# Data Levels of Measurement

A variable has one of four different levels of measurement: Nominal, Ordinal, Interval, or Ratio.  (Interval and Ratio levels of measurement are sometimes called Continuous or Scale).  It is important for the researcher to understand the different levels of measurement, as these levels of measurement, together with how the research question is phrased, dictate what statistical analysis is appropriate.  In fact, the Free download below conveniently ties a variable’s levels to different statistical analyses.

# Four Different Levels of Measurement

In descending order of precision, the four different levels of measurement are:

Nominal–Latin for name only (Republican, Democrat, Green, Libertarian)

Ordinal–Think ordered levels or ranks (small–8oz, medium–12oz, large–32oz)

Interval–Equal intervals among levels (1 dollar to 2 dollars is the same interval as 88 dollars to 89 dollars)

Ratio–Let the “o” in ratio remind you of a zero in the scale (Day 0, day 1, day 2, day 3, …)

The first level of measurement is nominal level of measurement.  In this level of measurement, the numbers in the variable are used only to classify the data.  In this level of measurement, words, letters, and alpha-numeric symbols can be used.  Suppose there are data about people belonging to three different gender categories. In this case, the person belonging to the female gender could be classified as F, the person belonging to the male gender could be classified as M, and transgendered classified as T.  This type of assigning classification is nominal level of measurement.

The second level of measurement is the ordinal level of measurement.  This level of measurement depicts some ordered relationship among the variable’s observations.  Suppose a student scores the highest grade of 100 in the class.  In this case, he would be assigned the first rank.  Then, another classmate scores the second highest grade of an 92; she would be assigned the second rank.  A third student scores a 81 and he would be assigned the third rank, and so on.   The ordinal level of measurement indicates an ordering of the measurements.

The third level of measurement is the interval level of measurement.  The interval level of measurement not only classifies and orders the measurements, but it also specifies that the distances between each interval on the scale are equivalent along the scale from low interval to high interval.  For example, an interval level of measurement could be the measurement of anxiety in a student between the score of 10 and 11, this interval is the same as that of a student who scores between 40 and 41.   A popular example of this level of measurement is temperature in centigrade, where, for example, the distance between 940C and 960C is the same as the distance between 1000C and 1020C.

The fourth level of measurement is the ratio level of measurement.  In this level of measurement, the observations, in addition to having equal intervals, can have a value of zero as well.  The zero in the scale makes this type of measurement unlike the other types of measurement, although the properties are similar to that of the interval level of measurement.  In the ratio level of measurement, the divisions between the points on the scale have an equivalent distance between them.

The researcher should note that among these levels of measurement, the nominal level is simply used to classify data, whereas the levels of measurement described by the interval level and the ratio level are much more exact.

Related pages:

Data Levels and Measurement

Data Analysis Plan

Statistics Solutions can assist with your quantitative analysis by assisting you to develop your methodology and results chapters. The services that we offer include:

Data Analysis Plan

Edit your research questions and null/alternative hypotheses

Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references

Justify your sample size/power analysis, provide references

Explain your data analysis plan to you so you are comfortable and confident

Quantitative Results Section (Descriptive Statistics, Bivariate and Multivariate Analyses, Structural Equation Modeling, Path analysis, HLM, Cluster Analysis)

Clean and code dataset

Conduct descriptive statistics (i.e., mean, standard deviation, frequency and percent, as appropriate)

Conduct analyses to examine each of your research questions

Write-up results

Provide APA 6th edition tables and figures

Explain chapter 4 findings

Ongoing support for entire results chapter statistics

Please call 727-442-4290 to request a quote based on the specifics of your research, schedule using the calendar on t his page, or email [email protected]