Page 91 - DMGT404 RESEARCH_METHODOLOGY

Unit 5: Measurement and Scaling Techniques

I strongly like it +2 Notes
I like it +1
I am indifferent 0
I dislike it -1

I strongly dislike it -2
In this manner, ranking can be obtained by asking the respondent their level of acceptability.
One can then combine the individual ranking and get a collective ranking of the group.

Interval scale uses the principle of "equality of interval" i.e., the intervals are used as the basis for
making the units equal assuming that intervals are equal.
It is only with an interval scaled data that researchers can justify the use of the arithmetic mean
as the measure of average. The interval or cardinal scale has equal units of measurement thus,
making 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
we can carry out on data collected in this form. It is possible to add or subtract a constant to all
of the scale values without affecting the form of the scale but one cannot multiply or divide the
values. It can be said that two respondents with scale positions 1 and 2 are as far apart as two
respondents with scale positions 4 and 5, but not that a person with score 10 feels twice as
strongly as one with score 5. Temperature is interval scaled, being measured either in Centigrade
or Fahrenheit. We cannot speak of 50°F being twice as hot as 25°F since the corresponding
temperatures on the centigrade scale, 100°C and -3.9°C, are not in the ratio 2:1.
Interval scales may be either numeric or semantic.

Characteristics

1. Interval scales have no absolute zero. It is set arbitrarily.
2. For measuring central tendency, mean is used.

3. For measuring dispersion, standard deviation is used.
4. For test of significance, t-test and f-test are used.
5. Scale is based on the equality of intervals.
Use: Most of the common statistical methods of analysis require only interval scales in order
that they might be used. These are not recounted here because they are so common and can be
found in virtually all basic texts on statistics.

5.1.3 Interval Scale

Interval scale is more powerful than the nominal and ordinal scales. The distance given on the
scale represents equal distance on the property being measured. Interval scale may tell us "How
far the objects are apart with respect to an attribute?" This means that the difference can be
compared. The difference between "1" and "2" is equal to the difference between "2" and "3".
Interval scale uses the principle of "equality of interval" i.e., the intervals are used as the basis for
making the units equal assuming that intervals are equal.
It is only with an interval scaled data that researchers can justify the use of the arithmetic mean
as the measure of average. The interval or cardinal scale has equal units of measurement thus,
making it possible to interpret not only the order of scale scores but also the distance between

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