Ragin's comparative method by wuyunyi


									Ragin’s comparative method
      Characteristics comparative
• Combinations of conditions are attributed causal value
• Cases are studied as unique combinations of conditions
  and are thus left intact
• Explanations are absolute in that they cover all instances
  of a phenomenon (no exceptions)
• Examines all the cases of a population (does not
  generalize from sample to population)
• Makes use of categorical variables with two values:
  present or absent; high or low; + or -
  Characteristics statistical method
• Analytical (effect of each individual variable is
  determined independently of other variables)
• Cases are no more than the bearers of variables
• Generalises from sample to population
• Explanations are not absolute but probabilistic (i.e.
  outliers and exceptions are accepted as long as there
  are not too many)
• Frequency is important (an explanation is stronger the
  more instances it covers)
• Intensity of phenomenon is taken into account (ordinal
  and continuous variables)
   When is Ragin’s comparative
   method a suitable approach?
• If explanatory unit of research is at a group level
  (school, municipality, region, country);
• If there are few units (e.g. less than 20);
• If the response variable is categorical with binary
  values (or can easily be turned into it);
• If most of the explanatory variables presumed
  important are binary or can be turned into binary
• When you are interested in multiple causation;
• To construct an empirical typology (pp 149-160
  of Ragin’s book)
 Ragin’s comparative method: how
           does it work?
A step by step approach:
• Select cases and variables relevant to research interest
  and hypotheses
• Turn selected variables into binary variables and define
• Assign present and absent values to each case on these
  variables by using upper and lower case letters
• Compile these values in a data matrix;
• Transform the data matrix into a truth table (p. 88 of
  Ragin’s book) A truth table lists all the logically different
  combinations of values of the independent variables
  found in the sample
• Use Boolean algebra to arrive at ‘primitive’ causal
   Advantages Ragin’s method
• Applicable in situations of limited cases;
• Statements can be made about combinations of
• The integrity of cases as (unique) combinations
  of properties is respected;
• No problems with generalization from sample to
• Gives powerful explanations that cover all cases;
• Convenient tool for constructing typologies.
 Disadvantages Ragin’s method
• Does not distinguish between lower and higher
  level variables
• Difficult to transform higher level variables into
  binary ones. Problems:
   – distance between original values
   – variables with a normal distribution
• Frequency not taken into account in assessing
  strength of explanations
• Strict explanations covering all instances may
  contain so many combinations that interpretation
  becomes difficult (i.e. transparency and
  parsimony suffer)

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