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The use of the above variables will help the nurse put the research into perspective for two groups, adults and other clients admitted to cardiac unit (Lash, Plonczynski, & Sehdev, 2012). In this particular research, it would be essential to measure these variables and record the values.
The most suitable level of measurement would be ordinal, as this will help answer the PICOT question. Being that the research measures the likelihood of patients with congestive failure having nosocomial infections, ordinal measurement best fits the research. This is because the nurse is interested in the occurrence (or non-occurrence) of this attribute. This implies that using ordinal measurement will help the nurse collect and record the data in the most appropriate format (Adler & Parmryd, 2010). In ordinal measurement, the values the nurse will use in recording the findings will be of no numerical importance other than describing the occurrence (or non-occurrence) of a trait.
A suitable statistical test to help answer the question is Pearson correlation coefficient. This correlation coefficient help determine the relation between two variables in question (Bishara, & Hittner, 2012). After the nurse collects data on the occurrence of nosocomial infections in both adults and other clients with congestive heart failure, it would be ideal for the nurse to correlate this variable for both groups. The correlation coefficient will then be useful in determining the likelihood of either group developing the infection.
Adler, J., & Parmryd, I. (2010). Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Manders overlap coefficient. Cytometry. Part A: The Journal of the International Society for Analytical Cytology, 77(8), 733-742. Bishara, A., & Hittner, J. (2012). Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches. Psychological Methods, 17(3),
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