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Sampling Design - Coggle Diagram
Sampling Design
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Define the Population Parameters
Sample statistics estimate population parameters based on data, and the choice of variables and measurement scales influences the type, size, and properties of the sample used for analysis.
Define the Sample Frame
The sample frame is a list of cases from which the sample is drawn, but it often differs from the desired population due to inaccuracies or omissions.
A Community as Sample Frame
a collection of digitally literate individuals
who want to engage with each other
and with a company to share ideas, concerns,
information and to assist with decision making
Define the Number of Cases
The ultimate test of
a sampling design is
how well any cases
we measure represent
the characteristics
of the target population
the design purports to represent.
Sample versus Census
Sampling allows us to draw conclusions about an entire population by examining a subset of it.
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Greater Speed of Data Collection
using a sample drawn from a target population will
always take less time than conducting a census.
Availability of Population Cases
Drawing a sample is also the only process
possible if the population is infinite.
Better Quality Results
“the possibility of better interviewing (testing), more thorough investigation of missing, wrong, or suspicious information, better supervision, and better processing than is
possible with complete coverage
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Precision
measured by the standard error of estimate, a type of standard deviation measurement;
Sample Size
If a census is chosen, then the researcher
need not determine sample method or size
Define the Sampling Method
Probability sampling is technically superior because it allows random selection and provides precision estimates, while nonprobability sampling is arbitrary and subjective.
Case Selection
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Restricted
Probability
Complex random
- Systematic
- Cluster
- Stratified
- Double
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Probability Sampling
a controlled procedure that
assures that each case
is given a known nonzero chance of selection
Simple Random Sampling
the purest form of probability sampling, where each population element has an equal and known chance of selection.
Complex Probability Sampling
Alternative sampling designs offer greater
statistical and economic efficiency by achieving
desired precision with smaller sample
sizes and lower costs.
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Stratified Random Sampling
The process by which the sample is constrained
to include cases
from each of the segments
Proportionate vs Disproportionate Sampling
Proportionate stratified sampling divides a population into strata proportional to their size,
while disproportionate is used when
strata variances or costs vary.
Cluster Sampling
Area Sampling
applied to
national pop.,
county pop.,
and where there are
well-defined political or
natural boundaries
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Double Sampling
more convenient or economical to collect some information by sample and then use this information as the basis for selecting a subsample for further study
Nonprobability Sampling
Nonprobability sampling is chosen for practical reasons like cost, time, and specific objectives, despite bias risks and limited generalizability.
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Snowball Sampling
sampling locates hard-to-find respondents through referrals, making it ideal for qualitative studies
and niche populations.
Define the Selection and Recruiting Protocols
Researchers must carefully select participants and ensure their cooperation through proper methods and incentives.
Ethical Issues and Their Solutions
Ethical sampling issues involve deception, incentives, and quality, requiring careful management of consent, confidentiality, and sample selection standards.