Types of Studies

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Types of Studies

• Observational Study
• Observes and measures characteristics without
  trying to modify the subjects being studied
• Experiment
• Impose a treatment on the subjects, then observe
  the response

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Types of Studies

• Cross-sectional Study
• Data are observed, measured, and collected at one
  point in time.
• Example What percentage of people own dogs?
• Most polls are cross-sectional studies

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Types of Studies

• Retrospective (or case control) Study
• Data are collected from the past
• Example What was the average rainfall in 1994?
• Prospective (or Longitudinal or Cohort) Study
• Data are collected in the future from groups
  (cohorts) sharing similar characteristics
• Example What percentage of dogs who attend an
  obedience class are still well-behaved 2 years
  later?

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Confounding

• When its not possible to distinguish the effects
  of each factor (i.e., which factor caused the
  outcome?)
• Usually, when there are multiple differences
  between comparison groups
• Confounding can be avoided by good study design

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Examples of Confounding

• Example A middle-school implements a new math
  curriculum. They also encourage parent
  participation, and offer after-school tutoring.
  An improvement in performance results
• Example An experiment is done to determine if
  students perform better on tests while listening
  to music. Each subject is given two similar
  tests the first in silence, and the second while
  listening to music. Performance is higher on the
  second test.

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Ways to control confounding

• Blocks
• Create groups with similar characteristics
• Ideally identical in every way except factor
  being compared
• Blinding
• Subjects dont know if theyre receiving a
  treatment or placebo
• Double-blinding
• Experimenters dont know which subjects are
  receiving the treatment

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Experiment Design(How to create blocks)

• Completely randomized experimental design
• Subjects are assigned to groups based on a
  process of random selection
• Rigorously controlled experimental design
• Subjects are very carefully chosen and assigned
  to groups so they have similar characteristics

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Sample size

• Sample Size
• Sample must be large enough to reveal the true
  nature of any effects
• Large samples do not make up for bad samples
  sample must be selected appropriately for results
  to be valid.

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Random Sampling

• Random Sample
• Members of the population are chosen so that each
  individual has equal likelihood of being chosen
• Simple Random Sample
• A special random sample where every possible
  sample is equally likely

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Other types of sampling

• Systematic sampling
• Population is ordered, and every kth element is
  chosen.
• This is only random sampling if the starting
  element is randomly chosen

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Other types of sampling

• Stratified sampling
• Population is divided into groups with similar
  characteristics, and a sample is chosen from each
  subgroup (stratum)
• This is only random sampling if the sample from
  each subgroup is chosen randomly

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Other types of sampling

• Cluster sampling
• Divide the population into sections, or clusters.
  Select a group of clusters, and use all members
  of those clusters.
• This is only random sampling if the clusters to
  be used are selected randomly

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Not-so-good sampling methods

• Voluntary response sample
• Convenience sample
• Choosing whoevers handy

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Sources of Error

• Sampling error
• The difference between the sample result and the
  population result, caused by chance fluctuations
• Non-sampling error
• Error caused by problems in collecting,
  recording, and analyzing the data (like broken
  tools, typos, or miscalculations), or by a biased
  sample (bad sample selection)

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Differences in error

• Sampling error is natural and unavoidable. We
  must consider it when analyzing our data, but we
  cannot eliminate it.
• Non-sampling error is avoidable, and every effort
  should be taken to do so.

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Homework

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