Download Sampling methods 2015

Designing Studies
In order to produce data that will truly answer the
questions about a large group, the way a study is
designed is important.
1) Decide on the population of interest.
Population: an entire group of individuals from/about
which information is wanting to be collected
A study of the
population is
called a
census.
Sample: the part of the
population that actually
participates in the study or
experiment
A study of a
sample is called a
sampling.
2) Determine what type of study you are going
to perform.
Observational Study:
Experiment:
Observe individuals
and measure
variables of interest
without influencing
the response
Deliberately apply
some treatment to
the individuals in
order to observe the
response
vs.
3) Determine the sampling method (how you choose the
actual sample to participate in your study)
Sampling methods are important to ensure the
conclusions can be extended to the entire population.
Voluntary Response Sample
Individuals choose to
participate in the study as a
response to a general
appeal
(Ex: call in polling)
Convenience Sample
Individuals chosen to
participate in the study
are easy to access
(Ex: interviews at a mall)
Both of these methods could create biased results
(ie: design systematically favors certain outcomes)
This is BAD !
The best way to choose the sample for a study or
experiment is by a Simple Random Sample (SRS).
This means a set of individuals are chosen in a way so
that every individuals (or set of individuals) in the
population had an equal chance to be selected.
Without technology you can do this is by a
Random Digits Table
1) Assign a number to each individual in the
population (2 – 3 digits)
2) Starting at a particular row of the table, each 2 – 3
digits in the series will represent a member of the
population (ignore repeated values)
3) Stop once you have reached the desired number of
members in your sample.
Using 2 digit #’s for the members of this class (01 – 20):
62, 56, 87, 02, 06, 40, 32, 50, 36, 99, 71, 08, 02, 25, 53, 11, 48, 61, 17, 76
Other Types of Random Samples
Stratified Random Sample:
- population is divided into groups of individuals
that have an important similarity (called strata)
or subpopulations
- an individual is chosen from each strata to
create the sample
Cluster Sample:
- population is divided into groups or clusters
- some of the clusters are randomly selected to
create the sample
Other Types of Random Samples Continue
Systematic Sample:
-Population is separated into even groups. The
people in the groups are ordered from 1 to n.
- Someone is randomly selected within one of the
groups.
-From that person, every 𝑁 𝑡ℎ person is selected.
Warnings!!!!
If it is not stated that a simple random sample (SRS) has
been used in choosing the sample, then you must analyze
the description of the study or experiment carefully to
ensure that a random sampling method has been used.
When dealing with humans, identifying a entire
population can be difficult. Because of this a degree of
undercoverage can occur. (some groups in the
population will be left out)
When a member of the sample cannot be contacted or is
uncooperative, this is called non-response. This along
with response bias (lying) can cause issues with the
study’s results.
Wording of the question can also cause bias. Avoiding
confusing or leading questions will help reduce this effect.
When extending results to a population, larger samples
will generally give more accurate results than smaller
samples.