Chapter 3

For example, we know that the powers of incumbency pose great difficulty to anyone who wishes to deny an incumbent her or his party's re-nomination‑‑if the incumbent desires re-nomination. So if you have event A, that is, an incumbent that desires re-nomination, you can usually predict that she or he will win it. Such predictions held for President Gerald Ford in 1976, President Jimmy Carter in 1980, George H.W. Bush in 1992, Bill Clinton in 1996, and George W. Bush in 2004. All except Clinton and George W. had serious opposition from within their own political party (Ronald Reagan and Ted Kennedy and Pat Buchanan respectively). (Ironically, the Democrats defeat in the 1994 congressional elections helped Clinton in that no other Democrat thought the nomination in '96 would be worth having since the Republicans would almost surely win. By late 1995 when Clinton's chances improved dramatically, it was too late to get a viable campaign started.) However, this explanation/prediction may not always hold because the relationship may be changing or because other related factors (that perhaps we don't fully know about in our theory) are not the same as before. For example, as a political party becomes weaker in terms of both its organization and its place in the minds of average citizens, the ability of the president to use his powers as head of his party to guarantee his re-nomination may be weakened.

The way the media describes presidential elections is another good example. Elections are frequently seen in terms of their similarity to past elections. Yet no two elections are ever exactly the same. Was the election of 1992 (incumbent Bush versus Clinton) closest to 1948 when an embattled incumbent (Harry Truman) ran against what he called a "do nothing" Congress? Was it closer to 1960 when a new generation of leaders with new ideas wanted to replace an older generation? Was it closer to 1976 when voters were clearly dissatisfied with the incumbent but were a little afraid of the inexperienced challenger? Was it like the 1980 election in that the economy was the deciding factor? 1992 did share something in common with all of them. Which fit best? Choosing the best fit and best explanation is much easier once the election is over (though it is still not a simple matter).

However, trying to predict which explanation will fit for an upcoming election is much harder. After the Gulf War of 1990, who would have even predicted any explanation that pointed toward George H.W. Bush's defeat? Those who predicted in 1995 that an unpopular Bill Clinton would follow George Bush's steps as a one-term president wished to take their prediction back a few months later. Clinton won easily. On the other hand, the unhappiness of the nation with the W. Bush presidency did make the prediction of Republican defeat in 2008 fairly easy. The winner was going to be the candidate least like W. Bush, and despite gallant efforts from John McCain to present himself as a different kind of Republican, a “maverick,” Obama was less like Bush and more clearly associated with what the nation wanted—change. In the 2010 Congressional elections one could safely predict that the Democrats would lose seats because the party that picks up seats and the White House almost always loses in the next non-presidential election, especially if the economy is doing poorly. But how many seats would the Democrats lose? Would it be more like 1994 when Republicans took over both houses in what was called a "Republican tsunami?" Of would it be closer to the usual number so that Democrats maintained control of both houses, but by much closer margins? That was a much more difficult prediction, depending on last minute events and on some "fortuna" and on the qualities and skills of particular candidates in their campaigns. It turned out to be more than a tsunami in the House but the Democrats barely held the Senate. 2012 would normally look very bad for an incumbent president with an unemployment rate over 9% and a stagnant economy. But other factors like a possibly weak Republican nominee and a nation that is angry with Republican refusals to compromise on issues could help the incumbent to hang on. With a lot of variables and many unknowns, a prediction is hard--it could go either way.

D. Relationships between explanation and prediction

This simply refers to whatever the individual "things" are that we are studying. In political science the "things" are usually people. However, we might be studying constitutions, as Aristotle did. Or we might be studying and classifying elections, as was done in critical election theory (remember?). Or we might be looking at nations as does balance of power theory.

B. Properties and variables

Whatever kind of units we are looking at, we observe them in order to take measurements of some property that each unit has. Elections have winners, parties, and money spent. Constitutions have powers assigned, prohibitions, and rights distributed. Nations have resources, powers, demographic (that means characteristics of the population like age, race, religion, wealth, and so on), economic, and geographic properties. In most cases, these properties vary. By definition, properties that vary are called variables. If a property does not vary, we then call it a constant.

C. Possible relationships among variables

Variables can be related to each other in many ways. Obviously, when more than a few variables are involved, the patterns of relationship can become quite complex. What I want to do here is talk about some of the most simple relationships involving only 2 or 3 variables.

1. Causal

This is what we would always like to find, some variable that causes change in some other variable as it changes in some specified way. If the change in the first variable is enough by itself to cause the change in the second variable, we can say that this variable is sufficient to cause a change in the second variable. One thing that social scientists look for as evidence of a causal relationship is the time relationship between the changes that occur in the variables. If the change in the second one consistently follows the change in the first, you have pretty good evidence for a causal relationship.

A useful way to talk about these relationships is to use diagrams, sometimes called "arrow diagrams," or "path diagrams." A causal relationship where a change in variable A results in a change in variable B is shown below.

