Methodology

By: Group 6
 Meet The Group

Eujhan Buendia
Member
Zeke Plaza
Member
Ivy Alvarado Raya Mangundadatu
Member Member
 Meet The Group

Alisa Villapando Guendoleen Batiancila Mafia Ciudad

Leader Secretary Conductor
CHAPTER 3 -
Comprehensive Description
METHODS/
METHODOLOG
Y •Research Design
•Samples and Sampling
Techniques
•Research Instruments
•Validation of Instruments
•Data Gathering Procedures
•Statistical Tools and Treatments
Research Research Design - Research design
Design refers to the overall strategy utilized
to answer research questions. A
research design typically outlines
the theories and models underlying a
project; the research question of a
project; a strategy for gathering
data and information; and a strategy
for producing answers from the data.
Qualitative Variable
A qualitative variable, also called categorical, is
one in which the variable categories are not
described as numbers but instead by verbal
groupings. There are two classifications of
categorical data: nominal and ordinal. Nominal
variables have “names,” not numerical values.
Quantitative Variable
A qualitative variable, also called categorical, is
one in which the variable categories are not
described as numbers but instead by verbal
groupings. There are two classifications of
categorical data: nominal and ordinal. Nominal
variables have “names,” not numerical values.
 Similarities
share concerns "in problem-finding, in
explaining the relationships of data to
claim, in theory, building and in explaining
particular cases in the light of established
knowledge and theory.
 Differences
Qualitative research

Deals directly with historical problems of

cause and effect or interpretation of
unique social phenomena.
 Differences
Quantitative Research

Represent the responses of large

numbers of individuals to different kinds
of stimuli.
 When you conduct research about a group of people, it’s
Samples rarely possible to collect data from every person in that
group. Instead, you select a sample. The sample is the
and group of individuals who will actually participate in the
research.

Sampling To draw valid conclusions from your results, you have to

carefully decide how you will select a sample that is
Techniques representative of the group as a whole. This is called a
sampling method. There are two primary types of
sampling methods that you can use in your research:
 2 Primary types
Prohability of Sampling
Probability sampling involves
random selection, allowing you to Method
make strong statistical inferences
about the whole group.
Non-Prohabilty
Non-probability sampling involves non-
random selection based on convenience
or other criteria, allowing you to easily
collect data.
 Sampling Frame Example; Sampling Frame

The sampling frame is the You are doing research on working conditions at a
social media marketing company. Your population is
actual list of individuals all 1000 employees of the company. Your sampling
frame is the company’s HR database, which lists the
that the sample will be names and contact details of every employee.
drawn from. Ideally, it Sample size
The number of individuals you should include in your
should include the entire sample depends on various factors, including the size
target population (and and variability of the population and your research
design. There are different sample size calculators
nobody who is not part of and formulas depending on what you want to achieve
that population). with statistical analysis.
Prohability Sampling Method

Probability sampling means that every

member of the population has a chance of
being selected. It is mainly used in quantitative
research. If you want to produce results that
are representative of the whole population,
probability sampling techniques are the most
valid choice.
 Four main types of probability
sampling.
1. Simple random sampling
In a simple random sample, every Example : Simple random sampling
member of the population has an
You want to pick a
equal chance of being selected.
Your sampling frame should random fruit written on
include the whole population. To a piece of paper, so, you
conduct this type of sampling, you put 10 papers inside a
can use tools like random number basket then you choose
generators or other techniques 2.
that are based entirely on chance.
 2. Systematic sampling Four main types of
Like stratified sampling, cluster sampling probability sampling.
also involves separating the population into
subgroups, or clusters. But that’s where the
two probability sampling methods diverge. Example: Systematic sampling
With cluster sampling, each cluster should
have similar characteristics to the For instance, if a local NGO is
population. Instead of selecting individuals seeking to form a systematic
from each and every cluster, you would
begin by randomly selecting entire clusters.
sample of 500 volunteers from a
If possible, you might include every population of 5000, they can
individual from each selected cluster in your select every 10th person in the
final sample. If the clusters are too large, you
would need to randomly select individuals
population to build a sample
from each cluster. systematically.
 Four main types of probability
3. Stratified random sampling sampling.
Many populations can be divided into Example: Stratified random sampling
smaller groups based on specific
characteristics that don’t overlap but
To give a quick example here:
represent the entire population when For research, the target market
put together. With stratified random is split into two strata based on
sampling, you would draw a sample gender, where there are 2,000
from each of these groups (or strata)
males and 6,000 females. Then,
separately. This allows you to make
for a sampling fraction of ¼, 500
sure that every subgroup is properly
represented, which leads to more males and 1,500 females will be
accurate results than simple random selected in the final sample
sampling. population.
 Four main types of probability
sampling
4.Cluster sampling Example: Cluster Sampling
Like stratified sampling, cluster sampling also involves Like stratified sampling, cluster sampling also involves
separating the population into subgroups, or clusters. separating the population into subgroups, or clusters.
But that’s where the two probability sampling methods But that’s where the two probability sampling methods
diverge. With cluster sampling, each cluster should have diverge. With cluster sampling, each cluster should have
similar characteristics to the population. Instead of similar characteristics to the population. Instead of
selecting individuals from each and every cluster, you selecting individuals from each and every cluster, you
would begin by randomly selecting entire clusters. If would begin by randomly selecting entire clusters. If
possible, you might include every individual from each possible, you might include every individual from each
selected cluster in your final sample. If the clusters are selected cluster in your final sample. If the clusters are
too large, you would need to randomly select individuals too large, you would need to randomly select individuals
from each cluster. from each cluster.
Research Instrument
A Research Instrument is a tool used to collect, measure, and
analyze data related to your research interests. These tools are
most commonly used in health sciences, social sciences and
education to assess patients, clients, students, teachers, staff,
etc. A research instrument can include interviews, tests, surveys,
or checklists.

