The Idea of Causation

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The Idea of Causation
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Causal Research

• If the objective is to determine which variable
  might be causing a certain behavior (whether
  there is a cause and effect relationship between
  variables) causal research must be undertaken.

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Causation

• We are always coming up with explanations for why
  things happen why we got into a particular
  school and not into another, why people treat us
  the way they do, why we gain or lose weight, why
  we oversleep.
• When we take the idea of causation seriously,
  however, it becomes complex and even
  threatening. 
• Notions of cause and effect that make easy sense
  in the materialistic realm of the natural
  sciences become more unsettling when applied to
  human beings.

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• In order to determine causality, it is important
  to hold the variable that is assumed to cause the
  change in the other variable(s) constant and then
  measure the changes in the other variable(s).
• This type of research is very complex and the
  researcher can never be completely certain that
  there are not other factors influencing the
  causal relationship, especially when dealing with
  peoples attitudes and motivations.
• There are often much deeper psychological
  considerations, that even the respondent may not
  be aware of.

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Deterministic View

• To say that X is caused by Y is to say that once
  Y has happened, X will follow.  X has no say in
  the matter, no choice. 
• This deterministic view, when applied to human
  behavior, flies in the face of our implicit
  notions of free will.

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• Although we are threatened by the idea that we
  have no free will, we also deny our ability to
  choose every day in the things we say.  "I could
  never date a boy who smoked."  "I couldn't tell
  my mother that."  We seldom say, "I choose not to
  do that." 
• Our reasonableness gets us into trouble in this
  realm.  When you give the reasons for some
  action, those reasons become the cause, not your
  personal choice.

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Models of Explanation

• Idiographic model aims at a complete
  understanding of a particular phenomenon, using
  all relevant causal factors
• enumerates detailed/unique factors that lie
  behind some action or social fact
• Nomothetic model aims at a general understanding
  of a class of phenomenon, using the smallest
  number of most relevant causal factors. The
  nomothetic model is probabilistic in its approach
  to causation. It is the model typically used in
  social scientific research.
• isolates the few key characteristics uniting
  similar cases

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An indiographic explanation

• Taking this research methods course
• If you reflect on it, there are probably a number
  of reasons for why you are taking this course. 
  Some reasons may be high-minded, such as your
  desire to learn research methods that will help
  you save the world and/or your interest in
  exploring pedagogical methods.  Some reasons may
  be more mundane the course is required, it fit
  into your schedule. 
• If you were to write down thirty or forty of the
  most important reasons why you are taking this
  course, we would feel we fully understood why you
  are doing it.

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A nomothetic explanation

• If you think about it, however, there are
  probably a few factors that would figure into the
  decisions of most people who are taking the
  course.
• A nomothetic explanation offers a partial
  explanation for the behaviors of many people.
• Such explanations are not limited to a single
  case, as idiographic explanations are they
  provide a more generally applicable explanation.
• The trade-off is that nomothetic explanations are
  probabilistic, not certain or complete.

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Correlation

• Relates to closeness, implying a relationship
  between objects, people events, etc.
• For example, people often believe there are more
  bizarre behaviors exhibited when the moon is
  full.

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Correlation Statistically Speaking

• Correlation is a measure of association that
  tests whether a relationship exists between two
  variables.
• It indicates both the strength of the association
  and its direction. The Pearson product-moment
  correlation coefficient, written as r, can
  describe a linear relationship between two
  variables.
• The value of r can range from 0.0, indicating no
  relationship between the two variables, to
  positive or negative 1.0, indicating a strong
  linear relationship between the two variables.

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Establishing Causality

• To establish whether two variables are causally
  related, that is, whether a change in the
  independent variable X results in a change in the
  dependent variable Y, you must establish
• 1) Time order The cause must have occurred
  before the effect
• 2) Co-variation (statistical association)
  Changes in the value of the independent variable
  must be accompanied by changes in the value of
  the dependent variable
• 3) Rationale There must be a logical and
  compelling explanation for why these two
  variables are related
• 4) Non-spuriousness It must be established that
  the independent variable X, and only X, was the
  cause of changes in the dependent variable Y
  rival explanations must be ruled out.

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Establishing Causality

• Note that it is never possible to prove
  causality, but only to show to what degree it is
  probable.

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Correlation Does Not Imply Causation

• There is a statistical correlation over months of
  the year between ice cream consumption and the
  number of assaults. Does this mean ice cream
  manufacturers are responsible for crime?
• No! The correlations occurs statistically because
  the hot temperatures of summer cause both ice
  cream consumption and assaults to increase.
• Thus, correlation does NOT imply causation. Other
  factors besides cause and effect can create an
  observed correlation.

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Necessary versus Sufficient

• A necessary cause represents a condition that
  must be present for the effect to follow
• A sufficient cause represents a condition that
  will pretty much guarantee the effect
• In social science, evidence of either a necessary
  or sufficient cause is often used as the basis
  for concluding that a relationship is causal

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Examples

• Being female is a necessary condition of being
  pregnant. Being female is not a sufficient
  condition, since you can be female without being
  pregnant.
• Being convicted of a crime is a sufficient cause
  of being judged guilty.  It's not a necessary
  cause, however, since you could be judged guilty
  as a result of confessing to the crime.

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• There are seldom absolute cases of either
  necessary or sufficient causes and never cases of
  both.
•  
• Having sexual intercourse, for example, would be
  called a necessary cause of being pregnant.  It's
  not absolute, since you could get pregnant via
  artificial insemination.  Since the great
  majority of pregnancies result from intercourse,
  however, it makes sense to call it a necessary
  cause.  It's clearly not a sufficient cause,
  since most of the time, intercourse doesn't
  result in pregnancy.

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Errors In Reasoning

• Provincialism means that all of us seek to
  understand in the terms of our past experiences
  and culture. 
• What makes sense to us may not make sense to
  others.
• Hasty conclusions are the explanations we settle
  on to resolve an issue quickly.
• may make sense, but other explanations we didn't
  think of might make better sense
• Similarly, what first seems like a logical cause,
  may become questionable upon later
  consideration. 
• Other evidence may make it unlikely.
• Once we have an initial conclusion about the
  important causes, we may ignore or suppress other
  information as irrelevant
• Could be missing the most important cause
• Sometimes we fall under the spell of a false
  dilemma in deciding which of two possibilities is
  the true cause. 
• Maybe both are causes.