What Are the Advantages & Disadvantages of Non-Experimental Design?

Quantitative methods help researchers of different stripes gather data on a variety of subjects. Researchers must choose their methods carefully, however, as one type of quantitative method design may not suit all types of research. Some research requires a design which includes no applied treatment, experiment or anything that otherwise interferes with the subject of the research. This type of method is called "non-experimental" design and features some distinct advantages and disadvantages.

Sometimes research requires short, shallow data gathering, as in a survey. For example, if you wish to conduct a census, applying a treatment or otherwise involving the researcher in the process other than simply asking questions can disrupt the research. Short surveys also have the advantage of not requiring researchers to administer them. Researchers need only hand out the surveys and collect and study the data. This becomes a strong advantage when working with a minimum of researchers and funding or when keeping the number of variables in a study very low.

The concise nature of non-experimental design becomes a disadvantage because it does not allow for the gathering of data post-treatment. Gathering data post-treatment can introduce entire new areas for researchers to consider. Without the inclusion of experimentation or applied treatments, the research becomes mostly one-dimensional -- focused on a small series of variables. The quick, grab-and-go nature of non-experimental quantitative design cannot deliver the same in-depth results as experimental design. Non-experimental design often fails to produce an adequate amount of data from which researchers may draw complicated, revealing or truly valuable conclusions.

Non-experimental design has a distinct advantage in research applications in which active involvement or experiment by the researchers might be unethical. Anthropological research illustrates this advantage quite well; when studying a human population, applying treatment or experimentation might interfere with the normal function, safety or peace-of-mind of the subjects. In some cases, experimental design in studies of humans can objectify the research subjects and, at its extreme, dehumanize them. Whether attempting to preserve the natural, observable activities of a population or preserving their self-possession and dignity with a non-invasive approach, non-experimental design works quite well.

Gathering data with which to make an argument for correlation between variables lies at the center of research. Quantitative methods designed with experimentation or applied treatments multiply the variety of ways researchers alter the variables within the research. For example, observing subject reactions in a single scenario can only reveal information about the few variables in that scenario. When a treatment or treatments are applied to that scenario, the amount of data researchers can gather greatly increases. The more the researchers observe an effect across a variety of different scenarios, the stronger the case for correlation. Non-experimental quantitative method designs can fail to provide enough data to make a convincing argument for correlation, let alone causation.