The Research Instrument is usually determined by researcher

and is tied to the study methodology.
 Example of Research
Instrument
Types of Research Instruments:
Interviews: Interviews or the
Interaction where verbal
questions are posed by an
Interviewer to elicit verbal
responses from an interviewee.
5 types of Research Instruments;

Structured Interview: A formal

set of questions posed to each
interviewee and recorded using
a standardized procedure.
5 types of Research Instruments;
Unstructured Interview: A less
formal set of questions; the
interviewer modifies the
sequence and wording of
questions
5 types of Research Instruments;
Non-Directive Interview: An
unguided interview, including
open-ended questions and
use of spontaneous
engagement.
5 types of Research Instruments;
Focus Interview: An Emphasis
on the interviewees subjective
and personal responses where
the interviewer engages to elicit
more information.
5 types of Research Instruments;
Focus Group Interview: A group
of selected participants are
asked about their opinions or
perceptions concerning a
particular topic.
 Validation of Instruments
Validity refers to the extent to which an
instrument measures what it was intended to
measure. Therefore, an instrument is
considered "valid" if it measured what it set
out to measure. Validity is associated with
quantitative data collection, and requires
various statistical techniques and concepts
to establish.
Examples of validity These
include:
A questionnaire may be considered valid
because each question addresses
specific and relevant aspects of the
study subject. In a brand assessment
study, researchers can use comparison
testing to verify the results of an initial
study.
 Data Collection 40

Process
30
Data Collection is the process of gathering
accurate information through different collection
methods to answer research problems and 20
understand existing probabilities. The data
collected can be in text, numbers, images, or any
other type of format. It is then processed and 10
organized to make it useful for decision-making. By
understanding and analyzing data, businesses can
make more informed decisions, improve their 0
Item 1 Item 2 Item 3 Item 4 Item 5
operations, and understand their customers better.
 Key Steps in Step 1: Defining the Goal of Research

Data Step 2: Choosing Data

Collection Collection Method

Process Step 3: Planning Data Collection

Procedures
The Data Collection Process
Involves Five Key Steps:
Step 4: Colllecting Data

Step 5: Cleaning an Organizing the Data

Step 1: Defining the Goal of Research

To collect data, you need to define what you want to learn from your
research. The goal of your research should be clear, concise, and
measurable. For example, if you’re conducting a survey, you might
include a list of questions like:

What types of products do customers prefer?

Which colors do customers prefer?
Do customers prefer different sizes?
Are there specific features that customers would like to see included in
future products?
 Step 2: Choosing Data Collection Method

There are many data collection techniques, and each has

its advantages and disadvantages. Choose the method
best suited to your research and will help you directly
address your research questions based on the data you
intend to collect.
Step 3: Planning Data Collection Procedures

Once you’ve identified which data collection method you’ll be using, you need
to plan the steps to collect the data. Planning includes deciding how you’ll
collect the data, who will manage it, when you’ll collect it, and where you’ll
collect it.

You can use traditional paper-based methods such as surveys through

physical copies of questionnaires. Or take advantage of digital ways to
gather the data such as mobile data collection or online data collection.

You’ll also need to choose the right records management software to help
you collect and manage your data.
 Step 4: Collecting Data

After you’ve planned your data collection procedures,

it’s time to collect the data. This step will vary
depending on the method you chose in Step 2. For
example, if you’re conducting a survey, you’ll need to
administer the survey to your participants. If you’re
doing a case study, you’ll need to observe and
interview your participants.
Step 5: Cleaning and Organizing the Data

After you’ve collected your data, it’s essential to

clean and organize it. This step is critical since it will
improve the accuracy of your data and make it
easier to evaluate. Afterwards, it will be analyzed
and used to discover any patterns and
relationships in the data using an algorithm.
 Statistical Tools
Are the mean or the arithmetical
average of numbers, median and
mode, range, dispersion, standard
deviation, interquartile range,
coefficient of variation, and
descriptive statistics.
 Example:
Example : A medical doctor can
prescribe three different type of
drugs to three different groups of
patients respectively to see the
effectiveness of
each drug.
Statistical tools are:
Standard Deviation - is another very widely
used statistical tools or method. It analyzes
the deviation of different data points from
the mean of the entire research

Formula:
Statistical tools are:
Mean / Average - is one of the most
popular method in statistical tools. This
determines the overall trends of the
data.
Formula:
Statistical tools are:
Descriptive Statistics - this provides the
summary of the main characteristics of a
data sets. Theses include the mean, median,
mode, standard deviationn etc.

Formula:
Statistical tools are:
The median is the middle value when a data
set is ordered from least to greatest. The
mode is the number that occurs most often
in a data set.
Formula:
Formula:
Statistical tools are:
Dispersion refers to the range of distribution of data
about an expected value. It shows the relation of
distribution from the standard or the central value. It is
an essential factor when estimating the quality,
volatility, and yield of data under any statistical
observation.
Formula:
Statistical tools are:
The interquartile range (IQR) measures the spread of
the middle half of your data. It is the range for the
middle 50% of your sample. Use the IQR to assess the
variability where most of your values lie. Larger values
indicate that the central portion of your data spread
out further.
Formula:
Statistical tools are:
The interquartile range (IQR) measures the spread of
the middle half of your data. It is the range for the
middle 50% of your sample. Use the IQR to assess the
variability where most of your values lie. Larger values
indicate that the central portion of your data spread
out further.
Formula:
Statistical tools are:
The coefficient of variation (CV) is the ratio of the
standard deviation to the mean. The higher the
coefficient of variation, the greater the level of
dispersion around the mean. It is generally expressed
as a percentage.

Formula:
Thank You