Type of Study: Observational Study
In an observational study, the researcher observes values
of the response variable and explanatory variables for the
sampled subjects, without anything being done to the
subjects (such as imposing a treatment).
In short, an observational study merely observes rather
than experiments with the study subjects. An experimental
study assigns to each subject a treatment and then
observes the outcome on the response variable.
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Observational Study – Sample Survey
A sample survey selects a sample of people from a
population and interviews them to collect data.
A sample survey is a type of observational study.
A census is a survey that attempts to count the number of
people in the population and to measure certain
characteristics about them.
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Example: Drug Testing and Student Drug
Use
A headline read: “Student Drug Testing Not Effective in
Reducing Drug Use” in a news release from the
University of Michigan.
Facts about the study:
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
76,000 students nationwide

497 high schools and 225 middle schools

Schools selected for the study included schools that
tested for drugs and schools that did not test for drugs

Each student filled out a questionnaire asking about
his/her drug use
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Example: Drug Testing and Student Drug
Use
Conditional Proportions on Drug Use
Questions:
1. What were the response and explanatory variables?
2. Was this an observational study or an experiment?
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Example: Drug Testing and Student Drug
Use
This study was an observational study.
In order for it to be an experiment, the researcher would
had to have assigned each school to use or not use drug
testing rather than leaving this decision to the school.
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Sampling Frame and Sampling Design
The sampling frame is the list of subjects in the population
from which the sample is taken, ideally it lists the entire
population of interest.
The sampling design determines how the sample is
selected.
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Simple Random Sampling, (SRS)
Random Sampling is the best way of obtaining a
sample that is representative of the population.
A simple random sample of ‘n’ subjects from a
population is one in which each possible sample of that
size has the same chance of being selected.
A simple random sample is often just called a random
sample. The “simple” adjective distinguishes this type of
sampling from more complex random sampling designs
presented in Section 4.4.
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SUMMARY: Using Random Numbers to
select a SRS
To select a simple random sample:
 number the subjects in the sampling frame using
numbers of the same length (number of digits).
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
select numbers of that length from a table of
random numbers or using a random number
generator.

include in the sample those subjects having
numbers equal to the random numbers selected.
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Accuracy of the Results from Surveys
with Random Sampling
Sample surveys are commonly used to estimate
population percentages.
These estimates include a Margin of Error which tells
us how well the sample estimate predicts the population
percentage.
When a SRS of n subjects is used, the margin of error
is approximately equal to 1
n
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 100%
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Poor Ways to Sample
Convenience Sample: a type of survey sample that is
easy to obtain.
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
Unlikely to be representative of the population.

Often severe biases result from such a sample.

Results apply ONLY to the observed subjects.
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Sample Surveys: Random Sampling Designs
It is not always possible to conduct an experiment so it is
necessary to have well designed, informative studies that
are not experimental, e.g., sample surveys that use
randomization.
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
Simple Random Sampling

Cluster Sampling

Stratified Random Sampling
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Sample Surveys: Cluster Random Sample
Cluster Random Sample
Preferable sampling design if
 a reliable sampling frame is unavailable, or
 the cost of selecting a SRS is excessive
Disadvantage
 Usually need a larger sample size than with a SRS
in order to achieve a particular margin of error.
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Sample Surveys: Stratified Random
Sample
Stratified Random Sample
 Advantage is that you can include in your sample
enough subjects in each group (stratum) you want
to evaluate.

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Disadvantage is that you must have a sampling
frame and know the stratum into which each
subject belongs.
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Comparing Random Sampling Methods
Figure 4.2 Ways of Randomly Sampling 40 Students. The figure is a schematic for a
simple random sample, a cluster random sample of 8 clusters of students who live together,
and a stratified random sample of 10 students from each class (Fresh., Soph., Jnr., Snr.).
Question: What’s the difference between clustering and stratifying?
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Comparing Random Sampling Methods
Comparison of different random sampling methods. A good sampling design ensures that
each subject in a population has an opportunity to be selected. The design should incorporate
randomness. Table 4.2 summarizes the random sampling methods we’ve presented.
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