The ordinal scale ranks the individuals, responses or objects into sub-categories and it also makes it possible to rank these sub-categories into some order of magnitude. This property of ordinal scale, to rank objects in some order, makes it different from the nominal scale. It does not quantify “how much” different in magnitude each sub-category is from the other. For example, in measuring the agreement among respondents towards any social program the researcher uses the sub-categories “agree”, do not agree” agreed to some extent”, the researcher cannot say that the difference between agree, do not agree and agreed to some extent is equidistant.
- It sub-categorizes values in the order of their magnitude. For example, the attitude of students towards a school program can be categorized in very effective, effective and not effective. The typical 5 or 7 point Likert scale is an example of ordinal scale. The use of Likert scale is very common in social sciences.
- This scale has greater scope for psychology and other social sciences research.
- This scale sorts the data in order but it does not quantify the relationship between different sub-categories.
- Because of lack of equal distances between scale points, arithmetic and statistical operations are not possible.
Analysis for ordinal scale
The researcher can apply median and mode to ordinal scale measurements but mean and standard deviation cannot be applied. Application of median is possible because data in ordinal scale is arranged in descending or ascending order. This arrangement of data allows finding out the median of the data where most values lie. On the other hand, in nominal scale, as data is not arranged in any order median cannot be applied. A median cannot be used in further mathematical calculations and therefore it cannot be used in statistical analysis. For nominal and ordinal scale mode can also be calculated and for nominal scale mode is the only value that can be calculated.
- Manikandan, S (2011). “Measures of central tendency: Median and mode”. Journal of Pharmacology and Pharmacotherapeutics. 2 (3): 214, 215. doi:10.4103/0976-500X.83300. PMC . PMID 21897729.
- Chrisman, Nicholas R. (1998). “Rethinking Levels of Measurement for Cartography”. Cartography and Geographic Information Science. 25 (4): 231–242. doi:10.1559/152304098782383043. ISSN 1523-0406. Retrieved 14 August 2015. – via Taylor & Francis
- Boone N. Harry (2012). Analyzing Likert Data. Journal of Extension. April 2012 Volume 50 Number 2 Article Number 2TOT2. West Virginia University.
- Gail M. Sullivan, MD, MPH and Anthony R. Artino, Jr, PhD. Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education. J Grad Med Educ. 2013 Dec; 5(4): 541–542. doi: 10.4300/JGME-5-4-18.