Validity of data cause and effect relationship examples

Guide 3: Reliability, Validity, Causality, and Experiments

validity of data cause and effect relationship examples

A cause-effect relationship is often assumed, but in reality the For example, the decision to initiate a project sets in motion several processes In other words , once something is deemed impossible (using valid knowledge) can be used to classify data, but not to establish cause-effect relationships. How do we establish a cause-effect (causal) relationship? logic here, consider a classic example from economics: does inflation cause unemployment? Just go look at the threats to internal validity (see single group threats, multiple group . Given samples from a pair of variables A, B, find whether A is a cause of B. Unraveling potential cause-effect relationships from such observational data could save a lot of time Please make new submissions with the new validation set.

For instance, consider the syllogism: I don't know about you, but sometimes I find it's not easy to think about X's and Y's. Let's put this same syllogism in program evaluation terms: This provides evidence that the program and outcome are related. Notice, however, that this syllogism doesn't not provide evidence that the program caused the outcome -- perhaps there was some other factor present with the program that caused the outcome, rather than the program. The relationships described so far are rather simple binary relationships.

Sometimes we want to know whether different amounts of the program lead to different amounts of the outcome -- a continuous relationship: It's possible that there is some other variable or factor that is causing the outcome. This is sometimes referred to as the "third variable" or "missing variable" problem and it's at the heart of the issue of internal validity. What are some of the possible plausible alternative explanations? Just go look at the threats to internal validity see single group threatsmultiple group threats or social threats -- each one describes a type of alternative explanation.

In order for you to argue that you have demonstrated internal validity -- that you have shown there's a causal relationship -- you have to "rule out" the plausible alternative explanations.

  • Internal Validity

How do you do that? One of the major ways is with your research design. This is a manifestation of the reference class problem.

Most statistical studies of the kind used in marketing or management studies are enumerative, not analytical — i. Therefore it is incorrect to attribute the outcome to a single factor such as farsighted managerial action.

She uses the somewhat dated and therefore incorrect example of the relationship between smoking and heart disease. But this fact may not show up in the probabilities if other causes are at work.

Background correlations between the purported cause and other causal factors may conceal the increase in probability which would otherwise appear. A simple example will illustrate. It is generally supposed that smoking causes heart disease.

Establishing Cause and Effect

This expectation is mistaken, however. Even if it is true that smoking causes heart disease, the expected increase in probability will not appear if smoking is correlated with a sufficiently strong preventative, say exercising.

To see why this is so, imagine that exercising is more effective at preventing heart disease than smoking at causing it. For the population of smokers also contains a good many exercisers, and when the two are in combination, the exercising tends to dominate….

In the case of strategic outcomes, it is impossible to know all the factors involved. Moreover, the factors are often interdependent and subject to positive feedback see my previous post for more on this. Conclusions The implications of the above can be summarised as follows: That said, it is only natural to claim responsibility for desirable outcomes and shift the blame for undesirable ones, as it is to seek simplistic solutions to difficult organisational problems.

Hence the insatiable market for management snake oil. This self selection bias probably caused an erroneous and spurious correlation between HRT and women's health.

Some scientists mistakenly believe that large samples can establish causality. Just as numeric measures can't establish cause, neither can the size of the sample or population studied. Large numbers of participants can increase the stability of research results, but do not help to designate cause and effect. Watch for some of these fallacies in establishing cause and effect in the research that you encounter. However, two variables can be associated without having a causal relationship, for example, because a third variable is the true cause of the "original" independent and dependent variable.

For example, 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 violent crime? The correlation occurs statistically because the hot temperatures of summer cause both ice cream consumption and assaults to increase. Thus, correlation does NOT imply causation.

Cause and effect in management | Eight to Late

Other factors besides cause and effect can create an observed correlation. The effect is the dependent variable outcome or response variable. If you can designate a distinct cause and effect, the relationship is called asymmetric. For example, most people would agree that it is nonsense to assume that contacting lung cancer would lead most individuals to smoke cigarettes.

For one thing, it takes several years of smoking before lung cancer develops. On the other hand, there is good reason to believe that the carcinogens in tobacco smoke could lead someone to develop lung cancer. Therefore, we can designate a causal variable smoking and the relationship is asymmetric. Two variables may be associated but we may be unable to designate cause and effect.

validity of data cause and effect relationship examples

These are symmetric relationships. For example, men over 30 with higher mental health scores are more likely to be married in the U. Marriage is a "buffer" protecting from the stresses of life, and therefore it promotes greater mental health. Perhaps the causal direction is the reverse. Men who are in better mental shape to begin with get married. Maybe both are true When we cannot clearly designate which variable is causal, we have a symmetric relationship.

RULES AND GUIDANCE Since we know that we cannot use experimental treatments in naturalistic variables to determine cause and effect, yet we know that scientists can and do draw causal conclusions in nonexperimental studies, here is a set of helpful rules for tentatively establishing causality in correlational data. For a more detailed discussion, I recommend the following books: Using Experimental and Observational Designs.

This excellent book is still in print! Used copies are available on Amazon and other auction sites and it covers causal issues in more than just surveys. By the way, there are always alternative causal explanations in experiments too. The study control group may be flawed. Participants' awareness of being studied may create conditions e.

Establishing Cause & Effect

So even though it may be easier to establish cause in experiments, keep in mind that nothing is fool-proof. The independent variable came first in time, prior to the second variable.

Gender or race are fixed at birth. Gender or race can be important causal variables because individuals behave differently toward males or females, and often behave differently toward individuals of different religions or ethnicities.

The independent variable is harder to change. The dependent variable is easier to change. One's gender is much harder to change than scores on an assessment test or years of school. If one variable is a necessary or sufficient condition for the other variable to occur, or a prerequisite for the second variable, then the first variable may be the cause or independent variable.

A certain type of college degree is often required for certain jobs. At most research universities, publications are a prerequisite for being awarded tenure.

Correlation and causality - Statistical studies - Probability and Statistics - Khan Academy

If two variables are on the same overall topic and one variable is quite general and the other is more specific, the general variable is usually the cause. Overall ethnic intolerance influences attitudes toward Hispanics. If reversing the causal order of the two variables seems illogical and makes you laugh, reverse the causal order back.

We will apply them all semester! So you obtain a sample of Educational Psychology undergraduate students. With the flip of a coin, half the students receive a physical and mental health screening and those who are fit begin this new exercise program.

The other half also receive a health screening but no exercise regimen. Six weeks later, you re-examine everyone who was physically fit in the screening and compare the two groups. The group receiving the exercise plan now score happier and healthier than the group that did not. Jubilant over the results, you assert that your new exercise plan contributes to physical and mental fitness! Are your results internally valid?

validity of data cause and effect relationship examples

This study was a "true experiment. It is randomization that makes true experiments so strong in internal validity and typically allows us to make relatively strong influences about causality. It is also random assignment to treatments that distinguishes a true experiment from other kinds of data collection.

Random assignment means that on the average at the beginning of a study, all your treatment groups are about the same. In your physical fitness study, it meant about the same percent of each group "flunked" the screening test and about the same percent exercised on a regular basis, even before your intervention.

Random assignment or "randomization" controls at the beginning for all the variables you can think of, and, more important, all the variables you didn't think of.

This study had another important research design aspect: Control or comparison groups are critical in all kinds of research.