1. 1
MEAL BASICS MASTERCLASS
DAY TWO
30 JULY 2023.

2. 2
METHODS OF DATA
COLLECTION AND
SAMPLING TECHNIQUES.

3. 3
METHODS OF DATA
COLLECTION.

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Qualitative Research Approach
o Qualitative research is a method of inquiry that aims to understand
people's experiences, motivations, beliefs, and mental and social
dynamics. Unlike quantitative research, it studies relatively small
samples of respondents and rarely uses statistical methods for data
analysis. Qualitative researchers use a variety of tools and methods
in their research
o Qualitative research is primarily exploratory research. It is used to
gain an understanding of underlying reasons, opinions, and
motivations.

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Qualitative Research Approach cont’d
o The process of research involves emerging questions and procedures,
data typically collected in participants’ setting, data analysis that
inductively builds from particular to general themes, and the
research interpreting the data.
o The focus is on clarifying new phenomenon and understanding
context.
o Thus, qualitative research focuses on words and uses open-ended
questions and some what less structured instruments for data
collection.

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Common qualitative data collection tools
Interviews
Most of the researchers collect qualitative data using interviews (Denzin &
Lincoln, 1998). It is identified that the activities, attitudes and views of the
participants could be easily interpreted with the help of these interviews
(Walsham, 1995). Denzin and Lincoln (1998) categorized the interviews as
structured interviews, semi-structured interviews and unstructured
interviews. In structured interviews, the interviewer would follow the
script/guidelines whereas in unstructured interviews, the interviewee would
be questioned as per the idea of the interviewer.

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Common qualitative data collection tools
Semi-structured questionnaires
A combination of closed and open questions will be included in these
questionnaires. It is used for business-to-business market research in
which a wide range of different responses are evaluated.
Unstructured questionnaires
Questions those could generate free responses will come under this
category. They are called as guided conversations and referred to as a
“topic guide”. They are mostly suitable for depth interviewing (face-to-face,
depth telephone interviews).

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Common qualitative data collection tools
Observation
Through observation, the researcher could monitor the behavior, events, or noting
physical characteristics of the participants. This method can be used in both
experimental and on social and anthropological research. Scientific observation is a
methodical way of recognizing and noting a fact or occurrence, often involving some
sort of measurement. Scientific observation should be specific, and recorded
immediately (T aylor-Powell; Steele , 1996).
Focus Groups
In this approach, highly experienced researchers will form a Focus group comprising 10
to 18 participants and record their opinion and beliefs through in depth qualitative
feedback. The research will probe into specific areas that are of interest to the company
commissioning the research (Krueger, 1988).

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QUANTITATIVE RESEARCH
APPROACH.

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Quantitative Approach
Quantitative research is a method of research that involves
collecting and analyzing numerical data
. It is used to find patterns and averages, make predictions, test
causal relationships, and generalize results to wider populations.
Quantitative research is widely used in the natural and social
sciences, such as biology, chemistry, psychology, economics,
sociology, marketing, and others.

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Types of Quantitative Research
1.Descriptive Research: This type of research is used to explain the current state of a
variable or topic. It can answer what, where, when, and how, but not why questions
2.Correlational Research: This research is used to determine if there is a relationship
between two or more variables. It can be used to make predictions about future events
3.Causal-Comparative/Quasi-Experimental Research: This research is used to
determine if there is a causal relationship between two or more variables. It is similar to
experimental research, but the researcher does not have control over the independent
variable
4.Experimental Research: This research is used to determine if there is a causal
relationship between two or more variables. The researcher has control over the
independent variable
5.Predictive Research: This research is used to predict future events based on current
data. It can be used to make forecasts or to identify trends.

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Methods used in Quantitative data collections
1. Surveys: Surveys involve asking a set of structured questions to a large number of
respondents. Surveys can be conducted through various means, such as paper, kiosk,
mobile, or online questionnaires.
2. Experiments: Experiments are controlled procedures where variables are
manipulated to observe their effects. This method is often used in scientific research to
establish cause-and-effect relationships
3. Controlled observations: In controlled observations, researchers carefully observe
and record data in a controlled environment. This method allows for systematic data
collection and analysis
4. Interviews: Interviews can be conducted to collect quantitative data by asking
respondents a standard set of close-ended questions. This method provides structured
data and can be more in-depth than surveys.

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Methods used in Quantitative data collections
5. Longitudinal studies: Longitudinal studies involve collecting data from
the same subjects over an extended period of time. This method allows for
the analysis of changes and trends over time
6. Polls: Polls are a type of survey that collects data from a specific
population to gauge opinions or preferences on a particular topic.
7. Telephone interviews: Telephone interviews involve conducting
interviews over the phone, typically using a structured questionnaire. This
method allows for efficient data collection from a large number of
respondents.

