While there are many types of quantitative research designs, they generally fall under one of two umbrellas: experimental research and non-experimental research. The four most commonly used designs for research studies are descriptive, correlational, quasi-experimental, and experimental.”(Grove, Gray & Burns, 2015).
In experimental design researchers uses random assignment and they manipulate an independent variable around a controlled variable. It is an objective, systematic, and highly controlled investigation conducted for predicting and controlling phenomena (Grove, Gray & Burns, 2015). A true experimental design there must be randomization, a control group and manipulation of a variable when examining the direct cause or predicted relationships between variables. In a quasi-experiment one of these aspects is missing (Sousa, Driessnack & Menders, 2007). As noted in Research Designs: Non-Experimental vs. Experimental (2018), When an experimental research is done correctly, experimental designs can provide evidence for cause and effect. Because of their ability to determine causation, experimental designs are the gold-standard for research in medicine, biology, and so on.
Descriptive and correlational designs can be referred to as non-experimental designs because the focus is on examining variables as they naturally occur in environments and not in the implementation of a treatment by the researcher. Non-experimental research, on the other hand, can be just as interesting, but you cannot draw the same conclusions from it as you can with experimental research. Non-experimental research is usually descriptive or correlational, which means that you are either describing a situation or phenomenon simply as it stands, or you are describing a relationship between two or more variables, all without any interference from the researcher. This means that you do not manipulate any variables (e.g., change the conditions that an experimental group undergoes) or randomly assign participants to a control or treatment group. Without this level of control, you cannot determine any causal effects. While validity is still a concern in non-experimental research, the concerns are more about the validity of the measurements, rather than the validity of the effects.
Grove, S., Gray, J., & Burns, N. (2015). Understanding Nursing Research, 6th Edition. Saunders, 092014. VitalBook file.
Research Designs: Non-Experimental vs. Experimental. (2018, July 19). Retrieved from http://www.statisticssolutions.com/research-designs-non-experimental-vs-experimental/
2-Experimental research is based around a test having a notable result. Basically, you test a hypothesis out and if the desired effect appears, it may be accurate. Essentially cause and effect. Normally this research will have controls and variables to help clarify the nature of the results. This kind of research is highly controlled to help prevent false conclusions. An example of experimental research would be common drug trials. During these trials, researchers are hoping to either discover new information about their drug or create further confirmation of what they already believe to be true. These tests are highly controlled.
Non-experimental research is based around the observation of behavior in a non-scientific setting. By this I mean that researchers look for possible data correlations by collecting information rather than testing a theory. An example of this would studies where researchers try to connect things like high mortality to a certain lifestyle or food choice. Because of the obvious risk to the patients, they would just collect information rather than staging experiments. The non-experimental model of research is much laxer and not as controlled.
Grove, S., Gray, J., & Burns, N. (2015). Understanding Nursing Research: Building an Evidence Based Practice (6th edition). St. Louis,MO. : Elsevier.
3- direct experimentation is indeed an excellent ways to obtain and analyze data. The observational changes observed can also be used to plan further studies. However, the preparation and execution of such experimentation is costly and time consuming. In contrast, lived experience of conditions suggested by numerical values found in experimental research is found in qualitative data. This data can be collected in fairly cheap and easy ways. However, the vastness of it and varying nature means that it has to be documented and analyzed by people, with little assistance from a machine (as various responses can be linked to one general value and that may not be easily programmed an algorithm to understand.The essential issues become: 1) Are you looking for qualitative or quantitative data? and 2)What does the data obtained say about the focus of the study? Ultimately, both types of research are necessary and valuable and allow problems to be considered in a detailed manner, differentiating the minutiae.