Transcript Chapter 8 Producing Data: Sampling HS 67 BPS Chapter 8

Chapter 8
Producing Data: Sampling
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BPS Chapter 8
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From Exploration to Inference
Exploratory Data Analysis
Statistical Inference
Purpose: identify and
describe patterns in data
Purpose: answer specific
question
Conclusions apply to data
and specific circumstance
Conclusions apply beyond
data and to broad
circumstance
Conclusions are formal
Conclusion are informal
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Types of Studies
• Observational studies →
individuals are studied without an
experimental intervention (e.g., most
surveys)
• Experimental studies →
individuals receive an experimental
intervention to determine its effect
(e.g., a study of a drug effectiveness)
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Example of an Observational Study
(Weight Gain & CHD)
• Purpose: understand relationship between
weight gain and coronary heart disease (CHD)
• 115,818 women, 30 to 55 years of age, recruited
in 1976
• Measure weight and height at age 18 and at
recruitment, record weight gain
• Followed individuals for 14 years
• Record fatal and nonfatal CHD outcomes (1292
cases)
• Adjusted results for lurking variables such as
Source:of
JAMA
1995;273(6):461-5
smoking and family history
CHD
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Illustrative Example: Results
Compared to subjects who gained less
than 11 pounds:
• Subjects who gains 11 to 17 lbs: 25%
more likely to develop CHD
• 17 to 24 lbs gained: 64% more likely
• 24 to 44 lbs gained: 92% more likely
• 44+ lbs gained: 165% more likely
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Illustrative Example (Questions)
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What is the population in this study?
What is the sample?
What makes this study observational?
Can we say that weight gain caused
CHD?
• Can we say weight gain is associated
with CHD?
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Sample Quality
• Poor quality samples favor a certain outcome
 misleading results  sampling bias
• Examples
– Voluntary response sampling: Allows
individuals to choose to be in the study, e.g.,
call-in polls (pp. 178–9 in text)
– Convenience sampling: individuals that are
easiest to reach are selected, e.g.,
Interviewing at the mall (p. 179)
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Voluntary Response Bias
• To prepare for her book Women and Love,
Shere Hite sent questionnaires to 100,000
women asking about love and sexual
relationships
• Only 4.5% responded
• Respondents “were fed up with men and
eager to fight them…”
• Selection bias: “angry women [were] more
likely” to respond  sampling bias
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Convenience Sample
• A lab study was conducted to see if a
drug affected physical activity in lab
animals
• The lab assistant reached into the
cage to select the mice for study
• The less active mice were chosen 
made it seem like the drug decreased
physical activity  sampling bias
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Simple Random Sample (SRS)
• To avoid sampling biases, use chance
(random) mechanisms to select subjects
• The most basic random sampling
mechanism  Simple Random Sample
(SRS)
• SRSs  every conceivable subset has
the same chance to be studied
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Selecting a SRS
• Methods: we can “pick them from a hat”,
use a random number generator, or use a
table of random digits (Table B) to derive
our sample
• We will use Table B
– Each digit 0 to 9 is equally likely
– Entries are independent (knowledge of one
entry gives no information about any other
entries)
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Choosing a
Simple Random Sample (SRS)
STEP 1: Label each individual in the
population with a identification
number
STEP 2: Use Table B to select numbers
at random (enter table at a
different location each time it is
used)
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Selecting a SRS (Illustration)
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Population of N = 30 individuals
Labeled the individuals 01 – 30
Select a row in table at random
Enter table at different random location each
time (e.g., to illustrate, enter at row 106)
• Row 106 with lines to indicate pairs
68|41|7 3|50|13| 15|52|9
• First two individuals relevant entries are 13
and 15
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Remainder of Chapter
• Not responsible for the sampling designs
discussed on pp. 200–201
• Are responsible for the cautions (pp. 201–202)
– Undercoverage: some population groups left out
of sampling process  sampling bias
– Nonresponse bias: some individuals do not
respond or refuse to participate  sampling bias
– Even good quality samples may not be a perfect
reflection of the population due to random sampling
error  unavoidable & dealt with in future chapters
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