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1. Methods of Data
Collection, Sampling
Techniques and
Methods in Presenting
Data Prepared by:
Gonzaga, RG Luis Vincent P.
Tinebro, Roderick
Dagot, Climnt Arzene
Gonzaga, RG Luis Vincent P.
Tinebro, Roderick
Dagot, Climnt Arzene

2. Methods of Data Collection
1. Observation
2. Interview
3. Schedule
4. Questionnaire

3. Observation
•Observation seeks
to ascertain what
people think and
do by watching
them in action as
they express
themselves in
various situations
and activities.

4. Interview
• The interview is, in a
sense, an oral
questionnaire.
• Instead of writing the
response, the
interviewee or subject
gives the needed
information verbally in
a face-to-face
relationship.

5. Schedule Method
• Schedule is very much similar to
questionnaire and there is very little
difference between the two so far as
their construction is concerned.
• The main difference between these
two is that whereas the schedule is
used in direct interview on direct
observation and in it the questions
are asked and filled by the
researcher himself.

6. Questionnaire
• Questionnaire provides the
most speedy and simple
technique of gathering data
about groups of individuals
scattered in a wide and
extended field. In this
method, a questionnaire
form is sent usually by post
to the persons concerned,
with a request to answer
the questions and return
the questionnaire.

8. Sampling techniques
•Probability (Random) Sampling
•Non-probability (Non-random)
Sampling

9. Probability (Random) Sampling
• In probability (random) sampling, you
start with a complete sampling frame of
all eligible individuals from which you
select your sample. (unbiased)
1. Simple random sampling
2. Systematic sampling
3. Stratified sampling
4. Clustered sampling

10. Simple random sampling
• In this case each individual is
chosen entirely by chance and
each member of the
population has an equal
chance, or probability, of
being selected. One way of
obtaining a random sample is
to give each individual in a
population a number, and
then use a fish bowl and a
paper(Draw lots).

11. Systematic sampling
•Individuals are selected at regular
intervals from the sampling frame. The
intervals are chosen to ensure an adequate
sample size. If you need a sample
size n from a population of size x, you
should select every x/nth individual for the
sample. For example, if you wanted a
sample size of 8 from a population of BSN
1-5 which is 40, select every 40/8 =
5th member of the sampling frame.

13. Stratified sampling
• In this method, the population is first divided
into subgroups (or strata) who all share a
similar characteristic.
• For example, in a study of stroke outcomes, we
may stratify the population by sex, to ensure
equal representation of men and women. The
study sample is then obtained by taking equal
sample sizes from each stratum.

15. Clustered sampling
• The population is divided into subgroups,
known as clusters, which are randomly
selected to be included in the study.
• In single-stage cluster sampling, all
members of the chosen clusters are then
included in the study.
• In two-stage cluster sampling, a selection
of individuals from each cluster is then
randomly selected for inclusion.

17. Non-probability (Non-random)
Sampling
• In non-probability (non-random) sampling,
you do not start with a complete sampling
frame, so some individuals have no chance
of being selected. (Bias)
1. Convenience sampling
2. Quota sampling
3. Judgement (or Purposive) Sampling
4. Snowball sampling

18. Convenience sampling
•Convenience sampling is perhaps the
easiest method of sampling, because
participants are selected based on
availability and willingness to take
part.
•Useful results can be obtained, but
the results are prone to significant
bias.

19. Quota sampling
•Interviewers are given a quota of
subjects of a specified type to attempt
to recruit.
•For example, an interviewer might
be told to go out and select 20 adult
men, 20 adult women, 10 teenage
girls and 10 teenage boys so that
they could interview them about
their television viewing.

20. Judgement (or Purposive) Sampling
•Also known as selective, or
subjective, sampling, this technique
relies on the judgement of the
researcher when choosing who to ask
to participate. This approach is often
used by the media when canvassing
the public for opinions and in
qualitative research.

21. Snowball sampling
• This method is commonly used in social
sciences when investigating hard-to-
reach groups. Existing subjects are
asked to nominate further subjects
known to them, so the sample increases
in size like a rolling snowball.
• Snowball sampling can be effective when
a sampling frame is difficult to identify.

22. Methods in Presenting Data
The main portion of Statistics is the
display of summarized data. Data is
initially collected from a given source,
whether they are experiments, surveys, or
observation, and is presented in one of four
methods:
• Textual Method
• Tabular Method
• Graphical Method

23. Textual Method
• Text is the main method of conveying information
as it is used to explain results and trends, and
provide contextual information. Data are
fundamentally presented in paragraphs or
sentences. Text can be used to provide
interpretation or emphasize certain data. If
quantitative information to be conveyed consists of
one or two numbers, it is more appropriate to use
written language than tables or graphs.

24. Example
•For instance, information about the
incidence rates of delirium following
anesthesia in 2016–2017 can be presented
with the use of a few numbers:
•“The incidence rate of delirium following
anesthesia was 11% in 2016 and 15% in
2017; no significant difference of incidence
rates was found between the two years.”

25. Tabular Method
• Tables, which convey information that has been
converted into words or numbers in rows and
columns. Anyone with a sufficient level of
literacy can easily understand the information
presented in a table. Tables are the most
appropriate for presenting individual
information, and can present both quantitative
and qualitative information.

27. Graphical Method
•Whereas tables can be used for presenting
all the information, graphs simplify
complex information by using images and
emphasizing data patterns or trends, and
are useful for summarizing, explaining, or
exploring quantitative data.

28. Bar graph and histogram
• A bar graph is used to indicate and compare values in a
discrete category or group, and the frequency or other
measurement parameters (i.e. mean). Depending on the
number of categories, and the size or complexity of each
category, bars may be created vertically or horizontally.

29. Pie chart
• A pie chart, which
is used to represent
nominal data (in
other words, data
classified in
different
categories),
visually represents
a distribution of
categories.

30. References
• https://www.healthknowledge.org.uk/public-health-
textbook/research-methods/1a-epidemiology/methods-of-
sampling-population
• http://www.yourarticlelibrary.com/social-research/data-
collection/top-6-methods-of-data-collection-explained/64498
• https://en.wikibooks.org/wiki/Statistics/Methods_of_Data_Col
lection
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5453888/

31. Thank You for
Listening…!