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SAMPLING TECHNIQUES

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COMMON SAMPLING
TECHNIQUES IN
QUANTITATIVE RESEARCH.

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Sampling methods.

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Simple Random Sampling
In a simple random sample, every unit in the sample frame has the same
probability of being selected. So you have a list (sample frame) of
individuals or schools or classrooms or some other unit, and you use one
of a variety of methods to randomly select individuals off of that list-- for
example, assigning every person a number and then drawing numbers out
of a hat. In the example show, 5 students (in red) have been randomly
selected from a population of 20 students.
Simple random samples require smaller sample sizes than other random
sampling methods, but it certainly may not be the most cost-effective
method if the sample is really spread out geographically.

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Simple random Sampling
Ex: Randomly select 50 people from a population of 200

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Stratified Sampling
A stratified sampling strategy is used when you want to make sure that a specific
subgroup is well-represented in your sample when it would likely be poorly represented
in a simple random sample. The population is divided into non-overlapping subgroups
called strata and then within each stratum you randomly select a certain number of
individuals or units.
You can make sure there is representation of minority groups this way– often this may
be the only way you can effectively generalize about small subgroups from your data
(see Trochim).
If you use stratified sampling methods, you may have to use sample weights when
analyzing the data to make sure that the results accurately reflect the population.
In this example, I have divided the population into boys and girls and even though there
are fewer girls, I am randomly choosing 5 girls and 5 boys. Since I’m oversampling girls
I should use sample weights when analyzing the data.

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Stratified Sampling
Randomly select 5 boys and 5 girls from the population

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Cluster Random Sampling
Cluster random sampling is when you draw a sample from
“naturally-occurring” clusters like schools, households, etc. This is
a very cost-effective method since it is often easier to travel to a
set number of clusters and interview all the individuals in that
cluster than it would be to randomly select and interview
individuals from across all of the clusters. It is also helpful to use
when you know all of the clusters in your sampling frame but not
all of the individuals (this is often the case for us– we have lists of
schools, but not typically a list of all the students in every school).

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Cluster Random Sampling
Ex: Randomly select 3 schools from the population, then sample all students in each school

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COMMON SAMPLING
METHODS IN QUALITATIVE
RESEARCH.

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Sampling Methods
WE ARE PRESENTING APPROACHES THAT ARE
COMMONLY USED FOR QUANTITATIVE AND
QUALITATIVE RESEARCH. BUT RESEARCHERS
USE RANDOM AND NON-RANDOM SAMPLING
WITH BOTH TYPES OF APPROACHES.

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Convenience Sampling
Convenience Sampling
Selection of participants that is based on accessibility, ease, speed, and/or cost
• Least desirable sampling method
• Most researchers avoid this method unless they have no other option
Examples
• Researcher has limited time and interviews the first 4 pregnant mothers who
show up at the clinic in the morning
• Emergency situations

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Purposive Sampling
Purposive Sampling
Systematically selecting participants based on knowledge of how the outcomes would appear
• Allows researcher to improve validity of findings
• Keeps research focus on specific characteristics of participants or method of selection
Examples
• A researcher is studying the impact of a mother education program on child health outcomes. As
part of this study the researcher includes fathers in the sample knowing that fathers often affect
how and when mothers can take the child to a clinic.
• Case studies: “Norm,” negative deviants, positive deviants

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Maximum Variation Sampling
Maximum Variation Sampling
Often, researchers want to understand how a phenomenon is seen and understood among different
people, who have distinctly different characteristics. This is a type of purposeful sampling where the aim
of the researcher is to sample for heterogeneity in lived experience.
• A researcher selects participants that maximize the diversity relevant to the research question
• A researcher has to have a prior hypothesis for each dimension of variation used for sampling
Example
• In studying maternal perceptions of perinatal care the researcher includes mothers of different
ages, and mothers with different number of children in the sample

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Criterion Sampling
Criterion Sampling
Sampling from a pool of participants who meet a pre-determined criteria.
• Useful for identifying and understanding cases that are information rich
• Can provide an important qualitative component to quantitative data
• Often used for identifying cases from a standardized questionnaire that might be
useful for follow-up
Example
• In studying how children interact with a new reading curriculum, the researcher
includes children who have different literacy levels on the baseline placement
test.

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Snowball Sampling
Snowball Sampling
Researcher uses well informed people to identify critical cases or participants who have a great deal of
information about a phenomenon. Researcher follows chain of contacts in order to identify and
accumulate critical cases.
• Can be a useful way in which to identify a small number of key cases
• Often used along with Key Informant Interviews
• While not fully systematic, method offers a clear line of reasoning for sampling decisions
• Can be done during data collection, prior to data collection, or both
Example
• In interviewing informal employers who hire youth, the researcher asks employers for their
recommendations on other employers to speak with.

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THE END