Feasibility and bias in experimental research

Nursing Education, Hospital Ratio, And Patient Outcome

Health is indisputably one of the main concerns of every state. For a society to function well, its citizenry must be of healthy state. One of the many interesting topics in the domain of healthcare would be the point of convergence of two important societal institutions: health and education -- which can be found in the concept of "nursing education."

Literature shows that the education of nurses affects patient outcomes. The study by Aiken et al. (2003) showed that there is a decrease in patient mortality for every increase in the number of nurses who have had baccalaureate and higher level education. This is supported by the 2004 study of Callahan which bore almost the same results, i.e. more degree-educated nurses are associated with higher survival chances for patients undergoing surgical procedures. The length of stay of patients in the hospitals has also been found to be lesser among hospitals with nurses who have higher degrees as shown in a recent study by Kutney-Lee and Aiken (2008).

Unfortunately, knowledge about nursing education has been found to be measly. It is in this regard that this research aims to contribute to scholarly works on the field of nursing education through the undertaking of this project. This research aims to solidify the results found in research literature by duplicating the studies on nursing education and patient outcome. Since most of the reviewed literature dealt with surgical patients, this research is prodded by the interest to look at the research when you factor out surgery-related cases. This means understanding the interplay of nursing education, patient-nurse ratio, and patient outcome in its most fundamental form, regardless of whether the cases of patients are surgery-related or not.

METHODOLOGY

II. Hypothesis and Research Design

This study aims to test the following hypotheses:

H0 = Both nursing education and patient-nurse ratio do not affect patient outcomes

H1 = Education of nurses decreases patient mortality rate as education is equated to more problem-solving skills and other abilities

H2 = The lower the patient-nurse ratio is, the more positive patient outcome becomes

To be able to test the above-mentioned hypotheses, this study will conduct a cross-sectional, quantitative method of hypothesis testing since it aims to discover the strength and direction of association between three variables, i.e. nursing education, patient-nurse ratio, and patient outcome. Quantitative data will be utilized in this study as this method allows for a more generalized perspective on the subject-matter which is in line with the objective of understanding the basic principles and logic that operates between these three variables.

III. Sampling

This study will employ purposive sampling in terms of choosing the hospital that will be included in this study. This study will look for a hospital which has a wide variation in terms of educational attainment of its nurses. The nursing population of this chosen hospital will then become the participants of this study. Only the currently-employed; full-time nurses are eligible for the survey.

IV. Data Collection and Analysis

This study will use secondary information. The researcher will request the educational attainment data of their employed nurses to the Human Resources Department. The data for patient-nurse ratio and patient outcome on the other hand, will be requested to the administrative authorities of the hospital.

After completing the data-gathering phase, the researcher will encode the answers and will analyze data using statistical software that will generate: (a) Pearson's r, which will determine the strength of association of these three variables; (b) linear regression, which will specify the nature of relationship among variables (Nachmias & Nachmias, 1996, p.421).

Bias

Since this study will be using secondary data, there is a very low possibility of bias entering the study. It can be susceptible to statistical errors though. Since this study will be analyzed via significance testing, Type I (rejection of true hypothesis) and Type II (acceptance of a false hypothesis) error are possible. In order to minimize these errors, the significance levels (alpha) will be lowered at.05 (ibid.).