods. It begins by defining quantitative and qualitative data, and primary and secondary data. The main methods of primary data collection are observation, interviewing, and questionnaires. Secondary data refers to existing data collected by others. The document then defines data sampling as selecting part of a data set to make inferences about the whole. Sampling is needed to save time and money. Random sampling gives the best results while non-random sampling includes quota, accidental, judgemental, expert and snowball sampling. Mixed sampling uses elements of both random and non-random designs.

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Data Sampling and Collection
Dr Dhaval Pujara
Professor & Head
Department of Electronics & Communication Engineering,
Institute of Technology, Nirma University
Ahmedabad 382 481
Email: dhaval.pujara@nirmauni.ac.in
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Presentation Outline
 What is Data?
 Quantitative Vs Qualitative Data
 Primary and Secondary Data
 Methods of Primary Data Collection
I. Observation
II. Interviewing
III. Questionnaire
 Secondary Data Collection
 Data Sampling
 Need for Data Sampling
 Types of Sampling
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What is Data?
 Data is a existing information/knowledge
represented or coded in some form suitable
for better usage or processing.
 Data are a set of values of qualitative or
quantitative variables about one or more
persons or objects.
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Quantitative Vs Qualitative Data
 Quantitative data is defined as the value of data in the form
of counts or numbers where each data-set has an unique
numerical value associated with it.
 Quantitative data is used to answer questions such as “How
many?”, “How often?”, “How much?”.
 Qualitative data is a categorical measurement expressed
not in terms of numbers, but rather by means of a natural
language description.
 Qualitative data is also called categorical data since this data can
be grouped according to categories.
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Example - Quantitative and Qualitative Data
Data Unit Numeric
Variable
Quantitative
Data
Categorical
Variable
Qualitative
Data
A person How
many children
do you have?
2 Children In which
country were
your children
born?
India
How much do
you earn?
Rs.60,000
(per month)
What is your
occupation?
Teacher
How many
hours do you
work?
40 hours per
week
Do you
work full-time
or part-time?
Full-time
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Primary and Secondary Data
The primary data are those
which are collected afresh and
for the first time, and thus
happen to be original in
character.
Primary Data
The secondary data are those
which have already been
collected by someone else and
which have already been
passed through the statistical
process.
Secondary Data

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Primary and Secondary Data
The task of data collection begins after a research problem
has been defined and research design/plan chalked out.
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A researcher as per requirement of study may decide on
use of primary data or secondary data or both.
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Secondary data are already available, one has to carefully
choose the sources , relevancy of data and reliability.
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Sources of secondary data are existing literature, reports
of professional agencies, archives, Internet, etc.
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While collecting secondary data one has to follow legal
procedures required and maintain the academic ethics.
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Methods of Data Collection
 The selection of Data Collection Method depends on several
parameters, like:
a) Nature, scope and object of the research being undertaken
b) Availability of funds
c) Availability of Time
d) Precision required
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Methods of Data Collection
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Primary Data Collection
 When a researcher is more interested in the behaviour than
in the perceptions of individuals, the data are to be collected
by observations.
 There are two types of observations:
I. Participant Observation;
II. Non-Participant Observation
 In Participant Observation, a researcher participate in the
activities of the group being observed in the same manner as
its members, with or without their knowing that they are
being observed.
 In Non-Participant Observation, a researcher, does not get
involved in the activities of the group but remain a passive
observer, watching and listening to its activities and drawing
conclusions from this.
Observation
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Problems
Observation as a Method of Data Collection
 When individuals or groups become aware that they are being observed, they may
change their behaviour. The observations may be faulty in such situations.
 There is always a possibility of observer bias.
 The interpretations drawn from observations may vary from observer to observer.
 There is the possibility of incomplete observation and/or recording.
 An observer may watch keenly but at the expense of detailed recording.
 The opposite problem may occur when the observer takes detailed notes but in doing
so misses some of the interaction.
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Primary Data Collection
Interviewing is a commonly used
method of collecting information from
people.
In this technique, the researcher has a
freedom
 to decide the format and content of
questions to be asked,
 to select the wording of the
questions,
 to decide the way we want to ask the
questions
 to choose the order in which the
questions are to be asked.
Interviewing

