This document discusses research methods, including sources of data, methods of collecting data, and sampling techniques. It describes primary and secondary sources of data, as well as direct/interview, indirect/questionnaire, registration, observation, and experimentation as methods of collecting data. The document also explains probability sampling techniques like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It provides Slovin's formula for determining sample size and discusses margin of error. Non-probability sampling techniques such as convenience, quota, and purposive sampling are also outlined.

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2. SOURCES OF DATA
• PRIMARY SOURCES
• SECONDARY SOURCES
METHODS OF COLLECTING DATA
• DIRECT /INTERVIEW
• INDIRECT/QUESTIONNAIRE
• REGISTRATION
• OBSERVATION
• EXPERIMENTATION
SAMPLING TECHNIQUES
• SLOVIN’S FORMULA
• NON – PROBABILITY SAMPLING
• PROBABILITY SAMPLING

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In doing a research, if the population is too
big, a substantial number of samples is
acceptable. One way of getting a number of
samples is by using Slovin’s formula:
where n is the sample size
N is the population size
e is the margin of error

4. MARGIN OF ERROR “e”
• The margin of error is a value which
quantifies possible sampling errors.
• Sampling error means that the results in the
sample differ from those of the target
population because of the “luck of the draw”

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6. TYPES OF SAMPLING TECHNIQUES
NON – PROBABILITY SAMPLING PROBABILITY SAMPLING
CONVENIENCE SIMPLE
SYSTEMATIC
QUOTA
STRATIFIED
PURPOSIVE CLUSTER

7. • PROBABILITY SAMPLING
Samples are chosen in such a way that each
member of the population has a known though not
necessarily equal chance of being included in the
samples.
• ADVANTAGES OF PROBABILITY SAMPLING
1. It avoids biases.
2. It provides basis for calculating the margin of error

8. • SIMPLE RANDOM SAMPLING: Samples are chosen at random with
members of the population having known or sometimes equal
probability or chance of being included in the samples.
a) Lottery
b) Sampling with the use of Table of Random Numbers
• SYSTEMATIC RANDOM SAMPLING: Samples are randomly chosen
following certain rules set by the researchers
• STRATIFIED RANDOM SAMPLING: This method is used when the
population N is too big to handle, thus dividing N into subgroups
called STRATA.
• CLUSTER SAMPLING: Cluster sampling is sometimes called area
sampling because it is usually applied when the population is
large. Groups or clusters instead of individuals are randomly
chosen.

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• NON – PROBABILITY SAMPLING:
Each member of the population does not have a
known chance of being included in the sample.
Instead, personal judgement plays a very
important role in the selection.

10. • CONVENIENCE SAMPLING: This type is used
because of the convenience it offers to the
researcher.
• QUOTA SAMPLING: This is very similar to
stratified random sampling. The only difference is
that the selection of the members of the samples in
stratified sampling is done randomly.
• PURPOSIVE SAMPLING: Choosing the
respondents on the basis of predetermined criteria
set by the researcher.