The document discusses identifying the population to be studied in research. It defines key population types including the target population, subject/respondent population, and stratum population. It also defines sampling, the sampling unit, sampling frame, and sampling design. The document outlines different sampling methods including probability sampling techniques like simple random sampling and stratified random sampling, and non-probability sampling techniques like convenience sampling and purposive sampling. It discusses the advantages and disadvantages of probability and non-probability sampling. The reasons for designing a sampling plan are also provided.

1. Research
February 22, 2012
Identifying the population to be studied
Population- refers to the accessible group of individuals from which the sample will be drawn by the
researcher, consistent with specific criteria, or the total possible participation of the group in the study
Population types:
1. Target population- group of individuals or objects which speculative info is desired
- Ex: all nursing students of the college of nursing
2. Subjects/respondent population- group of people/individuals chosen to provide data and info
needed in a research
- BSN 4 or the graduating students of XUCN
3. Stratum population- mutually exclusive segment of the population, distinguished by one or
more traits or qualifications
- Age- youngest to oldest
Sampling- process of selecting a representative portion of the population to represent the entire
population. It is a practical and efficient means of ensuring the quality data that will be gathered
a. Sampling unit- specific area or place which can be used during sampling process
b. Sampling frame- complete list of sampling units from which sample is drawn
c. Sampling design- the scheme that specifies the number of samples drawn from the population,
the inclusion and exclusion criteria for their choice, and the sampling techniques used, such as
purposive, random sampling, stratified sampling and convenience sampling, among others.
Example: sloven formula
4. Sample- a portion of the population from which data will be solicited for purposes of the
research. It is a subgroup of the population which constitutes the subjects or respondents of the
study
- sampling categories:
o respondents of the study who will respond to the survey, and/or
o Subjects of the study who will receive treatment or special attention dudring the
conduct of the study
5. Universe- the totality of elements to which research findings may apply. Refers to target
population, the group of people or objects from which the researcher intends to collect data
and generalize the findings of the study
- Elements- refers to entities that make up sample and population (e.g: pts, student nurses,
staff)

2. Differences between total and target population
- Total population- whole or total population
- Target population
Reasons for Designing the sampling plan.:
- Research requires an efficient and effective means of ensuring quality of data that will be
gathered
- Entails selection of appropriate subjects or respondents of the study that will generate data
specific to the purpose or objective of the inquiry
- To ensure validity and reliability of research findings, the researcher must come up with subjects
representative of the target population.
Methods of sampling:
1. Identify the target population or the Universe. The group to which you want to apply your
findings.
- Ex: all graduating students, all staff nurses of XUCHCC
2. Identify your respondent population. The portion of the target population accessible to you
from who you will draw needed data and information
- Ex: BSN graduating students
3. Specify the inclusion and exclusion criteria for respondent selection. Criteria must be specific
with respect to the characteristics of the respondents. State clearly the characteristics of the
population required in the study using the exclusion and inclusion criteria
- Ex: inclusion criteria- BSN graduating with no RLLE deficiencies; male and female
- Ex: exclusion- grades lower than 80%
4. Specify the sampling design. Once the respondent population is identified, decide how the
samples will be chosen and how large this will be my considering the representative proportion
of the population
- Ex: probability sampling, using simple random or the use of non-probability sampling using
purposive sampling
5. Recruit the subjects. When the sampling design has been determined, the next step is to recruit
the subjects and seek their cooperation and support. A screening instrument may be used to
determine if the subjects meet the inclusion and exclusion criteria set in the study
Types of categorization of sampling:
1. Non-probability sampling
a. Accidental or convenience sampling- uses the most readily available or most convenient
group of people or objects as study respondents
b. Quota sampling- divides the population into homogenous strata or sub-populations to
ensure representative proportions of the carious strata in the sample. The researcher

3. establishes desired proportion for some variables of interest to be able to elicit
homogenous data
c. Purposive or judgement sampling- subjects are handpicked to be included in the
sampling frame based on certain qualities for purposes of the study. Subjects are viewed
as “typical cases” or “experts” that provide enough data to answer the research
questions. Purposive sampling is the commonly used in qualitative studies
d. Snowball or network sampling- consists of the identification of a few persons who meet
the requisite characteristics of the study and who in turn refer other individuals who
may be interviewed. This process continues until the desired number of respondents is
reached. Snowball means effort that starts on a small scale and intensifies in the process
- Advantages: convenient and economical
- Disadvantages- it is likely to produce biased samples or errors in judgment because the
researcher cannot estimate the precise elements of the population that will be include in the
samples; certain elements may have no chance to be included in the sample
2. Probability sampling- involves the random selection of subjects or elements of the population,
to examine representative elements of the population
Types:
a. Simple random sampling- the selection on random basis of elements from a sampling
frame. Each element has an equal chance or probability of being chosen as subjects of
study.
b. Stratified random sampling- divides the population into homogeneous subgroups from
which elements are selected at random
c. Cluster sampling or multi-stage sampling- the successive selection of random samples
from larger to smaller units by either simple random or stratified random methods. It
involves several stages in drawing the samples from the population. Ex: province 
municipality  village  individual respondent
- At each stage, simple random, systematic techniques are used
d. Systematic or sequential sampling- the selection of every 10th name in a list of patients
in odd or even numbered rooms; every 6th baby in the nursery. The sequence of
selection can also be done, using odd or even numbered names on the sampling list
- Advantages of probability- less bias, as every element in the population is given an equal
(independent) chance to be selected
- Disadvantages- it is the time consuming, expensive, inconvenient
REASONS WHY?
- There are instances when complete involvement of all members of the population is not
possible
- Cheaper and more expeditious to involve only adequate sample subjects. Reduces cost and time
consumed

4. - Inclusion of all is often not worth the time & expense due to lack of intellectual capability.
Results from well selected sample can be as precise data obtained
- In some instances, the process of measurement can introduce spurious influence on the
research
- The number of study subjects should be kept as small as feasibly as possible when the
independent variable could have unpleasant side effects on the subjects