Quantitative Research Design

Quantitative research is a systematic process that collects and analyses measurable information by use of numbers and utilizes statistical methods. Quantitative studies can be distinguished using experimental and non-experimental research designs. Experimental research designs use a particular hypothesis to refute and confirm the hypothesis validity. The design uses and manipulates a dependent variable, independent variable, and a control group. 

Contrary to experimental research design, a non-experimental research does not alter, manipulate or control subjects or predictor variables but relies on observations, interactions, or interpretations to come up with conclusions. Thus a researcher relies on case studies, surveys, or correlations and therefore no actual cause-and-effect relationship is demonstrated. Polit and Beck (2004) explain that experimental research design introduces a treatment or intervention while non-experimental research design collects information without introducing or making changes to treatment. Experimental research is more advantageous to non-experimental design as it replicates studies to almost exact circumstances, has a high level of control and due to cause-and-effect.

One potential quantitative research study is an exploration of good death components in cancer care. Cancer is the leading cause of death in many countries with the patients suffering from both psychological and physical problems during their dying process. The best quantitative research design for this study would be non-experimental research design. The choice of this design is due to the vast number of cancer cases worldwide. Non-experimental research design as explained by Campbell and Stanley (2015) has a high external validity level making it generalized to an enormous population. The researcher in designing the conclusion will not manipulate the subjects but will relay on interpretations.

This study will rely on case studies to clarify on the relative significance of each component of good death. A statistical package should be used to measure the component. This study will assist in understanding the importance of palliative care to achieve ‘good dying processes or ‘good death.’ The quantitative research should explicate the important factors which influence the end life. Some of the factors may include achieving a sense of completion, preparation for death, and symptom and pain management.

Reference

Campbell, D. T., & Stanley, J. C. (2015). Experimental and quasi-experimental designs for research. Ravenio Books.

Polit, D., & Beck, C. (2004). Nursing research: principles and methods (7th ed., pp. 46-50). Philadelphia, Penns.: Lippincott Williams & Wilkins.