1. Indexes and Scales Why use a “composite” measure of a concept? ▫ There is often no clear “single” indicator ▫ Increase the range of variation ▫ Make data analysis more “efficient”

2. Indexes vs. Sclaes Index ▫ Simply “add up” single indicators to form a composite measure  Most common in social science Scale ▫ Assign score to pattern of responses (not all items are equal) Commonality: rank order composite measures

3. Index Construction Item selection  Face validity  Unidimensionality  General/specific  Variance considerations Scoring  Variance versus “adequate number of cases” in categories  Equal weight or not for the items in a composite index? Empirical examinations  Bivariate versus multivariate Dealing with missing data Index validation

4. Scales/Scaling Empirically driven  Responses can be scaled, not survey items/questions Reflect “intensity” of particular items  Is there a pattern in how the items tap a concept?  “easy and hard” questions

5. Scale Construction Key = not all indicators are equal ▫ “intensity structures” among indicators  Better assurance of “ordinality” Examples ▫ Bogardus Social Distance Scale ▫ Thurstone Scale ▫ Likert Scale ▫ Semantic Differential ▫ Guttman Scale

6. Typologies Allows a researcher to summarize the overlap/intersection of two or more variables ▫ Typically driven by some combination of theory and data  life-course criminality  probation officer example Difficult to use a typology as the dependent variable ▫ Why do particular people fall into a particular typology (predicting typologies).