Experimentation is one of the most frequent yet most challenging activities in research and academic studies. In an experiment, you expose a group of research subjects to some intervention or treatment. You then compare the results of your experiment with the results and processes observed in a non-experimentation group. Always and in all circumstances, the quality and design of your experiments affect the validity and reliability of your results.
In all experiments, treatment entails the administration of some intervention that may influence the health or behaviors of the study subjects. For instance, you will design a novel treatment strategy for a group of adults with hypertension. You will then measure changes in their blood pressure and compare them with the results achieved using conventional therapies. You will see which therapies provide the best result and outline recommendations for physicians and nurses. You may design your treatment strategy based on complexity or the dosage of medications provided.
A factor is also an independent variable – it is something the experimenter manipulates and controls in an attempt to achieve the desired result. For example, it can be a new treatment strategy in which the experimenter manipulates drug dosages and patterns of drug administration to yield a better response. The use of novel medications to treat hypertension is a factor or an independent variable in the experiment.
When the first bodies of primary data are retrieved from an experiment, researchers should take their time to review, systematize, and analyze the raw results. Experimental design is everything that occurs between the experiment itself and the moment its results are reported to the public. Therefore, any researcher should be clear about the questions he aims to answer or hypotheses he aims to test in his experiment. Confounding variables or factors that can mediate the effects of the experimental intervention on the research subjects should also be mediated and discussed. The aim of any experiment is not simply to add to the existing knowledge but also to enhance the precision and accuracy of its results.
Imagine that there are two groups of adults with hypertension. The first (experimental) group receives novel treatment, which promises to reduce their blood pressure. The second (control) group receives conventional treatment for hypertension. However, the first group comprises younger subjects, and their symptoms are not as pronounced as of the older subjects in the control group. The problem with this experiment is that the researcher does not account for the effects of age on the effectiveness of the novel hypertension treatment. As a result, he or she may conclude that the experimental treatment is more effective than conventional one.
In reality, the researcher must control for any variables that can confound the relationship between the treatment and changes in blood pressure among the study subjects. Otherwise, experimental bias will have negative impacts on the validity and reliability of the experimental results. Ideally, the researcher should create two groups of patients with similar demographic characteristics or assign all subjects randomly to the experimental and control groups.
Another problem is that the patients involved in the experiment may erroneously believe that the proposed treatment will help them. This is what researchers call “placebo effect”, when patients think that they feel better, although they take nothing but a sugar pill. This is one more reason why an experiment should always involve an experimental and a control group. However, even then, researchers cannot always guarantee that the results of their experiments are unbiased. If the members of the control group learn that they are taking a sugar pill, its effects on their health and wellness will be difficult to evaluate. This is why the current state of research and science recommends using double-blind designs to minimize the risks of bias and enhance the quality and reliability of experimental results. In double-blind studies, researchers can guarantee that both groups are treated absolutely equally. This type of design is best suited to eliminate research bias in experimentation.
Expert judgment and control variables are not sufficient means to eliminate the risks of bias. Therefore, researchers should make an extra step to create a robust and reliable research design. Randomization has proved to be one of the critical components in designing and implementing experiments. Randomized controlled trials have already become the gold standard of quality and reliability in research, particularly in the fields of nursing and medicine. Randomization implies that subjects are assigned to any of the two research groups, and today’s researchers can use several different designs with randomization.
Completely Randomized Design
It is a distinct type of research design when the subjects of research are assigned to an experimental or a control group. For example, researchers use labels for each subject and then randomly choose labels to assign the subject to either an experimental or a control group. Computer software can also be helpful in randomizing study subjects. Minitab is the statistical software used to randomize the study subjects between the two groups.
Randomized Block Design
A randomized block design is another option for a researcher who knows that tiny differences exist between the subjects in an experimental and a control group. It is an alternative to a completely randomized design trial. Here, the researcher will first need to divide all subjects into several clusters, depending on their demographic or other unique characteristics. For example, if the researcher considers gender as a serious factor of influence on the effectiveness of hypertension medication, he or she may want to divide all subjects into men and women and then assign them to an experimental and control group within each gender cluster. This design benefits researchers, since it takes into account the issues of both randomization and confounding variables.
Even when you use randomization to ensure that your experimental and control groups receive similar treatment, other researchers may still want to check the initial results of your study. This is why you should outline your experimental strategy in ways that allow other researchers to repeat the whole process. Moreover, if the initial sample in your study is small, you should be ready to replicate your study in a larger population group. This way you will be able to check the effectiveness of treatment and analyze possible deficiencies or limitations of your treatment method. For example, you will be able to note any side effects in relation to the proposed treatment, which you could not note in a smaller sample. With the help of replicability you can reinforce the validity and reliability of your experimental design and the results of your study. You will also increase the chances that these results will be used in future nursing practice and research.