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Interval Scale in Research Methodology

The interval scale has all the properties of ordinal and nominal scale plus the benefit that it provides equal intervals among categories. The values in the interval scale can take positive or negative values. This property enables the use of statistical analysis on data measured using interval scale. Age, IQ, temperature and dates are few examples of an interval scale. Variables measured at interval scale are called as interval variables and scaled variables. Likert scale is most commonly used in the interval scale, especially when this scale is used in social science research.

Advantages

  • This scale has constant equal distances between each successive values.
  • This scale has wide scope due to its characteristic that it can categorize the data in equal intervals.
  • Mean and standard deviation can be applied to data measured using this scale. The range can also be calculated to obtain the data dispersion.

Disadvantages

  • This scale has all the characteristics of a complete measuring scale except that it does not have an absolute zero.

Statistical Analysis of Interval Scale

Selection of the right statistical technique and data analysis depends heavily on the variables to be studied and the measurement scales used in the research. Therefore, the selection of right measuring scale is crucial to the success of the research. Data that has been measured using this scale can be treated by one of the several statistical techniques including mean, standard deviation, regression, correlation, range, analysis of variance etc. Studentized range and coefficient of variation cannot be calculated because ratios have no meanings in this scale.

References

  1. Campbell MJ, Machin D, Wiley J. Medical Statistics: A Commonsense Approach. Vol. 2. London: Wiley; 1993.
  2. Stevens SS. Bobbs-Merrill, On the Theory of Scales of Measurement. College Division; 1946.
  3. Marateb H. Reza, et al, Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies. Journal of Research in Medical Sciences; 2014 Jan; 19(1): 47–56.

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