A ‑‑‑‑‑‑‑‑‑> B

We use some special names for the roles each of these variables play in this relationship. A is called the independent variable, and B is called the dependent variable. One way to think about and remember this is to say that the dependent variable depends on the independent variable in the relationship. Look at the arrow diagram and say this a couple of times.

Many examples of this simple yet most important relationship exist. Let's look at one that is of political significance in the South and in South Carolina in particular. It is one that has great implications for social and medical policy in the region. It involves the question of infant mortality (measured by deaths per 1,000 births before the age of 1 year). The South has long had the highest rates of infant mortality in the nation, and South Carolina has about the highest of any state. Why? To answer that question we must look for causal variables. One that medical science has well established as a causal factor is birth weight. The relationship can be described as follows: the lower the birth weight, the more likely an infant is to die. In arrow diagram form:

Birth Weight ‑‑‑‑‑‑‑‑‑‑‑> Infant Mortality

Let's make the picture a little more complicated and add a third variable that also has a causal relationship with the other two. Suppose the arrow diagram looks as follows:

A ‑‑‑‑‑‑‑‑‑> B ‑‑‑‑‑‑‑‑‑> C

Now we have two causal relationships, one between A and B, and a second one between B and C. In a sense, there is also a third one between A and C, but it is mediated by variable B. In this case, variable B is called an intervening variable. In order to specify the role that each variable plays, we must talk about the role it plays in relation to some other variable or variables. For example, A is an independent variable with respect to B, and B is an independent variable with respect to C, but with respect to A and C both, B is an intervening variable. Got it?

Let's apply this terminology to our infant mortality example and expand it a bit. So low birth weight causes high infant mortality‑‑so what? If we think about what causes low birth weight, we begin to see the public policy implications. One of the principal causes of low birth weight is poor nutrition, and one of the principal causes of poor nutrition is poverty. I've added two intervening variables and now you see the southern connection. Here's what we have in arrow diagram form.

poverty ‑‑> nutrition ‑‑> birth weight ‑‑> infant mortality

A good exercise at this point would be for you to describe the roles that each of these variables play with respect to the other variables. Try it!

Although we may say a relationship is causal, in reality what usually happens in the complex world of human behavior is much weaker than that. The independent variable usually does not cause a change in the dependent variable, but merely makes some change more likely to happen. We might say that many of our causal relationships are really about variables that make something more likely to happen, but are neither necessary nor sufficient in making it happen. For example, low education contributes to poverty by making poverty more likely. But low education is neither necessary nor sufficient in creating poverty. And, until we get to a certain level of really low poverty, poverty only contributes to poor nutrition. In political science we are almost always really talking about how much some variable A contributes to some change in variable B. Rarely does a change in A always cause B to change in a specified way.

2. Conditioning

Now we will start making things a little more complicated. Sometimes a third variable weakens or strengthens a relationship. Then we say that a variable plays a conditioning role in the relationship, or to put it another way, the relationship only exists (or is more likely to exist) under certain conditions.

To use our example about infant mortality, we might say that knowledge about good nutrition will not by itself cause a person to have good nutrition. Nutritional knowledge is more likely to lead to good nutrition under the condition of financial means. So financial means conditions the relationship between nutritional knowledge and the actual practice of good nutrition.

Knowing the conditioning variables is politically important. How so? If we want to do something about infant mortality, for example, we might want to create programs and/or policies that create the right conditions for decreasing infant mortality or increasing good nutritional practices, which we know helps reduce infant mortality.

In path diagram form, we have a relationship between A and B that is affected, or conditioned, by the value of a third variable C. So in terms of our example, A is nutritional knowledge and B is the practice of nutrition, and C, the conditioning variable, is financial means. You must have the money to buy and consume the things you know are good for you. And just in case you did not know this, it turns out that better food generally costs more, so knowledge by itself is not really enough!

A --------------------------------> B

3. Reciprocal

An easy way to think about a reciprocal relationship is to think of it as a two-directional causal relationship. Each variable simultaneously plays the role of both independent and dependent variable. Each reinforces the other. Unlike a simple causal relationship, no clear indication tells us which variable changes first in time. Either variable could change first, or the changes may be so close in time that telling which came first is impossible to tell. You might think of this as a kind of "chicken and egg" situation. A reciprocal relationship is shown in an arrow diagram as follows.

A <‑‑‑‑‑‑‑‑> B

Although making the example of infant mortality illustrate this kind of relationship is a bit more difficult, we can make it fit if we stretch things a bit. Low concern over nutrition tends to reduce nutrition. However, we might also make an equally strong argument that poor nutrition leads one to be less concerned about good nutrition. To the extent that poor general health causes low motivation in all areas of life, poor nutrition may cause low nutritional motivation. Which comes first? Logically, either could happen first.

4. Symmetrical

This is a rather simple situation where one variable simultaneously causes changes in two other variables. Or you might say that we have one independent variable and two dependent variables. The arrow diagram is as follows.

The significance of this simple relationship is in our next type of relationship, or to be more precise, "non-relationship."

5. Spurious

This is an apparent, yet false causal relationship that is the result of some unknown third variable having a symmetrical relationship with the first two variables. Therefore, a spurious relationship is an untrue relationship. This could be diagramed in an arrow diagram as shown below.

