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This chapter explores the concept of causation in social research. It discusses determinism, the causal model, idiographic and nomothetic models of explanation, criteria of causality, and different types of causal relationships. It also addresses the limitations and challenges in establishing causal relationships in social science research.
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Scientific explanations rest on the idea that events and conditions have causes.
Determinism: events are determined, or caused to happen; they are not free to happen any other way.
We recognize that our free will is limited by certain constraints.
Eg. When we look for the causes of prejudice, we look for the reasons, or factors, that make some people prejudiced and others not. • Research shows that education tends to reduce prejudice • This would be a causal explanation.
The ultimate implication is that our attitudes and behaviours can be traced back through a long and complex chain of reasons that explain why we turned out the way we have.
What is not part of the model: • social scientists do not have to believe that all human actions, thoughts, and feelings are determined, nor do they lead their lives as though they believed it
What is not part of the model: • the deterministic model does not assume that causal patterns are simple
What is not part of the model: • does not suggest that we now know all the answers about what causes what or that we ever will – much useful research is designed to reveal associations, or relationships
What is not part of the model: • social science operates on the basis of a causal model that is probabilistic – likelihood that if certain factors exist, a more or less likely relationship
Social scientists don’t need to believe that all aspects of human life are totally determined, but they do need to be willing to use deterministic logic in seeking explanations for the phenomena that interest them
Causation in Idiographic and Nomothetic Models of Explanation
Idiographic model of explanation • aims at an explanation through the enumeration of the many reasons that lie behind a particular event or action • Looking at all the reasons that cause an event or action
Nomothetic model of explanation • Does not involve enumerating all the consideration that result in a particular action or event – it is designed to discover those consideration that are most important in explaining general classes of actions or events
Nomothetic model of explanation • Eg. voting behaviour • The nomothetic model of explanation involves the isolation of those relatively few considerations that will provide a partial explanation for the voting behaviour of many or all people.
Nomothetic model of explanation • Use phrases like: leads to; arise from; predictor of – is a signal of causal explanations.
Nomothetic model of explanation • In the nomothetic case, eg. prejudice, they look for actors that affect levels of prejudice in general – eg. Those with more education are generally less prejudiced than are the less well educated
Nomothetic model of explanation • The criticism: dehumanizing the people they study – this charge is lodged specifically against the nomothetic model of explanation
Nomothetic model of explanation • The nomothetic explanation is more deterministic
Criteria of Causality • The main criteria for judging the validity of an explanation are • 1) its credibility for believability – logic of explanation • 2) whether alternative explanations (rival hypotheses) were seriously considered and found wanting
A requirement of a causal relationship: cause comes before effect • - This can be unclear eg. What comes first education or prejudice
Correlation • In a causal relationship the two variables need to be empirically correlated. • A correlation exists between variables when they are observed to be related – that is, when one occurs or changes, so does the other
A perfect correlation – as one variable take on different values so does some other variable
In a causal relationship, the observed empirical correlation between two variables cannot be explained in terms of some third variable – a spurious relationship – in reality a third variable explains the observed association
However, association in itself does not tell us if it is a causal relationship.
Inverse relationship • as one variable increases the other decreases • eg. As education increases this causes a decrease in prejudice
Direct or Positive relationship • as one variable increases as does the other • eg. As education increases this causes an increase in prejudice
Direction cannot be determined with categorical variables. That is, the variables, when they are set up as categories, cannot show direction.
Necessary Cause • represents a condition that must be present for the effect to follow • Eg. It is necessary for you to take university courses in order to get a degree but you need to take the right ones and pass them
Sufficient Cause • represents a condition that if it is present guarantees the effect in question
Eg. It would be nice to discover a single condition that • 1) must be present for delinquency to develop • 2) always resulted in delinquency
Provincialism • that researchers will interpret people’s behaviour in ways that make sense from the researcher’s own points of view – the key is to be conscious of our particular views and remain open to broadening our perspectives by recognizing that others’ views may be equally valid
Hasty conclusion • be sure to evaluate the weight of evidence leading to that interpretation
Questionable cause • whenever it seems that X caused Y, ask yourself if that is necessarily the case – eliminate other possible explanations
Suppressed Evidence • researcher will dismiss information that is not relevant
False dilemma • is a choice that seems to be forced but really is not. • Eg. Economics, not biology, may explain male domination – what about politics, education, custom, religion