The document discusses sample and sampling techniques used in research. It defines key terms like population, sample, sampling, and element. It describes two main sampling techniques - probability sampling which uses random selection, and non-probability sampling which uses non-random methods. Some examples of probability sampling techniques include simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. Examples of non-probability sampling include convenience sampling, quota sampling, and purposive sampling. Sample size is determined using formulas like Slovin's formula.

1. Sample and Sampling
Techniques

2. THE POPULATION
• Consists of the totality or aggregate of the
observations with which the researcher is
concerned

3. • Population is an accessible group of
people who meets a well-defined set of
eligibility criteria.
• The utmost importance in selecting a
population is that
– “the population should be clearly defined so
that the sample can be accurately identified.”

4. • The Specific Population types are:
– Target population
• is a group of individuals who meets the criteria.
– Subject or respondent population
• refers to a group of individuals participating in the study.
– Strata or stratum
• is described as a mutually exclusive segment of a population
established by one or more characteristics.

5. THE SAMPLING
• Sample
– Subset of the population that is selected for a
study
• Also called subjects or respondents of the study

6. • Sampling
– Process of choosing a representative portion
of the entire population.
– an integral part of research methodology.
– involves selecting a group of people, events,
behaviors or other elements with which to
conduct a study.

7. • Element
– most basic unit about which information is
collected.

8. • Representativeness
– means that the sample must be like the
population in as many ways as possible.
– The accessible population must be
representative of the target population.

9. • Example of a sample:
– The population of BSN students is 600, only
200 BSN students are included as the target
population and only 100 students are chosen
as samples for the actual study.

10. Eligibility Criteria

11. • Eligibility Criteria
• A description chosen by the researcher to define
which elements should be included in or excluded from
the population.
• Such criteria may include sex, age, marital status,
education level and diagnosis.

12. SAMPLING THEORY

13. SAMPLING THEORY
• is developed to determine mathematically
the most effective way to acquire a sample
that would accurately reflect the
population under study.

14. • Key concepts of sampling theory includes:
– Sampling unit
• refers to specific place or location which can be
used during sampling process.
– Sampling frame
• describes the complete list of sampling units from
which the sample is drawn.

15. SAMPLING CRITERIA

16. SAMPLING CRITERIA
• refers to the essential characteristics of a
subject or respondent such as ability to
read and write responses on the data
collection instruments.

17. The steps involved in sampling
include:
• Identify the target population
• Identify the subject or respondent population
• Specify the criteria for subject or respondent selection
• Specify the sampling design
• Recruit the subjects

18. SAMPLE SIZE

19. SAMPLE SIZE
• Prior to the selection of sampling technique, the
nurse-researcher must first determine the size of
the sample.

20. • A sample size can be determined using the
Slovin’s (1960) formula, which is as follows:
N
n = ---------------
1 + Ne2
Where:
n is the sample size
N is the population size
e is the margin of error
1 is a constant value

21. • Example:
– From the population of 10,000 clients with
tuberculosis, a nurse-researcher selected a
sample size with a margin of error of 5%.
– The desired sample size is computed to be
385

22. TYPES OF SAMPLING
TECHNIQUES

23. TYPES OF SAMPLING
TECHNIQUES
• two basic sampling techniques used in
nursing research:
– probability (random) sampling
– nonprobability (nonrandom) sampling.

24. SAMPLING TECHNIQUES
NON-PROBABILITY PROBABILITY
CONVENIENCE SIMPLE RANDOM
QUOTA SYSTEMATIC
PURPOSIVE STRATIFIED
CLUSTER
MULTI-STAGE

25. • Probability Sampling
• Involves the selection of elements from the population
using random in which each element of the
population has an equal and independent chance of
being chosen.

26. Four Classification of Probability
Sampling
1. Simple Random Sampling
• Each member of the population has an equal chance of being included in
the samples
• Most commonly used method is the lottery or Fish Bowl
technique
• In using the lottery method, there is a need for a complete listing of the
members of the population.
• The names or codes of all members are written on pieces of paper cards
and placed in a container.
• The researcher draws the desired number of sample from the container.
• The process is relatively easy for small population but relatively difficult
and time consuming for a large population

27. 2. Systematic Sampling Technique
• Type of probability sampling which selects samples
by following some rules set by the researcher which
involves selecting the Kth member where the random
start is determined.
• A system is a plan for selecting members after a starting
point or random start has been determined.
• Then every nth member of the population will be
determined by the system in drawing or selecting the
members of the sample

28. 3. Stratified Random Sampling
– Type of probability sampling which selects members
of the sample proportionally from each subpopulation
or stratum.
– Used when the population is too large to handle and
is divided into subgroups (called strata)
– Samples per stratum are then randomly selected,
but considerations must be given to the sizes of the
random samples to be drawn from the subgroups.
– An example of procedure to use is proportional
allocation which selects the sample sizes proportional
to the sizes of the different subgroups.

29. 4. Cluster Sampling
– Used when population is divided into groups
or clusters
– Samples are selected in groups rather
than individuals which is employed into a
large-scale survey

30. 5. Multi-Stage Sampling
– Selects samples using more than two
sampling techniques
– Rarely used because of the complexity of its
application
– Requires a lot of effort, time, and cost

31. 2. Non-Probability Sampling

32. 2. Non-Probability Sampling
– Involves the selection of elements from a
population using nonrandom procedures.

33. Characteristics of Non-Probability
Sampling
2. The members of sample are drawn or selected based
on the judgment of the researcher.
4. The results of these techniques are relatively biased.
6. The techniques lack objectivity in terms of the
selection of samples.
8. The samples are not so reliable.
5. The techniques are convenient and economical to use.

34. Types of Non-Probability
Sampling

35. Types of Non-Probability
Sampling
1. Convenience or Accidental Sampling
– Involves the nonrandom selection of subjects
based on their availability or convenient
accessibility.
2. Quota Sampling
– Involves the nonrandom selection of elements
based on the identification of specific
characteristics to increase the sample’s
representativeness.

36. Types of Non-Probability
Sampling
3. Purposive of Judgmental Sampling
– Involves the nonrandom selection of elements based
on the researcher’s judgment and knowledge about
the population.
– This is useful when a group of subjects is
needed to participate in a pretest of newly developed
instruments or when a group of experts is desirable to
validate research information