Scaling is the measurement of a variable in such a way that it can be expressed on a continuum. Rating your preference for a product from 1 to 10 is an example of a scale.
With comparative scaling, the items are directly compared with each other (example : Do you prefer Pepsi or Coke?). In noncomparative scaling each item is scaled independently of the others (example : How do you feel about Coke?).
~ What level of data is involved (nominal, ordinal, interval, or ratio)?
Scales should be tested for reliability, generalizability, and validity. Generalizability is the ability to make inferences from a sample to the population, given the scale you have selected. Reliability is the extent to which a scale will produce consistent results. Test-retest reliability checks how similar the results are if the research is repeated under similar circumstances. Alternative forms reliability checks how similar the results are if the research is repeated using different forms of the scale. Internal consistency reliability checks how well the individual measures included in the scale are converted into a composite measure.
Scales and indexes have to be validated. Internal validation checks the relation between the individual measures included in the scale, and the composite scale itself. External validation checks the relation between the composite scale and other indicators of the variable, indicators not included in the scale. Content validation (also called face validity) checks how well the scale measures what it is supposed to measure. Criterion validation checks how meaningful the scale criteria are relative to other possible criteria. Construct validation checks what underlying construct is being measured. There are three variants of construct validity. They are convergent validity, discriminant validity, and nomological validity. The coefficient of reproducibility indicates how well the data from the individual measures included in the scale can be reconstructed from the composite scale.Composite measures
Indexes are similar to scales except multiple indicators of a variable are combined into a single measure. The index of consumer confidence, for example, is a combination of several measures of consumer attitudes. A typology is similar to an index except the variable is measured at the nominal level. Scaling, indexes, and typologies are all examples of composite measures. Data types
The type of information collected can influence scale construction. Different types of information are measured in different ways.
Scale Construction Decisions
~ What will the results be used for?
~ Should you use a scale, index, or typology?
~ What types of statistical analysis would be useful?
~ Should you use a comparative scale or a noncomparative scale?
~ How many scale divisions or categories to use (1 to 10; 1 to 7; -3 to +3)?
~ Odd or even number of divisions - odd gives neutral center value; even forces respondents to take a non-neutral position
~ The nature and descriptiveness of the scale labels?
~ The physical form or layout of the scale? (graphic, simple linear, verticle, horizontal)
~ Forced versus optional response? Comparative Scaling Techniques
Non-comparative Scaling Techniques
Scale Evaluation