2.Survey: Measurement and Scaling

2.1 Introduction


Measurement – assigning numbers or other symbols to characteristics of objects according to certain pre-specified rule

– one-to-one correspondence between the numbers and characteristics being measured

– the rules for assigning numbers should be standardized and applied uniformly

– rules must not change over objects or time



Scaling – involves creating a continuum upon which measured objects are located.

Primary Scales of Measurement


  • numbers serve as labels for identifying and classifying objects
  • not continuos


  • numbers indicate the relative positions of objects
  • but not the magnitude of difference between them


  • differences between objects can be compared
  • zero point is arbitrary

Ratio a.k.a. metric:

  • zero point is fixed
  • ratios of scale values can be computed

Primary Scales of Measurement

Scale Basic Characteristics Common Examples Marketing Examples

Permissible Statistics






Numbers identify

and classify objects


Social security numbers, numbering of football players


Brand numbers, store types sex, classification


Percentages, mode


Chi-square, binomial test




Numbers indicate the relative positions of the objects but not the magnitude of differences between them


Quality rankings, ranking of teams in tournament


Preference rankings, market position, social class


Percentile, median


Rank-order correlation, Friedman ANOVA




Differences between objects can be compared; zero point is arbitrary


Temperature (Fahrenheit, Centigrade)


Attitudes, opinions, index numbers


Range, mean, standard deviation


Product-moment correlations, t-tests, ANOVA, regression, factor analysis




Zero point is fixed; ratios of scale values can be compared


Length, weight, time, money


Age, income, costs, sales, market shares


Geometric mean, harmonic mean


Coefficient of variation


Classification of Scaling Techniques

Comparison of Scaling Techniques

Comparative Scales: 

  • involve the direct comparison of stimulus objects.
  • data must be interpreted in relative terms
  • have only ordinal and rank-order properties

Non-comparative Scales:

  • each object is scaled independently
  • resulting data is generally assumed to be interval or ratio scaled

– nature of the research

– variability in the population

– statistical considerations

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