Certain research data are qualitative in nature. Data on attitude, opinion or behavior of employees, customers, and sales persons etc., are qualitative. The terms “attitude” and “opinion” have frequently been differentiated in psychological and sociological investigations. A commonly drawn distinction has been to view an attitude as a predisposition to act in a certain way and an opinion as a verbalization of the attitude. Thus, a statement by a respondent that he prefers viewing color to black-and-white television programs would be an opinion expressing one aspect of the respondent’s attitude toward color television. Motivation, commitment, satisfaction, leadership effectiveness, etc involve attitude measurement based on revealed opinions. These qualitative data require **measurement scales** for being measured.

## Types of Measurement Scales used in Research

There are **four different scales of measurement used in research**; nominal, ordinal, interval and ratio. The rules used to assign numerals objects define the kind of scale and level of measurement. A brief account of each scaling type is given below;

**Nominal Scales**: Nominal scale is the simplest form of measurement. A variable measured on a nominal is one which is divided into two or more categories, for example, gender is categorized as male or female, a question as to whether a family owns a iPhone can be answered ‘Yes’ or ‘No’. It is simply a sorting operation in which all individuals or units or answers can be placed in one category or another (i.e. the categories are exhaustive). The essential characteristic of a nominal scale is that in terms of a given variable, one individual is different from another and the categories are discriminate (i.e. the categories are mutually exclusive). This characteristic of classification if fundamental to all scales of measurement. Nominal scales that consist only two categories such as female-male, agree-disagree,aware-unaware, yes-no, are unique and are called dichotomous scales. Such dichotomous nominal scales are important to researchers because the numerical labels for the two scale categories can be treated as though they are of interval scale value.**Ordinal Scales**: Ordinal scales have all the properties of a nominal scale, but, in addition, categories can be ordered along a continuum, in terms of a given criterion. Given three categories A, B and C, on an ordinal scale, one might be able to say, for e.g., that A is greater than B and B is greater than C. If numerals are assigned to ordinal scale categories, the numerals serve only as ranks for ordering observations from least to most in terms of the characteristic measured and they do not indicate the distance between scale that organizes observations in terms of categories such as high, medium and low or strongly agree, agree, not sure, disagree, and strong disagree.**Interval Scales**: Interval scales incorporate all the properties of nominal and ordinal scales and in addition, indicate the distance or interval between the categories. In formal terms one can say not only that A is greater than B and B is greater than C but also that (A-B)=(B-C) or (A-C)=(A-B)+(B-C). Examples of interval scale include age, income and investments. However, an interval scale is one where there is no absolute zero point. It can be placed anywhere along a continuum e.g., the age can be between 20 to 60 years and need not necessarily start from 0 years. This makes ratio comparison, that A is twice that of B or so wrong.**Ratio Scales**: A special form of interval scale is the ratio scale which differs in that it has a true zero point or a point at which the characteristic that is measured is presumed to be absent. Examples of ratio scales include, weight, length, income, expenditure and others. In each there is a concept of zero income, zero weight, etc. Since ratio scales represent a refinement of interval scales, generally these scales are not distinguished and both the terms are used inter-changeably.

Each of the above four types of scales have a unique method of measurement. Both nominal and ordinal scales consist of discrete number of categories to which numbers are assigned. Thus, a variable such as number of families owning a BMW or iPhone can only take values of 0, 1, 2 3 4 etc. It cannot have values such as 1.5 or 2.5 as the units are integers and indivisible. But interval and ratio scales take any value between two integers, as the variables are continuous. For example, given any ages however close, it is possible to find a third which lies in between. Interval and ratio scales are superior to normal and ordinal scales and a wealth of statistical tools can be employed in their analysis. The different statistical tools are related to these different **measurement scales in research**, in that there is usually a correspondence between mathematical assumptions of the statistical tool and the assumptions of the **scale of measurement**. Care must be always taken to match the tools used with the **scale of measurement** of variables and to use a method which implies a higher scale measurement than the variable allows.