The Idea of Causation

1. The Idea of Causation

2. “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.

3. 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.

4. 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 people’s attitudes and motivations. • There are often much deeper psychological considerations, that even the respondent may not be aware of.

5. 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.

6. 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.

7. 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

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

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

14. 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.

15. 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

16. 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.

17. 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.

18. 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 conclusionsare 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 suppressother 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.