Health Care-Focused and the Variables

Nominal 

The criterion under this category is based on the named data that cannot overlap. The category lists variables without any quantitative value and is mutually exclusive. The first example of nominal data as employed in healthcare data system is gender; male and female. Patients are categorized as either male or female. Another example includes surgical outcome which is either dead or alive (Marateb, 2014). Another important example in this category is the result of diagnosis for example; the patient can be diagnosed with Malaria, Typhoid, HIV/AIDs or Pneumonia. 

Ordinal 

The data or information is classified in specific order or scale. Probable categories can be listed in an exact order or in a natural way that can provide further details. The variable focuses on non-numerical values. A perfect example would be the severity of a condition of a patient can be classified into three categories (Sarkar, 20114). The first category would be mild, followed by moderate and finally severe. Another example is the stage of cancer ranging from 1 to 5. The final example is the heat on the body of patients. It can be hot, cold or warm (normal). 

Interval 

The measurements of data have equal distances between the values and zero has no meaning. The body length in infants falls in this category. Similarly, pain level ranging from 1-10 can also be categorized here (Sarkar, 20114). Undertaking IQ tests and listing the ranges from 100-150 would be in this category. 

Ratio 

Observation and data are given quantitative score just as in the interval scale number. The only difference is that this category has an absolute zero. Measuring the height of patients is in this category (Marateb, 2014). Additionally, finding the difference in weights of patients also fall in the category of ratio variable. Finally, measure of temperature in Kelvin thermometer is a ratio variable. 

References 

Marateb, H. R et al. (2014). Manipulating measurement in medical statistical analysis and data mining: A review of methodologies. Journal of Research in Medical Sciences . 19(1), 47-56. https://www.ncbi.nlm.nhi.gov/pmc/articles/PMC3963323/#_ffn_sectittle 

Sarkar, S. (2014). Understanding data for medical statistics. International Journal of Advanced Medical and health research . 1(1), 30-33. DOI:10.4103/2349-4220.134449