Naturalistic Observation and Case-Study Research

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Naturalistic Observationand Case-Study Research

• Psyc 50
• Chapter 6

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Naturalistic Observation Examples

• Charles Darwins voyage on the HMS Beagle (the
  basis for his theory of natural selection)
• Jane Goodall's study of chimpanzees
• Dian Fosseys study of the mountain gorilla
• Adeline Levines study of the Love Canal
• Rosenhans study of psychiatric hospitalization

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Case-Study Examples

• Sigmund Freuds study of patients, which formed
  the basis for his psychoanalytic theory
• E. L. Witmers study of children in the first
  psychology clinic in North America
• Jean Piagets study of the development of
  children, which led to numerous theories of child
  development

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When to use low-constraint methods

• For questions involving the natural flow of
  behavior
• When first studying a new research area
• When testing the feasibility of a procedure
• To test the generalizability of laboratory
  findings

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Problem Statementsand Hypotheses

• Problem statements are often general and flexible
  in low-constraint research studies
• Often evolve to accommodate new results
• Unable to test causal hypotheses with
  low-constraint research

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Value of these methods

• Provide new descriptive information
• Can negate a general proposition
• Provide information about contingencies

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Challenge of Low-Constraint Research

• Can be very difficult to observe behavior in
  natural surroundings
• Often we are not sure what behaviors are
  important until after we have observed for a
  while
• Without the controls of the laboratory,
  participants are free to do what they want to do,
  and not what we are hoping to observe

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Making Observations

• Unobtrusive observation observing behavior
  without participants knowledge
• Participant observation observing behavior
  while participating in the situation
• Want to reduce measurement reactivity
• People behaving differently when observed
• Reactive measures enhance reactivity
• Nonreactive measures minimize reactivity

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Sampling

• Sampling of participants
• Representative samples allow us to generalize
  findings to the larger group
• Sampling is often not under the control of the
  researcher in low-constraint research
• Therefore, caution is required in interpreting
  the results
• Generalize only to similar participants and NOT
  to the general population

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Sampling

• Sampling of participants
• Sampling of situations
• Sampling in different situations will help us
  know what situational factors affect behavior

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Sampling

• Sampling of participants
• Sampling of situations
• Sampling of behaviors
• Repeated sampling of behavior in a specific
  situation will indicate the consistency of the
  behavior

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Evaluating the Data

• The data from low-constraint research is a rich
  set of information
• Must be cautious in interpreting data from
  low-constraint research
• Limitations
• Poor representativeness
• Poor replicability
• Ex post facto fallacy
• Limitations of the observer
• Going beyond the data

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Poor Representativeness

• Small samples non-randomly selected
• Rarely do the samples represent the population
• Dangerous to generalize findings to the general
  population

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Poor Replicability

• Studies can be replicated only if
• The procedures are clearly specified
• The procedures were followed exactly
• In low-constraint research
• Procedures are often not specified
• They may change as the study continues
• They are often unique to the observer
• Therefore, replication is very difficult

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Ex Post Facto Fallacy

• Interpreting an observed contingency as if it
  represented a causal connection
• Low-constraint observation will never provide the
  controls for such strong conclusions
• If ex post facto conclusions are interpreted as
  hypotheses for further research, and not as
  established facts, then they serve a useful
  scientific purpose

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Limitations of the Observer

• Low-constraint studies often rely on the
  observational skills of the researcher
• Ex. specificity of observation procedures
• Specific procedures decrease flexibility
• Non-specific procedures increase possibility of
  experimenter biases (experimenter reactivity)

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Going Beyond the Data

• One must be careful in drawing strong conclusions
  (dont over-interpret)
• Interpretation should take into account other
  information we know about a phenomenon