NURS 8200 Discussion 1: Levels of Measurement

NURS 8200 Discussion 1: Levels of Measurement

NURS 8200 Discussion 1: Levels of Measurement

Research Question

In sleep apnea patients (P), how does the treatment of OSA with CPAP (I) compared with no treatments (C) reduce the risk of cardiovascular diseases (O)?

Independent and Dependent Variables

Independent variable: Treatment of OSA with CPAP. It does not vary. It is not dependent upon other variables

Dependent variable: Cardiovascular disease. Cardiovascular disease is the dependent variable because not everyone has this disease. Its value depends on changes in the independent  

Level of Measurement of Both the Independent and Dependent variables

            Measurement involves assigning numbers to qualities of people or objects to designate the quantity of the attribute. There are four different levels of measurement: nominal, ordinal, interval, and ratio (Polit & Beck, 2020).

            Ordinal measurement will be used for my independent variable. Rationale: This will be the best measurement to rate patients’ improvement using CPAP from 1 to 10. The ordinal measurement will be a great tool when evaluating the Epworth Sleepiness Scale ( ESS) for my sleep apnea patients to compare the ESS score before the use of CPAP and while using Something measured on an ordinal scale does have an evaluative connotation (Bond C.M, 2019).

            Interval measurement will be used for my dependent variable. I will use the interval measurement to get greater analytic flexibility, more robust statistical options, and a more significant amount of information than at the lower levels (Polit & Beck, 2020).

Discuss considerations of analyzing data related to each variable based on its level of measurement. What are the advantages, or disadvantages, of the levels of the variables of measurements?

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When analyzing information from a quantitative study, we are often dealing with numbers; therefore, it is essential to understand the

NURS 8200 Discussion 1 Levels of Measurement
NURS 8200 Discussion 1 Levels of Measurement

source of the numbers. The term variable defines a specific item of information collected in a study. Examples of variables include age, sex or gender, ethnicity, exercise frequency, weight, treatment group, and blood glucose. Each variable will have a group of categories, which are referred to as values, to help describe the character of an individual study participant. For example, the variable “sex” would have values of “male.” and “female” (Bond C.M, 2019).

An ordinal variable implies that the categories can be placed in a meaningful order, as would be the case for exercise frequency (never, sometimes, often, or permanently). Nominal-level Furthermore, ordinal-level variables are also referred to as categorical variables because each category in the variable can be separated from the others. The categories for an interval variable can be placed in a meaningful order, with the interval between consecutive types also having meaning. Age, weight, and blood glucose can be considered interval variables and ratio variables because the ratio between values has meaning (e.g., a 15-year-old is half the age of a 30-year-old). Interval-level and ratio-level variables are also referred to as continuous variables because of the underlying continuity among categories (Simpson, 2018).

As we progress through the levels of measurement from nominal to ratio variables, we gather more information about the study participant. The amount of information that a variable provides will become important in the analysis stage because we lose information when variables are reduced or aggregated, a common practice that is not recommended. For example, if age is lowered from a ratio-level variable (measured in years) to an ordinal variable (categories of < 65 and ≥ 65 years), we lose the ability to make comparisons across the entire age range and introduce error into the data analysis (Simpson, 2018).


Bond C.M. ( 2019). The research jigsaw: how to get started. Can J Hosp Pharm., 67(1):28–30.

Simpson S. H. (2018). Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study. The Canadian journal of hospital pharmacy, 68(4), 311–317.

Polit, D. F., & Beck, C. T. (2020). Essentials of nursing research: Appraising evidence for nursing practice. Lippincott Williams & Wilkins.