According to Houser (2018), nominal and ordinal level data are not subject to mathematical calculations, and “numbers given to these data are strictly for showing membership in a category” (Houser, 2018, p. 292). Nominal data only shows that one or something belongings to a certain category. Example would be labeling patient population as male or female. Categories can be given numbers as Males=1 and Females=2, but numbers can’t be ranked nor do they have mathematical meaning.

Ordinal data helps us to identify something as less or more, but “because the level of measure is still categorical, the exact level of difference cannot be identified” (Houser, 2018, p. 292). Examples of ordinal data would be answers to the question such as “How satisfied are you with the nursing care provided?” Predetermined answers such as; not satisfied, satisfied, and very satisfied can be labeled as: 1, 2 and 3. We can conclude that 3 is better than 2, 2 is better than 1, but we can’t determine how much better. The ordinal data can be ranked but not qualified.

Houser (2018) states that “interval and ratio are recorded on a continuous scale that has equal intervals between all entries” and “data collected on interval or ratio levels result in numbers that can be subjected to many mathematical procedures, including mean, standard deviation, variance, and evaluation of the distribution (Houser, 2018, p.292)”. Example of interval data would be measuring patients’ temperature. The interval between each degree is constant, can be ranked and provides numerical data that can be qualified. The example of ratio level data could be length of walk CABAG patients take on each post-operative day. Unit of measure is numeric, it has exact order, and difference between each value is known and constant. Difference between 10-20 feet is same as difference between 20-30 feet, and so on. Collected data is numerical and tells us not only that 30 feet is longer than 20 feet, but that difference between two is 10 feet. Numerical data collected can be used to calculate in example relationship between length of walks and number of hospital days, or length of walks and frequency of post-operative complications such as pneumonia or DVT.

Angela

Resources:

Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Burlington, MA: Jones & Bartlett Learning.

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