True experiments enable us to draw conclusions about causality. Relational (correlational) studies do not.?

Explain why each of these is the case and give examples to clarify your points








I need helpTrue experiments enable us to draw conclusions about causality. Relational (correlational) studies do not.?
In experiments, you manipulate the variable you believe to be the cause, and then look at the results.





With correlational studies, you just look at the data and see if they correlate.





For example, a typical correlation example is that as ice cream sales increase, so do drownings, so here you have a positive correlation.





But does ice cream really cause people to drown?





With an experiment, you would give some people ice cream and let them swim, the others they will have none and swim,. Then hopefully if your hypothesis is correct, people who had the ice cream would drown.True experiments enable us to draw conclusions about causality. Relational (correlational) studies do not.?
Correlational studies use observational data to determine whether or not there is a relationship between two variables. You can not determine causation, because there is no manipulation of variables. All you can determine is that the variables are related, but you don't know whether A causes B, B causes A, or a third factor, C, is causing both A and B. For example, let's say there is a positive correlation between number of police in a given city, and crime. You can not say based on this relationship whether the high amounts of police are causing the crime, crime causes the high police presence, or some other factor is causing both.





True experiments involve manipulation of variables that can prove some of the conditions for causation. These conditions are time order (you have to prove that the changes in the dependent variable occur AFTER the variation in the independent variable). A second condition is Nonspuriousness. That is, the independent variable, and not some other third variable, is causing the changes in the dependent variable. The third most important factor is correlation. For there to be a causal relationship, the variables must move together. (Don't get confused---causation requires correlation, but correlation does not assume causation). For example, in an experiment to determine how coffee effects reaction time, you can manipulate the circumstances by testing people's reaction time when they've had caffeine, and testing later without caffeine. You would also control for all sorts of conditions (make sure you test at the same time of day, etc. to fulfill the condition of nonspuriousness), and lastly make sure that the variables are related.