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INDEXES, SCALES, & TYPOLOGIES
COMPOSITE SCORES INDEXES, SCALES, & TYPOLOGIES
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WHY USE COMPOSITE SCORES
May not be a “clear” single indicator Offers an increase in variability Makes the analysis of data more “efficient”
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Index or Scale Index Scale
Most “common” Cumulative score (perform mathematical operation Scale Assign values to a “pattern” of responses (note: not all items are “equal” Note: both indexes and scales are rank-order composite measures
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Index Construction Item Selection Scoring Empirical Examination
Face validity Unidimensionality General vs Specific Variability Scoring Variability vs “Adequate number of cases in categories Item weighting (equal/unequal) in the index Empirical Examination Bivariate vs Multivariate Missing Data Validation
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Scales and Scaling Empirically based Intensity of particular items
Responses are “scaled (not the survey questions/items Intensity of particular items Is there a pattern in how the items tap a concept?
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Scale Construction Not all items are “equal” Examples:
“intensity structures” among items assurance of “ordinality” Examples: Thurstone Scale Bogardus Social Distance Scale Likert Scale Semantic Differential Guttman Scale
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Typologies Researcher can summarize the “overlap/intersection of two or more variables Typically driven by some combination of “theory” and “data” Life-course criminality Probation example Difficult to use a typology as the dependent variable Question: why do particular cases “fit” in a particular typology (predicting typologies)
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Using SPSS to Create an Index
Locate the data file Identify the variables Define the “New Variable” Define the “mathematical operation” Select “Transform” Select “Compute” Enter commands Save file
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Characteristics of Items
Recoding of some items may be necessary Not all items (statements/questions) are in the same “direction” Managing missing data “Don’t Know” is problematic
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Verification of Composite Scores
Factor Analysis Reliability Coefficient Item-Item Correlation Item-Total Correlation
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