As doctoral students, conducting empirical research is one a foundational aspect of the body of work for the DPI project. Two key elements of evaluation of research is to understand the validity and reliability of the data. If data elements are not valid the results of the study will be impacted. There are four types of validity: construct, statistical conclusiveness, internal and external validity (Abowitz & Toole, n.d.). When establishing the data elements in a research project, it is crucial to ensure that the data is measurable and non-biased. Using data elements that have been used in previous studies provides a higher level of validity of data versus creating an untested element.
Being able to critique quantitative research is an important skill for medical providers. Consideration must be given not only to the results of the study but also the rigor of the research. Rigor refers to the extent to which the researchers worked to enhance the quality of the studies. In quantitative
research, this is achieved through measurement of the validity and reliability. Validity refers to the credibility or believability of the research. Validity looks at whether the measuring instrument adequately covered all of the content that it should cover with respect to the variable (Lawrence, 2015). For example, does the instrument cover the entire domain related to the variable, or construct it was designed to measure? An example is, does an undergraduate nursing course with instruction about public health, does an examination with content validity would cover all the content in the course with greater emphasis on the topics that had received greater coverage or more depth.
Reliability refers to the repeatability of findings. If the study were to be done a second time, would it yield the same results? If the data produces the same results then we can say that the data is reliable. If there is more than one person observing an actual behavior or some event, all observers should agree on what is being recorded in order to claim that the data are reliable. The question to ask is “does the research instrument consistently has the same results if it is used in the same situation on repeated occasions?” A simple example of validity and reliability is an alarm clock that rings at 7:00 each morning, but is set for 6:30. It is very reliable (it consistently rings the same time each day), but is not valid (it is not ringing at the desired time) (Lawrence, 2015).
Reliability and validity are important to remember when analyzing a research article. If the results of a study are not deemed to be valid then they are meaningless to a study. If it does not measure what we want it to measure then the results cannot be used to answer the research question,
which is the main aim of the study. These results cannot then be used to generalize any findings and become a waste of time and effort. It is important to remember that just because a study is valid in one instance it does not mean that it is valid for measuring something else. Once again reliability refers to the repeatability of findings. If the study were to be done a second time, would it yield the same results?
Lawrence, L. (2015). Validity, reliability, and generalizability in qualitative research. Journal of Family Medicine and Primary Care, Vol 4, Iss 3, Pp 324-327 (2015), (3), 324. doi:10.4103/2249-4863.161306
Mohajan, H. (2017). Two criteria for good measurements in research: Validity and reliability. Annals of Spiru Haret University Economic Series, Vol 17, Iss 4, Pp 59-82 (2017), (4), 59. https://doi-org.lopes.idm.oclc.org/10.26458/1746
Struggling with online classes or exams? Get expert help to ace your coursework, assignments, and tests stress-free!