Designing a research study that produces accurate results and draws appropriate conclusions is generally complicated. For the study to be valid, many components should be considered, including demographics, sampling, cause-and-effect relations, and other factors. Moreover, the most important validity check is that research should be able to produce the same results if repeated. However, despite decades of emphasis on appropriate research design, examples of studies with false or misguided conclusions still exist. The researchers in these studies tend to ignore the best practices on the above-mentioned factors. The peer-research groups are there to replicate research and catch mistakes. However, this process takes time, and during this time gap, the inaccurate claims have a space to spread through the media to the general public, birthing false beliefs. The two studies provided in the discussion question exemplify this phenomenon. Despite the obvious nature of these studies’ misguided conclusions (Ubago-Guisado et al., 2021), it is hard to pinpoint the exact causes due to the lack of details. However, considering each type of research, general assumptions about the source of invalidity can be made.
Many things can facilitate the production of false results in a case-control study. Thus, the conclusions by researcher A can be caused by one of these factors or a combination of them, depending on the research design. The first factor is recall bias, in which people tend to be unreliable in recalling the facts about their exposure to a risk factor. In the case of researcher A, the data would be inconsistent if the study was designed to gather information on fruit and vegetable consumption from the participant’s memory. Thus, the conclusion of the study cannot be trusted.
Another factor is related to sampling and demographics. For the case-control study, it is necessary to select two groups of participants, case and control (Schuemie et al., 2019). In this case, it would probably be people with endometrial cancer and without, respectively. The possible issue with demographic and sampling could be that one of the two groups is influenced by other potential causes. One example could be the lack of genetic predisposition to cancer in the control group and its presence in the case group. Thus, it can be impossible to distinguish between the effects of fruit and vegetable consumption and genetic predisposition.
Similarly, researcher B’s inaccurate conclusions can result from sampling and demographics in the study. The cross-sectional study takes the slice of a diverse population to assess the effects of some phenomena. Therefore, a similar error is unlikely but still possible, as there are studies that selected the sample of a population clearly distinct from the general population. This sampling can allow the majority of chosen people to possess certain characteristics like genetic predisposition to prostate cancer, similar to study A.
Moreover, a cross-sectional study has an inherent weakness in creating cause-and-effect connections. It can also happen in case-control studies but not as frequently. For example, in research A, people who eat fruits and vegetables can be more likely to engage in other cancer-preventative measures that can cause the effect later. On the other hand, it is even harder to determine the linear cause-and-effect relation in a cross-sectional study, as both are studied simultaneously (Wang & Cheng, 2020). In other words, some prostate cancer patients can start eating fruits and vegetables to improve their overall health and recovery chances. Therefore, the cause-and-effect relation could be reversed, as prostate cancer may cause the increased consumption of fruits and vegetables.
References
Schuemie, M. J., Ryan, P. B., Man, K. K., Wong, I. C., Suchard, M. A., & Hripcsak, G. (2019). A plea to stop using the case‐control design in retrospective database studies.Statistics in medicine, 38(22), 4199-4208. Web.
Ubago-Guisado, E., Rodríguez-Barranco, M., Ching-López, A., Petrova, D., Molina-Montes, E., Amiano, P., & Sánchez, M. J. (2021). Evidence update on the relationship between diet and the most common cancers from the European Prospective Investigation into Cancer and Nutrition (EPIC) study: a systematic review.Nutrients, 13(10), 3582. Web.
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: strengths, weaknesses, and recommendations.Chest, 158(1), S65-S71. Web.