Analysis 1.1 Correlation versus Experimental Research
There are two general categories under which most empirical research falls: correlational and experimental. In correlational research variables are not manipulated, but are only measured. The purpose is to identify the relationship between the variables. For example, correlation between the set of variables such as blood pressure and cholesterol level is examined. In experimental research, variables are manipulated, and the effects of this manipulation is measured upon the dependent variable.
In experimental research a treatment is deliberately imposed on a group of objects or participants. In other words, blood pressure can be artificially increased by a researcher; and then cholesterol level is recorded, showing increase or decrease. If changes that are made in variable A lead to changes in variable B, then the conclusion that “A influences B” can be made. Casual relations between variables can only be conclusively demonstrated by experimental data. Data from correlational research cannot prove causality: correlation does not equal causation (Kowalczyk, 2015).
There appears to be only disadvantages to non-experimental research. It cannot find cause and effect relationships, cannot manipulate predictor variables and the methods of study are often correlation or case studies. There are clear cut disadvantages to non-experimental designs. However, non-experimental research does have at least some advantages over experimental design. A non-experimental study picks up the slack from an experimental design (Kowalczyk, 2015).
Kowalczyk, D. (2015, September). Non-Experimental and Experimental Research: Differences, Advantages & Disadvantages. Retrieved from study.com/: http://study.com/academy/lesson/non-experimental-and-experimental-research-differences-advantages-disadvantages.html...