Developing a proper scale helps us to measure the attitude of newly recruited employees. Each scale has unique properties. Some scales can only establish an association between variables and limited in their mathematical properties.
Other scales have more extensive mathematical properties, and some can also hold out the possibility of establishing cause-and-effect relationships between variables.
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Levels of Measurement:
There are four levels of measurement. These are nominal, ordinal, interval and ratio.
1. Nominal Scale:
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This scale classifies individuals, companies, products, brands or other entities into categories without any order. This is why this scale is often referred to as a categorical scale. This scale simply counts the frequency of the cases assigned to the various categories.
An example of a nominal scale
| Which of the following FMCG products you buy at least once in a month? (Please tick) |
| Toothpaste □_____________ Shampoo □___________ Soap □_______________ |
| Deodorant □______________ Hair oil □______ Detergent □___________ |
These numbers have no arithmetic properties and the only measure is the mode. This is because; using this scale we simply try to get a set of frequency counts. Testing of hypothesis can be done based on these data in the nominal form.
2. Ordinal Scales:
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Ordinal scales involve ranking along the continuum of the characteristic being scaled. To take an example, we may ask the respondents to rank some brands of shampoo in order of their preferences.
An example of an ordinal scale
| Order of preference | Brand |
| 1 | Sun Silk |
| 2 | Rejoice |
| 3 | Head & Shoulder |
| 4 | Clinic Plus |
| 5 | Pantene |
This table gives the researcher the order of preference but do not provide any information about how much more one brand is preferred to another. This is what we call interval between any two brands. In addition to the information that are made available through a nominal scale, using ordinal scale we can compute the median, quartile and percentile. We can also compute the rank correlation coefficient (commonly known as Spearman’s rank correlation) and Kendall’s coefficient of concordance.
3. Interval Scales:
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The interval or cardinal scale has equal units of measurement, which makes it possible to interpret not only the order of scale scores but also the distance between them. However, it must be recognized that the zero point on an interval scale is arbitrary and is not a true zero. This of course has implications for the type of data manipulation and analysis. Interval scales may be either numeric or semantic.
4. Ratio Scales:
The highest level of measurement is a ratio scale. This scale has the properties of an interval scale together with a fixed origin or zero point. Examples of variables, which are ratio scaled, include weights, lengths and time. Ratio scales permit the researcher to compare both differences in scores and the relative magnitude of scores. For instance, the difference between five and ten minutes is the same as that between ten and fifteen minutes, and ten minutes is twice as long as five minutes.