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Merits & Demerits
Interviewing as a Method of Data Collection
Merits:
More and in-depth information obtained
Personal Information
Greater Flexibility
Adaptation as per the respondent
Demerits:
Bias of Interviewer
Expensive/Time Consuming
Need expertise
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Primary Data Collection
 A questionnaire is a written list of questions, the answers to
which are recorded by respondents.
 The layout of a questionnaire should be such that it is easy to
read and the sequence of questions should be easy to follow.
 A questionnaire should be developed in an interactive style.
 The most common approach to collecting information is to
send the questionnaire to prospective respondents by mail.
Questionnaire
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Sample Questionnaire
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Merits & Demerits
Questionnaire as a Method of Data Collection
Merits:
Low Cost
Time Saving
No interviewer’s bias
Respondent’s convenience
Demerits:
Can be used only for educated people
Many questions remain unanswered
The respondent can consult other persons before filling in the questionnaire.
Sometimes different respondent’s interpreted questions differently
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Secondary Data Collection
 Secondary data means data that are already available.
 The data which have already been collected and analysed by someone else.
 Secondary data may either be published data or unpublished data.
 Usually published data are available in:
 various publications of the central, state are local governments;
 various publications of foreign governments or of international bodies
 technical and trade journals;
 books, magazines and newspapers;
 reports and publications of various associations connected with business and
industry, banks, stock exchanges, etc.;
 reports prepared by research scholars, universities, economists, etc. in different
fields; and
 statistics, historical documents, and other sources of published information. 1
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What is Data Sampling?
Sampling may be defined as the
selection of some part of an
aggregate on the basis of which a
judgement or inference about the
aggregate or totality is made.
In other words, it is the process of
obtaining information about an
entire population by examining
only a part of it

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Concept of Data Sampling
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Need for Data Sampling
Sampling is used in practice for a variety
of reasons :
 Sampling can save time and money.
 It produces results at a relatively faster
speed.
 Sampling remains the only way when
population contains infinitely many
members.
 Sampling is useful when a test involves the
destruction of the item under study.
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Sampling Terminology
Study Population:
The class, families living in the city from which you select your sample are called the
population or study population, and are usually denoted by the letter N.
Sample Size:
The number of students, families from whom you obtain the required information is called
the sample size and is usually denoted by the letter n.
Sample:
The small group of students, families from whom you collect the required information is
called the sample.
Sample Statistics:
Your findings based on the information obtained from your respondents (sample) are called
sample statistics.
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Types of Sampling
Random / Probability
Sampling Designs
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Non-Random /
Non-Probability
Sampling Designs
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Mixed
Sampling Designs
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Types of Sampling
Random / Probability
Sampling Designs
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A random sample is a sample in which every member of a
population has an equal chance of being selected.
The results of random sampling are amongst the best if
adequate sample size is selected.
The random samples can be generated by the Computer
Programme or by a table of randomly generated numbers.
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Types of Sampling
Non-Random /
Non-Probability
Sampling Designs
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Non-probability sampling designs do not follow the theory of
probability in the choice of elements from the sampling
population.
There are five commonly used non-random designs:
1. Quota sampling
2. Accidental sampling
3. Judgemental sampling or purposive sampling
4. Expert sampling
5. Snowball sampling

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Types of Sampling
Quota sampling: In this sampling method, the researchers choose samples according to
specific traits or qualities.
Accidental sampling: This method involves taking a population sample that is close at hand,
rather than carefully determined and obtained.
Judgemental sampling or purposive sampling: In this method, the researchers rely on their
own judgment when choosing members of the population to participate in their study.
Expert sampling: In this method, the sample are selected from experts in the field you're
studying. It's used when you need the opinions or assessment of people with a high degree
of knowledge about the study area.
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Types of Sampling
Snowball sampling: It is the process of selecting a sample using networks. To start with, a
few individuals in a group or organisation are selected and the required information is
collected from them. They are then asked to identify other people in the group or
organisation, and the people selected by them become a part of the sample. Information is
collected from them, and then these people are asked to identify other members of the
group and, in turn, those identified become the basis of further data collection.
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Types of Sampling
Mixed
Sampling Designs
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It has the characteristics of both random and non-random
sampling designs.
*SRS – Simple Random Selection
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