CAUSATION

! According to Guppy, causation is:

[a] relationship between two variables in which a change or variation inone variables produces a change or variation in a second variable.

Four cr iteria ares essential to establishing a causal relation between twovariables:

1. Association

2. Time

3. Nonspuriousness

4. Theoretical Rationale

Independent and Dependent Variables

! An independent variable is the cause of a dependent variable.

! A dependent variable is the effect of an independent variable.

! To reiterate, the independent variable is the cause and the dependentvariable is the effect.

! The dependent variable is what you are interested in explaining.

! It is dependent upon the independent variable in that its value changes (atleast in part) as a function of changes in the independent variable.

! For instance, if you hypothesize that:

H1: Income is positively associated with level of formal education.

Level of formal education is the independent variable and income is thedependent variable.

The above might be restated as follows:

The greater the number of years of formal education one has, the highertheir annual income.

! It should be noted that any given variable might be treated as independentin one part of the analysis and dependent in another part of the analysis.

For Example, for Marx:

Economic Religious &Structures -----> Ideological

Structures

For Weber:

Religious &Ideological -----> EconomicStructures Structures

For Marx the economy is an independent variable, and religion a dependentvariable.

For Weber religion is an independent variable, and the economy an independentvariable.

Weber clarified such relationships further by stating that it is perfectly legitimateto explore a given variable as an independent variable in one analysis, and adependent variable in another.

In so doing with regard to the connection between religion and the economy, hewas said to be rounding out the work of Marx.

! I would note, that such relationships have to make "causal sense".

For example age might affect level of psychological development, but levelof psychological development can not affect age.

Necessary and Sufficient Causes

! With probabilistic model of the sort we usually consider within sociology,it is useful to distinguish between two types of causes:

necessary causes and sufficient causes.

! A necessary cause represents a condition that must be present for theeffect to follow.

For example, it is necessary for a person to be a women to becomepregnant, even though not all women do become pregnant.

! A sufficient cause represents a condition which, if it is present, inevitablyresults in the effect. Enlisting in the army is a sufficient cause for beingissued a uniform, even though there are other ways of acquiring uniforms.

! For any given phenomenon we seek to explain, we ideally hope to discovera cause that is both necessary and sufficient. Unfortunately, we rarelydiscover causes that are both necessary and sufficient. In fact, as Guppynotes, multiple causes are the rule in most social scientific explanation.

THIRD VARIABLES AND CAUSAL RELATIONS

! Explanations which are confined to only two variables tend to be overlysimplistic.

! Sociologists must almost always consider one or more additional thirdvariables.

! How a third variable is incorporated into an analysis depends on how thehypothesis relates it to the independent variable.

Intervening Variables:

! An intervening variable is one that occurs in time between the occurrenceof the independent variable and the occurrence of the dependentvariable.

Antecedent Variables:

! An antecedent variable is one that occurs earlier in time than either theindependent or the dependent variable.

** SEE EDUCATION --> OCCUPATIONAL STATUS EXAMPLE

Spurious Relationships:

! A spurious relationship is an originally observed association between two variablesthat disappears when a third variable is introduced into the analysis.

In such cases the third variable must be antecedent to the fir st two, and cause them.

For example:

! storks and births (urban vs. rural settings)

! tigers and crimes (city size)

! religious attendance and shoe size (gender)

! number of fire trucks and amount of damage caused by a fire

CONTROLLING FOR THIRD VARIABLES

! In developing explanations that attempt to incorporate multiple causal factors, we maystart off by examining the relationship between two variables (ideally an independentand dependent variable), but then we try to "control" (or hold constant) important thirdvariables (antecedent or intervening).

! For example, lets say we are interested in gender inequality, or more precisely, therelationship between gender and income.

According to available statistics, if we look at all men and women in the paid labourforce in 1982, women earned about 50% of what men earned.

If we control by full-time vs part-time employment, and look only at those employedfull-time then women earned about 64% of what their male counterparts earned.

If we control for amount of formal education, this differences decreases again.

In this case, we can also control for other important variables such as ethnicity/race,type of job, years of experience, etc.

While research as show that there still tend to be a gap between the earnings of men andwomen that indicates discrimination against women, this gap is exaggerated if we onlymake gross comparisons -- e.g., between all men and all women in the paid labourforce.