This poses a great problem for researchers. Every causal relationship is potentially spurious. We never know for sure until we have tested all possible third variables. The process of doing this is called controlling for third variables. The fact that we can never control for all third variables along with the fact that we may someday find some third variable that renders what we thought to be a causal relationship to be spurious are additional reasons why scientific explanations are open and always subject to change.

We need to add some other terminology here that is often used. When a third variable C causes a bivariate causal relationship between A and B to disappear, that is, renders it spurious, we say that the third variable has a confounding effect or is a confounding variable.

For example, medical researchers noticed that people with very low levels of cholesterol have higher death rates. They wondered what was going on, because low cholesterol is supposed to be good.

cholesterol (low) --------------> death rate (high)

As good researchers, they looked at things that could cause this relationship to be spurious. Pretty quickly they found some good candidates for that third variable: smoking and alcohol. High levels of alcohol consumption and heavy smoking depress the appetite and cause cholesterol to be low. Simultaneously, these activities contribute to higher death rates.

6. Controlling for a Third Variable: How to do It

When we are trying to see if a relationship is spurious or if a third variable conditions a relationship, we control for whatever third variables we think might be related to both the independent and dependent variables. We do this by reexamining the relationship for each value of the control variable. Using the example above about cholesterol and death rates, we would control for smoking by looking at the relationship for smokers and then look again for non-smokers. If the relationship is spurious, then the relationship between cholesterol and death rate would disappear when looking at each group alone. If conditioning were taking place, we would see a different relationship between cholesterol and smoking for smokers than for non-smokers (which is not the case).

It turns out that the test for spuriousness is the very same test for seeing if a third variable intervenes between an independent and dependent variable. When you control for the possible intervening variable, the relationship also disappears. So the statistics cannot tell us whether a relationship is about spuriousness or involves an intervening variable. We can only tell the difference from theory, from whether we have reasons for the third variable to play a role between the independent and dependent or whether it should have an effect on both.

Let's look back at our example of infant mortality. Here is where it gets complicated and highly controversial. Researchers have long noted a relationship between race and infant mortality. Black mothers are more likely to have underweight infants than are white mothers. Most observers regarded this apparent relationship between race and birth weight as caused by poverty, where poverty plays in intervening role and wipes out any direct relationship. Here is what that relationship would look like in path diagram form. You will note that I drew this path diagram so that it looks like a spurious relationship except for one thing. Can you see what it is? The arrow between race and poverty is in the other direction, so it is really the diagram for an intervening relationship. Logically, it could not be in the other direction unless somehow a change in poverty could cause a change in race! But if poverty does really intervene and the relationship between face and birth weight does disappear when we look at similar economic groups, we can still conclude that race has nothing to do with birth weight directly.

Well that is what we thought would happen. But it did not exactly turn out that way!

A number of researchers have argued that the relationship between race and birth weight is real. They have argued that some unknown genetic difference between blacks and whites makes blacks more likely to have offspring with lower birth weights even after you account for the impact of poverty. They have looked at the relationship between race and birth weight while controlling for a number of third variables that approximate poverty. For example, they have found that white mothers with low education have larger infants than black mothers with low education. At the other extreme, they have found that black mothers with high education have smaller infants than white mothers with high education. Other researchers have compared infant mortality rates of blacks in South Carolina with blacks elsewhere and found no significant difference. They also found that South Carolina whites are also not significantly different than whites elsewhere. Looking at these facts together, they have tentatively concluded that the major reason for higher infant mortality rates in southern states is NOT poverty, but the simple fact that southern states have higher percentages of blacks in their populations. Mississippi and South Carolina, the two states with highest percentage of blacks in their populations, should therefore be expected to have the highest infant mortality rates. Indeed, they consistently have.

Now this is highly controversial for a number of reasons. The first is methodological. Critics of this research argue that insufficient data on mothers are available to fully measure the concept of poverty. Single variables like education only partially capture the concept. To use the terminology we have been using, they are saying that this new research is faulty at the "operationalization" stage, that the measures used are not valid.

Recent research suggests that the explanation may lie in the time between births, another variable. If for cultural reasons blacks have births closer together than other racial groups, that could be the intervening variable. The answer is still not certain--science moves on. Until better data are available on mothers, we have no good reason to think racial genetic differences exist.

The second reason that this is controversial is that it has enormous public policy consequences. If science tells policy makers that they can do little about infant mortality, then the research may provide policy makers with an excuse to reduce their efforts to combat infant mortality. Policy makers may conclude that health and educational programs for pregnant women are a waste of valuable resources. Again, we see that research has great political implications, regardless of the avowed neutrality of those doing the research.

The third reason for controversy is that even if in fact real racial differences exist, someone (probably many someones) will put value connotations on the results. Whites who are racists will presume that this is proof of what they have been sure of all along: intrinsic white superiority. Some blacks will react to these outrageous and unwarranted conclusions and argue that the research is a sign of white racism in the scientific establishment.

D. Measurement