Scale analysis (statistics)

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In statistics, scale analysis refers to a set of methods to analyse survey data, in which responses to questions are combined to measure a latent variable. These items can be dichotomous (e.g. yes/no, agree/disagree, correct/incorrect) or polytomous (e.g. disagree strongly/disagree/neutral/agree/agree strongly). Any measurement for such data is required to be reliable, valid, and homogeneous with comparable results over different studies.

Contents 1 Measurement models 1.1 Traditional models 1.2 Modern models based on Item response theory 1.3 Other models 2 References

[edit] Measurement models

Measurement is the assignment of numbers to subjects in such a way that the relations between the objects are represented by the relations between the numbers (Michell, 1990).

[edit] Traditional models

• Likert scale
• Reliability analysis, see also Classical test theory and Cronbach's alpha
• Factor analysis

[edit] Modern models based on Item response theory

• Guttman scale
• Mokken scale
• Rasch model
• (Circular) Unfolding analysis
• Circumplex model

[edit] Other models

• Latent class analysis
• Multidimensional scaling

[edit] References

• Michell, J (1990). An Introduction to the logic of Psychological Measurement. Hillsdales, NJ: Lawrences Erlbaum Associates Publ.