Validity of data cause and effect relationship examples

MDM4U – Grade 12 Data Management – Analysis of 2 Variable Data Test— onstudynotes

validity of data cause and effect relationship examples

The three criteria for establishing cause and effect – association, time ordering A common example is the relationship between education and income: in general, procedures whenever possible), careful data collection and use of statistical. Strong internal validity refers to the unambiguous assignment of causes to effects . .. For example, there is a statistical correlation over months of the year between on establishing cause and effect in observational or correlational data , see. Q 2: Which one is NOT an example of reverse relationship? Q 5: Are the following example shows reverse cause-effect relationship?.

Because again causality is critical to the research enterprise! Much of science consists of ruling out alternative causes or explanations. While science is one form of knowing and one generic way of gathering evidence that either disconfirms or is suggestive of causality, it is not the only way of doing so.

The results of science may or may not be accurate, but without following "the rules" of science, most scientists do not believe one is "doing science. According to science rules, definitive proof via empirical testing does not exist. Science uses the term "proof" or, rather, "disproof" differently from the way attorneys or journalists do. Our measurements could be later shown to be contaminated by confounding factors.

A correlation could have many causes, only some of which have been identified. Later work can show earlier causes to be spurious, that is, both cause and effect depend on some prior causal often extraneous variable.

validity of data cause and effect relationship examples

Further, science at its best is a self-correcting process. Another researcher can try to duplicate your results. If the results are interesting, in fact, dozens of researchers may try to duplicate the results. If something was awry with your study, the subsequent research projects should discover and correct this.

We use the rules of science in this course. While normal lung tissue is light pink in color, the tissue surrounding the cancer is black and airless, the result of a tarlike residue left by cigarette smoke. Lung cancer accounts for the largest percentage of cancer deaths in the United States, and cigarette smoking is directly responsible for the majority of these cases. There are many topics where it is neither possible--nor desirable--to use the experimental method.

To accept more correlational evidence it will help to examine the rules below. I have never understood how the numeric level of one's measures can have much to do with cause.

After all, variables such as gender, nationality, and ethnicity can have profound casual effects and they are categorical variables. Authors who make this mistake may also misunderstand causality. This causal conclusion about smoking and lung cancer is based on correlational or observational evidence, i.

There is no doubt that the results from careful, well-controlled experiments are typically easier to interpret in causal terms than results from other methods.

MDM4U – Grade 12 Data Management – Analysis of 2 Variable Data Test

However, as you can see, causal inferences are often drawn from correlational studies as well. Non-experimental methods must use a variety of ways to establish causality and ultimately must use statistical control, rather than experimental control. The results of the Hormone Replacement Therapy experiments, released in the summer ofremind us of the great care that must be taken when designing nonexperimental research. Self selection of women into the original "hormone" non-experimental conditions implied that HRT prevented heart attacks and strokes among women.

In fact, when the topic was studied experimentally the reverse was true: HRT increased the risk of heart and circulatory disease among women. The discrepancy probably occurred because women who take better care of themselves may see a physician on a more regular basis, and thus be in better health to begin with. 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.

Explicit Cause and Effect Relationships

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. 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.

validity of data cause and effect relationship examples

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. 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.

Cause and Effect Relationship: Definition & Examples

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. 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. 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.

Establishing Cause and Effect - Scientific Causality

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? 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. If we did not have a control or comparison group, the study would be open to the criticism--and alternative causal explanation--that improvement in health would have occurred in any event among young adults, even had the exercise program never been instituted.

validity of data cause and effect relationship examples

Without the alarm, you probably would have overslept. In this scenario, the alarm had the effect of you waking up at a certain time.

This is what we mean by cause and effect. A cause-effect relationship is a relationship in which one event the cause makes another event happen the effect. One cause can have several effects. For example, let's say you were conducting an experiment using regular high school students with no athletic ability. The purpose of our experiment is to see if becoming an all-star athlete would increase their attractiveness and popularity ratings among other high school students.

Suppose that your results showed that not only did the students view the all-star athletes as more attractive and popular, but the self-confidence of the athletes also improved. Here we see that one cause having the status of an all-star athlete has two effects increased self-confidence and higher attractiveness ratings among other students.

Cause-Effect Criteria In order to establish a cause-effect relationship, three criteria must be met. The first criterion is that the cause has to occur before the effect. This is also known as temporal precedence. In the example above, the students had to become all-star athletes before their attractiveness ratings and self-confidence improved. For example, let's say that you were conducting an experiment to see if making a loud noise would cause newborns to cry.

In this example, the loud noise would have to occur before the newborns cried. In both examples, the causes occurred before the effects, so the first criterion was met.