Clinical Simulation in Nursing (2013) 9, e35-e36
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Making Sense of Methods and Measurement: Levels of Measurement for Quantitative Research Susan Prion, EdD, RN, CNEa,*, Katie Anne Adamson, PhD, RNb a b
School of Nursing and Health Professions, University of San Francisco, San Francisco, CA 94117-1080, USA University of Washington Tacoma, Tacoma, WA 98402, USA
The purpose of research is to answer questions. The type and format of the research question are essential to the success of the project and influence the equally important type and format of the answer. In this column, we describe the levels of measurement used to measure quantitative research variables. In a future column, we will explore the types of answers possible for qualitative research. There are four levels of measurement for answering quantitative research questions. They are identified as nominal, ordinal, ratio, and interval. Ratio and interval levels are very similar, with the exception of a fixed zero point, and so are often referred to as ratioeinterval, giving us only three levels. Nominal data refers to numbers that serve to classify responses into discreet, mutually exclusive categories. The actual categories used do not really function as numbers but serve to identify and separate information using numbers. For example, a zip code of 60604 refers to a part of Chicago, Illinois, within Cook County. The zip code 30302 identifies an area of Atlanta, Georgia, a part of Fulton County. Chicago is not twice as nice as Atlanta (30302 2 ¼ 60604), just as Atlanta is not half as nice as Chicago. These numbers, like phone numbers, area codes, patient room numbers, and a host of other nominal variables, serve solely to identify one from another and cannot be used for computation. Commonly used nominal variables include race, sex, ethnicity, student level (e.g., freshman or senior), clinical specialty, and assigned unit.
* Corresponding author:
[email protected] (S. Prion).
Ordinal data are also known as rankings. Items in a group are prioritized according to some defined criterion. Even though the numbers convey some order to the list, the numerals used are still arbitrary. For example, a student scores a 95 out of 100 on a test. The next highest scores are 50 and 45, well below the passing standard of 80. The students are ranked 1, 2, and 3 on the exam, but the differences between student 1 and student 2 (9550 ¼ 45) and student 2 and student 3 (5045 ¼ 5) are very different. The distance between the scores is not consistent or regular. Ordinal variables are used for Apgar scores, some pain scales, and student satisfaction surveys such as the National League for Nursing’s Student Satisfaction and SelfConfidence in Learning scale. Interval data have the following characteristics: the data are ordered, and the distance between numbers is meaningful. For example, the distance between a weight of 50 pounds and 100 pounds (50 pounds) is the same as the difference between 100 and 150 pounds (50 pounds). Interval data have an arbitrary zero point, meaning that the point signifying no amount depends on the variable being measured. Celsius, Fahrenheit, and Kelvin scales have stable distances between numbers, but the zero points vary depending on the temperature scale. Ratio data, like interval data, are ordered and have a constant distance between numbers, but ratio data have a nonarbitrary zero point. This practically means that there can be a real absence of the ‘‘something’’ you are measuring. Blood pressure is a common example of ratio data because the zero point must be the same for all blood pressure scales.
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Levels of Measurement
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The level of measurement used for the research variable determines the type and robustness of the statistical techniques that can be used to analyze the data. Nominal and ordinal data do not allow for very sophisticated calculations, and ratioeinterval data are usually preferred because the choice of data analysis strategies is much greater. This means that, before starting a study, the researcher must make sure that the expected data analysis plan matches the level of measurement for the collected data. Sadly, some researchers discover that it is impossible to collect the same data a second time if they were not collected in the proper format initially.
Helpful Resources Broyles, R. W. (2005). Fundamentals of Statistics in Health Administration. Sudbury, MA: Jones and Bartlett. Cowell, J. (2007). Statistics Basics: A Resource Guide for Healthcare Managers. Marblehead, MA: HCPRO. Horton, L. (2011). Calculating and Reporting Healthcare Statistics (3rd ed.). Chicago, IL: Ahima. Polit, D. (2009). Statistics and Data Analysis for Nursing Research. New York, NY: Prentice Hall. Prion, S. K. (2012). Basic data analysis techniques for DNP nurse leaders. In K. T. Waxman (Ed.), Financial Management for DNP Graduates (pp. 199-215). New York: Springer, in press.
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