Spurious Relationship - SAGE Research Methods
In looking at how sociologists try to establish causal relationships among variables, we took To explore spurious and intervening relationships among nominal or ordinal variables, People with upper class parents . Examples using SPSS. Dictionary of Statistics and Methodology: A Nontechnical Guide for the Social "( b) A spurious correlation, as defined in definition a, is sometimes called an. This PsycholoGenie article explains spurious correlation with examples. statistics, psychology, sociology, etc., correlation is very important in order to.
A college student notices that on the days she sleeps in and skips her early classes there are a larger amount of traffic accidents on and around campus. The spurious correlation in this example is that she thinks her sleeping in means that more accidents will occur.
In fact, what is actually happening is the reason she is staying in those days is because of bad weather lurking variableand bad weather tends to cause traffic accidents. Significance in Sociology In the field of sociology, the main idea illustrated by spurious correlations is the concept of correlation vs. In both sociology and statistics, correlation and causation are determined by performing experiments.
- Spurious Correlation Explained With Examples
If a researcher notices a relationship between two variables and wants to find out if the connection is spurious or not, he may conduct an experiment and control for other factors. A big social issue that seems to pop up in discussions fairly regularly is the death penalty, specifically, does the death penalty curb violent crime. The idea that someone will not commit a crime out of fear of being convicted then sentenced to death has been considered a spurious correlation by those that rally against the death penalty.
They sometimes claim that because the death penalty is enacted so seldom that whatever year-to-year changes in violent crimes committed cannot be linked to fear of the death penalty, hence it is does not do what it sets out to accomplish.
In both an academic environment and in an everyday situation, an assumption may present itself and must be reasoned with to a degree and make the person think hard about what they are dealing with. For instance, an employer at a restaurant may tell a worker that business is slow on the days that particular worker is there.
However, many spurious correlations do not seem absurd and some seem compelling. Other Descriptions and Examples The spurious-correlation fallacy is not widely recognized by most people. Its occurrence is pervasive, but it is generally unnoticed. Part of the problem is the wide variation in terminology that is used by different authors. The purpose of this paper is to provide a set of examples, which illustrate the various ways that the fallacy can be described and discussed.
The goal is to use repetition to help develop a "feel" for the pattern so that recognition becomes easier. Because the examples are highly redundant, once the pattern is clearly understood, reading the entire paper may be unnecessary.
The following excerpts are from a dictionary, two books devoted entirely to mathematical and statistical fallacies, an elementary statistics textbook, and a book on statistical methodology. They all describe the same phenomenon in somewhat different terminology. Dictionary of Statistics and Methodology: When the effects of the third variable are removed, they are said to have been partialed out.
See confound, lurking variable. For example, a if the students in a psychology class who had long hair got higher scores on the midterm than those who had short hair, there would be a correlation between hair length and test scores. Not many people, however, would believe that there was a causal link and that, for example, students who wished to improve their grades should let their hair grow.Social Stratification: Crash Course Sociology #21
The real cause might be gender: Or that might be a spurious relationship too. The real cause might be class rank: Seniors did better on the test than sophomores and juniors, and, in this class, the women who also had longer hair were mostly seniors, whereas the men with shorter hair were mostly sophomores and juniors.
A third variable that causes a correlation between two others - sometimes, like the troll under the bridge, an unpleasant surprise when discovered.
A lurking variable is a source of a spurious correlation. Compare covariate, latent variable, moderator variable. For example, if researchers found a correlation between individuals' college grades and their income later in life, they might wonder whether doing well in school increased income. It might; but good grades and high income could both be caused by a third lurking or hidden variable such as tendency to work hard.
It turned out that they did.
Spurious Correlation: Skirt Lengths and Stock Markets
This pleased a good many people and they have been making much of it ever since. The road to good grades, it would appear, lies in giving up smoking; and, to carry the conclusion one reasonable step further, smoking makes dull minds.
This particular study was, I believe, properly done: The fallacy is an ancient one which, however, has a powerful tendency to crop up in statistical material, where it is disguised by a welter of impressive figures. It is the one that says that if B follows A, then A has caused B. An unwarranted assumption is being made that since smoking and low grades go together, smoking causes low grades. Couldn't it just as well be the other way around? Perhaps low marks drive students not to drink but tobacco.
When it comes right down to it, this conclusion is about as likely as the other and just as well supported by the evidence. But it is not nearly so satisfactory to propagandists. It seems a good deal more probable, however, that neither of these things has produced the other, but both are a product of some third factor. Can it be that the sociable sort of fellow who takes his books less than seriously is also likely to smoke more?
Or is there a clue in the fact that somebody once established a correlation between extroversion and low grades - a closer relationship apparently than the one between grades and intelligence? Maybe extroverts smoke more than introverts. The point is that when there are many reasonable explanations you are hardly entitled to pick one that suits your taste and insist on it. But many people do. To avoid falling for the post hoc fallacy and thus wind up believing many things that are not so, you need to put any statement of relationship through a sharp inspection.
The correlation, that convincingly precise figure that seems to prove that something is because of something, can actually be any of several types. A good deal of dirty work has been done with this one.
The poor grades among cigarette smokers is in this category, as are all too many medical statistics that are quoted without the qualification that although the relationship has been shown to be real, the cause-and-effect nature of it is only a matter of speculation. As an instance of the nonsense or spurious correlation that is a real statistical fact, someone has gleefully pointed to this: There is a close relationship between the salaries of Presbyterian ministers in Massachusetts and the price of rum in Havana.
Which is the cause and which the effect? In other words, are the ministers benefiting from the rum trade or supporting it? That's so farfetched that it is ridiculous at a glance. But watch out for other applications of post hoc logic that differ from this one only in being more subtle. In the case of the ministers and the rum it is easy to see that both figures are growing because of the influence of a third factor: Walker has worked out an amusing illustration of the folly in assuming there must be cause and effect whenever two things vary together.
In investigating the relationship between age and some physical characteristics of women, begin by measuring the angle of the feet in walking. You will find that the angle tends to be greater among older women.
You might first consider whether this indicates that women grow older because they toe out, and you can see immediately that this is ridiculous. So it appears that age increases the angle between the feet, and-most women must come to toe out more as they grow older.
Spurious relationship - Wikipedia
Any such conclusion is probably false and certainly unwarranted. You could only reach it legitimately by studying the same women - or possibly equivalent groups - over a period of time. That would eliminate the factor responsible here. Which is that the older women grew up at a time when a young lady was taught to toe out in walking, while the members of the younger group were learning posture in a day when that was discouraged.
And it is often more seriously misleading. It is rather like the conviction among the people of the New Hebrides that body lice produce good health. Observation over the centuries had taught them that people in good health usually had lice and sick people very often did not.
The observation itself was accurate and sound, as observations made informally over the years surprisingly often are. Not so much can be said for the conclusion to which these primitive people came from their evidence: Lice make a man healthy.
Everybody should have them.
More sophisticated observers finally got things straightened out in the New Hebrides. As it turned out, almost everybody in those circles had lice most of the time.
It was, you might say, the normal condition of man. When, however' anyone took a fever quite possibly carried to him by those same lice and his body became too hot for comfortable habitation, the lice left. There you have cause and effect altogether confusingly distorted, reversed, and intermingled.