Chapter 4 Gathering data

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Chapter 4Gathering data

• Learn .
• How to gather good data
• About Experiments and Observational Studies

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Section 4.1

• Should We Experiment or Should we Merely Observe?

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Population, Sample and Variables

• Population all the subjects of interest
• Sample subset of the population -
• data is collected on the sample
• Response variable measures the outcome of
  interest
• Explanatory variable the variable that explains
  the response variable

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Types of Studies

• Experiments
• Observational Studies

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Experiment

• A researcher conducts an experiment by assigning
  subjects to certain experimental conditions and
  then observing outcomes on the response variable
  
• The experimental conditions, which correspond to
  assigned values of the explanatory variable, are
  called treatments

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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)

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Example Does Drug Testing Reduce Students Drug
Use?

• Headline Student Drug Testing Not Effective in
  Reducing Drug Use
• Facts about the study
• 76,000 students nationwide
• 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 Does Drug Testing Reduce Students Drug
Use?
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Example Does Drug Testing Reduce Students Drug
Use?

• Conclusion Drug use was similar in schools that
  tested for drugs and schools that did not test
  for drugs

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Example Does Drug Testing Reduce Students Drug
Use?

• What were the response and explanatory variables?

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Example Does Drug Testing Reduce Students Drug
Use?

• Was this an observational study or an experiment?

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Advantages of Experiments over Observational
Studies

• We can study the effect of an explanatory
  variable on a response variable more accurately
  with an experiment than with an observational
  study
• An experiment reduces the potential for lurking
  variables to affect the result

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Experiments vs Observational Studies

• When the goal of a study is to establish cause
  and effect, an experiment is needed
• There are many situations (time constraints,
  ethical issues,..) in which an experiment is not
  practical

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Good Practices for Using Data

• Beware of anecdotal data
• Rely on data collected in reputable research
  studies

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Example of a Dataset

• General Social Survey (GSS)
• Observational Data Base
• Tracks opinions and behaviors of the American
  public
• A good example of a sample survey
• Gathers information by interviewing a sample of
  subjects from the U.S. adult population
• Provides a snapshot of the population

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Section 4.2

• What Are Good Ways and Poor Ways to Sample?

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Setting Up a Sample Survey

• Step 1 Identify the Population
• Step 2 Compile a list of subjects in the
  population from which the sample will be taken.
  This is called the sampling frame.
• Step 3 Specify a method for selecting subjects
  from the sampling frame. This is called the
  sampling design.

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Random Sampling

• Best way of obtaining a representative sample
• The sampling frame should give each subject an
  equal chance of being selected to be in the sample

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Simple Random Sampling

• 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

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Example Sampling Club Officers for a New
Orleans Trip

• The five offices President, Vice-President,
  Secretary, Treasurer and Activity Coordinator
• The possible samples are
• (P,V) (P,S) (P,T) (P,A) (V,S)
• (V,T) (V,A) (S,T) (S,A) (T,A)

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The possible samples are (P,V) (P,S) (P,T)
(P,A) (V,S) (V,T) (V,A) (S,T) (S,A)
(T,A)

• What are the chances the President and Activity
  Coordinator are selected?
• 1 in 5
• 1 in 10
• 1 in 2

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Selecting a Simple Random Sample

• Use a Random Number Table
• Use a Random Number Generator

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Methods of Collecting Data in Sample Surveys

• Personal Interview
• Telephone Interview
• Self-administered Questionnaire

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How Accurate Are Results from Surveys with Random
Sampling?

• Sample surveys are commonly used to estimate
  population percentages
• These estimates include a margin of error

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Example Margin of Error

• A survey result states The margin of error is
  plus or minus 3 percentage points
• This means It is very likely that the reported
  sample percentage is no more than 3 lower or 3
  higher than the population percentage
• Margin of error is approximately

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Be Wary of Sources of Potential Bias in Sample
Surveys

• A variety of problems can cause responses from a
  sample to tend to favor some parts of the
  population over others

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Types of Bias in Sample Surveys

• Sampling Bias occurs from using nonrandom
  samples or having undercoverage
• Nonresponse bias occurs when some sampled
  subjects cannot be reached or refuse to
  participate or fail to answer some questions
• Response bias occurs when the subject gives an
  incorrect response or the question is misleading

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Poor Ways to Sample

• Convenience Sample a sample that is easy to
  obtain
• Unlikely to be representative of the population
• Severe biases my result due to time and location
  of the interview and judgment of the interviewer
  about whom to interview

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Poor Ways to Sample

• Volunteer Sample most common form of
  convenience sample
• Subjects volunteer for the sample
• Volunteers are not representative of the entire
  population

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A Large Sample Does Not Guarantee An Unbiased
Sample
Warning