Scale (social sciences)
In the social sciences, scaling is the process of measuring or ordering entities with respect to quantitative attributes or traits. For example, a scaling technique might involve estimating individuals' levels of extraversion, or the perceived quality of products. Certain methods of scaling permit estimation of magnitudes on a continuum, while other methods provide only for relative ordering of the entities. The level of measurement is the type of data that is measured. The word scale, including in academic literature, is sometimes used to refer to another composite measure, that of an index. Those concepts are however different . Scale construction method. It is possible that something similar to one's scale will already exist, so including those scale(s) and possible dependent variables in one's survey may increase validity of one's scale. Data types. The type of information collected can influence scale construction. Different types of information are measured in different ways. Composite measures. Composite measures of variables are created by combining two or more separate empirical indicators into a single measure. Composite measures measure complex concepts more adequately than single indicators, extend the range of scores available and are more efficient at handling multiple items. In  addition to scales, there are two other types of 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. Indexes are constructed by accumulating scores assigned to individual attributes, while scales are constructed through the assignment of scores to patterns of attributes. While indexes and scales provide measures of a single dimension, typologies are often employed to examine the  intersection of two or more dimensions. Typologies are very useful analytical tools and can be easily used as independent variables, although since they are not unidimensional it is difficult to use them as a dependent variable. Comparative and non comparative scaling. With comparative scaling, the items are directly compared with each other (example: Does one prefer Pepsi or Coke?). In noncomparative scaling each item is scaled independently of the others. (Example: How does one feel about Coke?) Scale evaluation. 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 one 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 is supposed to measured. 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 (Campbell and Fiske, 1959; Krus and Ney, 1978). The coefficient of reproducibility indicates how well the data from the individual measures included in  the scale can be reconstructed from the composite scale.