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The purpose of the assignment is to compare and contrast integrative reviews and systematic reviews. Please pick a topic and compare and contrast what the possible pros and cons are of an integrative review compared with a systematic review for that topic. You can dig deeper and find actual published work regarding your topic or use the review examples in Box 11.4

Chapter 12
Sampling
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Sampling
Process of selecting representative units of a
population for study in a research investigation
 Usually found in the methods section

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Sampling Concepts

Population
➢ Well-defined set with specific properties
➢ May be humans, medical records, specimens
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Sampling Concepts (Cont.)

Target population
➢ The entire set of cases about which the researcher
would like to make generalizations
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Sampling Concepts (Cont.)

Accessible population
➢ Available population that meets the criteria
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Sampling Concepts (Cont.)

Inclusion and exclusion criteria
➢ Inclusion criteria, also called eligibility criteria
➢ Exclusion criteria, also called delimitations
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Sampling Concepts (Cont.)

Inclusion and exclusion criteria
➢ Determine the subjects used in a study by defining
the criteria used to include or exclude a subject from
a study
➢ Must be explained by the researcher
➢ Control for extraneous variables or bias
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Samples and Sampling
Sampling: the process of selecting a portion or
subset of the designated population
 Sample: a set of elements that make up the
population

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Samples and Sampling (Cont.)

Element: the most basic unit about which
information is collected
➢ In nursing research, “elements” are usually
individuals.
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Samples and Sampling (Cont.)
Purpose: to be more efficient; it is not costeffective, or even feasible, to study an entire
population
 Should be representative; a representative
sample has the same key characteristics as the
entire population

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Types of Sampling
Nonprobability
 Inclusion in a group is
NOT random
 Less generalizable
 Less representative
 Three types:

Probability sampling
 Uses randomization to
assign elements
 More generalizable
 More representative
 Three types:

➢ Convenience
➢ Simple random sampling
➢ Quota
➢ Stratified random
➢ Purposive
sampling
➢ Cluster sampling
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Nonprobability Sampling

Convenience
➢ Use of the most readily accessible persons or objects
as subjects in a study
➢ Easy to recruit subjects
➢ Risk of bias greatest in this type of sample
➢ Used most with quantitative nonexperimental or
qualitative studies
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Nonprobability Sampling (Cont.)

Quota
➢ Knowledge about characteristics of the population of
interest used to build representativeness into the
sample
➢ Identifies the strata of the population and
proportionally represents the strata in the sample
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Nonprobability Sampling (Cont.)

Purposive
➢ Subjects selected who are considered to be typical of
the population
➢ Useful in studying populations with unusual/rare
characteristics
➢ Assumes that errors of judgment in overrepresenting
or underrepresenting characteristics of the population
in the sample will tend to balance out
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Nonprobability Sampling (Cont.)
Network sampling (Snowballing)—used for
locating samples that are difficult or impossible
to locate in other ways
 Use of social networks

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Nonprobability Sampling (Cont.)

Critiquing convenience samples
➢ What motivated some of the people to participate and
others not to participate (self-selection)?
➢ What kind of data would have been obtained if
nonparticipants had also responded?
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Nonprobability Sampling (Cont.)

Critiquing convenience samples
➢ How representative are the people who did participate
in relation to the population?
➢ What kind of confidence can you have in the evidence
provided by the findings?
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Probability Sampling

Uses random selection
➢ Each element of the population has an equal and
independent chance of being included in the sample.
Strongest type of sampling strategy
 Used in experimental and quasi-experimental
studies

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Probability Sampling (Cont.)

Simple random sampling
➢ Researcher defines the population (a set), lists all the
units of the population (a sampling frame), and
selects a sample of units (a subset) from which the
sample will be chosen.
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Probability Sampling (Cont.)

Advantages: simple random sampling
➢ Sample selection is not subject to conscious biases.
➢ Representativeness of the sample is maximized.
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Probability Sampling (Cont.)

Advantages: simple random sampling
➢ Differences in the characteristics of the sample and
the population are purely a function of chance.
➢ Probability of choosing a nonrepresentative sample
decreases as the size of the sample increases.
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Probability Sampling (Cont.)

Disadvantages: simple random sampling
➢ Time consuming and usually inefficient method of
obtaining a random sample
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Probability Sampling (Cont.)

Stratified random sampling
➢ Population divided into homogeneous strata or
subgroups
➢ Allows more representativeness
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Probability Sampling (Cont.)
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Probability Sampling (Cont.)

Advantages: stratified random sampling
➢ Enhanced representativeness of the sample
➢ Makes comparisons among subsets
➢ Disproportionately small stratum oversampled to
adjust for underrepresentation
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Probability Sampling (Cont.)

Disadvantages: stratified random sampling
➢ It is difficult to obtain a population list containing
complete critical variable information.
➢ It is time consuming.
➢ Enrolling proportional strata is challenging.
➢ A large-scale study is costly and takes time.
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Probability Sampling (Cont.)

Multistage (cluster) sampling
➢ A successive random sampling of units (clusters) that
progress from large to small
➢ Sampling units or clusters that can be selected by
simple random or stratified random sampling methods
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Probability Sampling (Cont.)
Multistage (cluster) sampling
 Advantages

➢ More economical in terms of time and money

Disadvantages
➢ More sampling errors tend to occur than with simple
random or stratified random sampling.
➢ Appropriate handling of the statistical data from
cluster samples is complex.
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Sampling Strategies
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Sample Size
Largest sample size possible
 Data saturation
 Pilot study

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Sampling Critiquing Criteria
Have the sample characteristics been
completely described?
 Can the parameters of the study population be
inferred from the description of the sample?

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Sampling Critiquing Criteria (Cont.)
To what extent is the sample representative of
the population?
 Are the criteria eligibility in the sample
specifically identified?
 Have sample delimitations been established?

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Sampling Critiquing Criteria (Cont.)
Would it be possible to replicate the study
population?
 How was the sample selected? Is the method of
sample selection appropriate?
 What kind of bias, if any, is introduced by this
method?

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Sampling Critiquing Criteria (Cont.)
Is the sample size appropriate? How is it
substantiated?
 Are there indications that rights of subjects have
been ensured?
 Does the researcher identify the limitations in
generalizability of the findings?

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Sampling Critiquing Criteria (Cont.)
Is the sampling strategy appropriate for the
design and level of evidence provided by the
design?
 Does the researcher indicate how replication of
the study with other samples would provide
increased support for the findings?

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Approximately what percent of
extra subjects are needed to
ensure the ability to detect
differences between groups or that
the effect of an intervention
remains intact?
When calculating sample size using power analysis, the total
sample size needs to consider that attrition or dropouts will occur.
A. 10%
B. 15%
C. 25%
D. 30%
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To determine ethnic differences
in cancer pain among four ethnic
groups in the United States, what
is the appropriate number of
patients per ethnic group that
should be used in the study?
A. 50
B. 68
C. 112
D. 480
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When might an entire population
be used in a research study?
A. When comprehensive results are desired
B. To increase generalizability of the findings
C. When the population size is very small
D. When the study is highly funded
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What type of sampling includes
using the Internet and social
networking to locate samples that
are otherwise difficult or
impossible to locate?
A. Snowball sampling
B. Simple random sampling
C. Cluster sampling
D. Matching
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