Interpreting Data

6. What about causation in observational studies? Beware the confounding variable!

6.1 Confusion of scales

There is a tendency to treat qualitative variables that are measured using an ordinal scale of measurement as though they are measured on an interval/ratio scale. The reason is that relationships involving them can be examined using correlation coefficients.

An early observational study compared the results from 3 studies to examine the relationship between smoking habits and death rates (Cochrane1968). These data is presented in the scenario below.

Is there a variable that is strongly related to dying?

Confounding variables tend to be more of a problem in observational studies than they are in experimental studies because in experiments it is possible to control for confounding variables. In general, the presence of a confounding variable can lead to misinterpretation when data are aggregated over the different values of the confounding variable.

Sometimes data from several groups, like separate age groups in the previous example, are combined to form a single group as in Figure 6.1.

Figure 6.1

This can happen with categorical and quantitative variables and it can lead to faulty reasoning. For example, the data in Table 6.1 simulates that which may be obtained from a university Admissions Centre (these data are artificial but reflect research in this area (Bickel & O'Connell 1975). The data are of acceptance rates for post-graduate study for males and females.

Table 6.1

If you convert these data to percentages, what do you observe?

Table 6.2

These percentages appear to indicate that the university admits a higher proportion of males than females. This would seem to indicate that females do not get a fair chance when they are seeking admission to the university. However, the university says that its figures do not indicate that it is acting unfairly towards females. Table 6.3 presents the data for enrolments within programs of study.

Note that the university admitted the same proportion of males and females into Science/Engineering and the English/History program. By dividing the data into programs of study within the university, the university was able to show that the Science/Engineering program admitted half of the applicants, both male and female, and English/History program admitted a quarter of both females and males.

This example is a classic case of Simpson's Paradox named after E. H. Simpson who identified this phenomenon in part of a wider discussion of conditional probability. Simpson's paradox occurs when the aggregate affect is in the opposite direction to the parts.

In some states of the USA convicted murderers can be sentenced to death. People have argued that whether a convicted murderer is sentenced to death or not depends on their race. Table 6.4 showed the following results for one study on race and the death sentence (Radelat. 1981).

  Table 6. 4 Defendant's Race and the Death Sentence

Table 6.4 data indicates that 19/160 = 11.9% of white defendants are sentenced to death and 17/166 = 10.2% of black defendants are sentenced to death. From this data, there is little difference between the percentage of black and white defendants being sentenced to death. This seems counter intuitive to what we would expect, knowing a little about race discrimination in the U.S.A. When more data is available, such as the race of the victim, the data appears as in Table 6.5:

Table 6.5 Death Sentence in Reference to Race of Victim

Let's consider the 'sentencing to death' data in Table 5.

First, if the victim is white.

• When a victim is white, a white defendant has a 19/151 = 12.6% chance of being sentenced to death
• When a victim is white, a black defendant has a 11/63 = 17.5% chance of being sentenced to death
• When a victim is white, a white defendant has a 19/151 = 12.6% chance of being sentenced to death
• When a victim is white, a black defendant has a 11/63 = 17.5% chance of being sentenced to death

Second, if the victim is black.

• When a victim is black, a white defendant has a 0/9 = 0% chance of being sentenced to death
• When a victim is black, a black defendant has a 6/97 = 6% chance of being sentenced to death

Clearly the variable “race of victim” has introduced significant differences in the chance of being sentenced to death when compared with Table 4 – and the black defendant has a higher chance of being sentenced. “Race of victim” is a confounding variable.

So there has been a confounding variable at work – namely the race (ethnicity) of the victim.

Tables 6.4 and 6.5 indicate that there existed a confounding variable that was hidden when all crimes for which the death penalty was invoked were combined and then compared against the race of the defendant.

There may be other factors that are instrumental in the sentencing decision. If the circumstances of the crime are taken into account, it is found that black homicides tend to occur during disputes between people who know each other. However, white homicides are more likely to occur when the defendant is committing another type of crime such as robbery. The circumstances are considered 'aggravating' and thus increases the likelihood that the defendant, regardless of race, will be sentenced to death. Although another more recent study (Seligman 1994) has demonstrated that a higher proportion of white defendants is sentenced to death, other researchers (Rothman & Powers 1994) have argued that currently there is no racial bias in the death sentence.

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