Research has taken over empirical studies in the modern-day world. Research forms an integral part of day-to-day life and is attributable to the great strides made in the advancement of human life. A recent study by the Oregon hospital regarding health insurance is aimed at shedding some light on public insurance. The research study looks into the effects of accessibility of public insurance by use of some randomized, controlled design. The research takes into consideration the respondent variables such as the number of policies taken up. It also puts emphasis on predictive variables such as the duration of conducting the study.
Human health remains a paramount item of concern with the rush to have a disease-free planet. Through research, the medical world has made great strides in the eradication of opportunistic diseases, which has brought about a paradigm shift as far as health safety is concerned. However, the methods of conducting various types of research have raised some issues even among the research bodies. The research techniques employed and research methodologies have been a bone of contention in conducting research. This paper seeks to highlight and criticize a recent research carried out by the Oregon hospital. This will bring forth limitations that will question the reliability of research as an accurate tool for predicting outcomes.
The research conducted by the hospital selected its research group by means of lottery. The researchers randomly selected patients that did not have Medicaid cover. A similar method of selection was used to get a control group for the study. The research was to go on for a period of more than a year. Statistically, this is a random method of natural selection. Although it is an acceptable way of selecting a target population for conducting a study, it is not objective in nature. The distribution of the population is not exact. The rationale for getting the sample size is not adequate so as to fully represent or rather generalize the entire population.
There is a fair level of arbitrariness in the fact that different, sometimes extreme, sample species are clustered in the same standardized treatment effect. It is worth noting that individuals without insurance vary for many different reasons such as income levels, employment status, age, and health status. These critical factors are less likely to correlate with the desired objective. Due to the fact that the research is an experimental one, it does not bring forth a clear picture of the research objective. Given that the research conducted was for a period of one year, it is indeed a very short period of time to come up with a conclusive and objective report.
This research employs various variables both dependent and independent. By broad description, a dependent variable is a variable that represents output or effect for that matter. A dependent variable is tested to check if it causes the effect. For the purposes of the research by the Oregon hospital, some of the dependent variables that were used include having a personal doctor, getting all necessary medical care for the last six months, having access to all required drugs for the last six months, and having a usual clinic-based care. Clearly, not all the respondents have a stable income to employ the services of a personal doctor. Therefore, it is not objective to consider this as a parameter for conducting this study. Another variable used is getting all the medical care needed by an individual. Obviously, even in developed economies, such as the United States where this study was carried out, not all required medical services are available. Again on this note, this report fails to articulate properly a correct variable so as to give an objective report. For proper, objective research, better dependent variables need to be used. As highlighted above, this is not the case.
The medical research done by the Oregon hospital indicates the financial implication to an individual seeking an insurance cover. This is a great aspect in research. Due to the fact that the research is experimental, it beats the logic, as the data involved are not accurate and do not correctly represent the general population. This research makes an assumption that all the people under the study are employed and generate income. In reality, a portion of the sample population is not satisfactorily employed to afford Medicaid insurance. It is necessary to consider price and income effects of subsidised health insurance. This includes but is not limited to poor health service delivery to individuals. It also includes under equipment of resources for the health insurance policy to run smoothly and for a longer period. Therefore, this puts into question the credibility and consequently the reliability of this study to effectively address the intended objective.
In conclusion, the argument put forth that the use of computer simulation technology has reduced the sampling error to an acceptable range does not alleviate the margin of error in the study. It does not show that the methods used to gather sample data have a balance between treatment and control characteristics. There are concerns about the major assumptions for causal inference. The disparities in the attrition and in pre-randomization of characteristics have made the study be out of context in realizing the objective of the report. It is clear that while other factors have been well-presented in the research paper, some of the critical elements have been overlooked. The variables and methods of selecting a sample population are not adequate in the experiment research. Thus, the study needs to consider the discussed attributes.