1. Ragin’s comparative method

2. Characteristics comparative method 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 -

3. 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)

4. 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 ones; When you are interested in multiple causation; To construct an empirical typology (pp 149-160 of Ragin’s book)

5. 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 values; 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 equation

6. Advantages Ragin’s method Applicable in situations of limited cases; Statements can be made about combinations of causes; The integrity of cases as (unique) combinations of properties is respected; No problems with generalization from sample to population; Gives powerful explanations that cover all cases; Convenient tool for constructing typologies.

7. 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)