Research methods and statistics_Quantitative research

1. Research Methods and
Statistics

2. Give the two types of Sampling and when do you
use each?
• Probability sampling involves random selection,
allowing you to make strong statistical inferences about
the whole group.
• Non-probability sampling involves non-random
selection based on convenience or other criteria,
allowing you to easily collect data.

3. Probability Sampling Methods
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.

4. 4 types of Probability Random Sampling
1. Simple random Sampling. In a simple random sample, every
member of the population has an equal chance of being
selected.
Example. You want to select a simple random sample
of 100 employees of Company X. You assign a
number to every employee in the company database
from 1 to 1000, and use a random number generator
to select 100 numbers.

5. Types of Probability Random Sampling
2. Systematic Sampling. Respondents are chosen on intervals
Example. All employees of the company are listed in
alphabetical order. From the first 10 numbers, you randomly
select a starting point: number 6. From number 6 onwards,
every 10th person on the list is selected (6, 16, 26, 36, and so
on), and you end up with a sample of 100 people.

6. 4 types of Probability Random Sampling
3. Stratified Sampling. To use this sampling method, you divide
the population into subgroups (called strata) based on the
relevant characteristic (e.g. gender, age range, income bracket,
job role).
Example. The company has 800 female employees and 200 male
employees. You want to ensure that the sample reflects the gender
balance of the company, so you sort the population into two strata based
on gender. Then you use random sampling on each group, selecting 80
women and 20 men, which gives you a representative sample of 100
people.

7. 4 types of Probability Random Sampling
4. Cluster Sampling. Cluster sampling also involves dividing the
population into subgroups, but each subgroup should have
similar characteristics to the whole sample.
Example. The company has offices in 10 cities across the
country (all with roughly the same number of employees in
similar roles). You don’t have the capacity to travel to every
office to collect your data, so you use random sampling to
select 3 offices – these are your clusters.

8. Non-probability Sampling Methods
• In a non-probability sample, individuals are selected based on
non-random criteria, and not every individual has a chance of
being included.

9. 4 Types of Non-Probability Sampling Method
1. Convenience Sampling. A convenience sample simply includes
the individuals who happen to be most accessible to the
researcher.
Example. You are researching opinions about student
support services in your university, so after each of your
classes, you ask your fellow students to complete
a survey on the topic.

10. 4 Types of Non-Probability Sampling Method
2. Voluntary Response Sampling. Instead of the researcher
choosing participants and directly contacting them, people
volunteer themselves (e.g. by responding to a public online
survey).
Example. You send out the survey to all students at your
university and a lot of students decide to complete it.

11. 4 Types of Non-Probability Sampling Method
• 3. Purposive Sampling. This type of sampling, also known as
judgement sampling, involves the researcher using their
expertise to select a sample that is most useful to the purposes
of the research.
Example. You want to know more about the opinions and
experiences of disabled students at your university, so you
purposefully select a number of students with different
support needs in order to gather a varied range of data on
their experiences with student services.

12. 4 Types of Non-Probability Sampling Method
• 4. Snowball Sampling. If the population is hard to access,
snowball sampling can be used to recruit participants via other
participants. The number of people you have access to
“snowballs” as you get in contact with more people.
Example. You are researching experiences of homelessness in
your city. Since there is no list of all homeless people in the city,
probability sampling isn’t possible. You meet one person who agrees
to participate in the research, and she puts you in contact with other
homeless people that she knows in the area.

13. When should you use Probability Random
Sampling?
• Probability sampling is best used when the goal of the
research is to study a particular subgroup within a greater
population.
• 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.

14. When should you use Non-Probability Random
Sampling?
• Use this type of sampling to indicate if a particular trait or
characteristic exists in a population.
• Researchers widely use the non-probability sampling method when
they aim at conducting qualitative research, pilot studies, or
exploratory research.
• Researchers use it when they have limited time to conduct research
or have budget constraints.
• When the researcher needs to observe whether a particular issue
needs in-depth analysis, he applies this method.
• Use it when you do not intend to generate results that will generalize
the entire population.

15. Explain the integration of research question, main
theory and main method in the research proposal.
• A research question is "a question that a research project sets
out to answer“.
• Theories are formulated to explain, predict, and understand
phenomena and, in many cases, to challenge and extend existing
knowledge within the limits of critical bounding assumptions. The
theoretical framework is the structure that can hold or support a theory of a
research study.
• Research methods are the strategies, processes or techniques utilized
in the collection of data or evidence for analysis in order to uncover
new information or create better understanding of a topic.

16. Explain 5 examples how statistical methods complement
research methods in defining the Statement of the
Problem

17. Quantitative Research
• Quantitative research methods are designed to collect
numerical data that can be used to measure
variables. Quantitative data is structured and statistical; its
results are objective and conclusive. It uses a grounded theory
method that relies on data collection that is systematically
analyzed. Quantitative research is a methodology that provides
support when you need to draw general conclusions from your
research and predict outcomes.

18. Qualitative Research
• Qualitative research is a methodology designed to collect non-
numerical data to gain insights. It is non-statistical and
unstructured or semi-structured. It relies on data collected
based on a research design that answers the question “why.”
• Qualitative data collects information that seeks to describe a
topic more than measure it. This type of research measures
opinions, views, and attributes vs. hard numbers that would be
presented in a graph or a chart.

19. Explain 5 examples how statistical methods complement
research methods in defining the Statement of the
Problem
Statement of the
Problem
Research Methods Statistical Methods
1. Competency level Quantitative Research Pre-test and Post-test
(Instrument)
Average mean
Paired t-test
2. Experiences Qualitative Research Instrument: Journal,
Interview, FGD, Observation
Sheets
3. Effects of the intervention Qualitative and Quantitative
(It depends)
Pre-test and Post-test
T-test, Z-test

20. Using your own proposed study/proposal for your
thesis, briefly provide the following
Problem Data Needs Sources of Data Methods to be used
1.
2.
3.
4.

21. Discuss some legal constraints placed on researchers
conducting researches using human subjects. Mention at
least 5 guidelines to be observed in planning and
conducting research so as to protect the rights and
welfare of the subject.

22. Data Privacy Act
Republic Act No. 10173, otherwise known as the Data Privacy
Act is a law that seeks to protect all forms of information, be it
private, personal, or sensitive. It is meant to cover both natural
and juridical persons involved in the processing of personal
information.

23. Intellectual Property Rights
• Intellectual property rights are the rights given to persons
over the creations of their minds. They usually give the
creator an exclusive right over the use of his/her creation for a
certain period of time.

24. Objectivity
• Strive to avoid bias in experimental design, data
analysis, data interpretation, peer review, personnel
decisions, grant writing, expert testimony, and other
aspects of research.

25. Carefulness
• Avoid careless errors and negligence; carefully and
critically examine your own work and the work of your
peers. Keep good records of research activities.

26. Responsible Publication
• Publish in order to advance research and scholarship,
not to advance just your own career. Avoid wasteful and
duplicative publication.

27. Social Responsibility
• Strive to promote social good and prevent or mitigate
social harms through research, public education, and
advocacy.

28. Non-discrimination
• Avoid discrimination against colleagues or students on
the basis of sex, race, ethnicity, or other factors that
are not related to their scientific competence and
integrity.

29. Human Subject Protection
• When conducting research on human subjects,
minimize harms and risks and maximize benefits;
respect human dignity, privacy, and autonomy.

30. Animal Care
• Show proper respect and care for animals when using
them in research. Do not conduct unnecessary or poorly
designed animal experiments.