1. Index and Scale Similarities: Both are ordinal measures of variables. Both rank order units of analysis in terms of specific variables. Both are measurements based on more than one data item.

2. Index and Scale Scoring Differences: Index: accumulate scores assigned to individual attribute (i.e., additive index) Scale: assign scores to patterns of responses. –May differentiate items by intensity –Example: voting record (liberal v. conservative) –Weight votes by the content of bill

3. Constructing an Index Select items for a composite index. Examine empirical relationships. Assign scores for responses. Handle missing data. Validate the index (internal/external).

4. Selecting Items Criteria Face (logical) validity Unidimensionality General or specific Variance

5. Empirical Relationships Established when respondents’ answers to one question help predict how they will answer other questions. If two items are empirically related, we can argue that each reflects the same variable, and both can be included in the same index.

6. Assign Scores for Responses Two basic decisions: Decide the desirable range of the index scores. Decide whether to give each item in the index equal weight or different weights.* *some scholars have noted there is no a priori way of determining the appropriate weight for an index (Fleischmann, et al. 1992).

7. Ways to Handle Missing Data Exclude cases with missing data from the construction of the index and the analysis. Treat missing data as one of the available responses. Analyze missing data to interpret the meaning. –Who tends to be missing? –The poor? Minorities? Women? Elderly? Etc.

8. Validate the Index Item Analysis - internal validation. External validation - ranking of groups on the index should predict the ranking of groups in answering similar or related questions. Beyond the scope of this class right now –But we could run some cross-tabulations, Chi- Square, Crombach Alpha

9. Let’s create an index GSS http://faculty.unlv.edu/kfernandez/gss.sav http://faculty.unlv.edu/kfernandez/gss.sav Looking at this dataset what could be used to create an index?

10. Example Index for technology use Do you use a computer? Do you use email? Do you use the internet? Do you use WWW? Can we add these together to create an index?

11. Step 1: run a frequency distribution of the questions Step 2: do we need to do any recoding? –Doesn’t look like it. –All are coded 0 to 1 in a logical manner. –Missing data seems to be coded properly Step 3: Compute new variable –Go to pull down menu “TRANSFORM” –Select “Compute Variable” –Choose target variable name –Select variables

12. Is this a good index Is there variation in the index –yes What does the distribution look like? Histogram

14. Techniques of Scale Construction Bogardus social distance scale - measures the willingness of people to participate in social relations. Likert scaling - uses standardized response categories (can help identify degree) Guttman scaling - uses an empirical intensity structure

15. Creating a Scale Weigh use computer use, email use, internet use, www use differently We could add more items –Hours using email, internet, etc. –The hours used helps measure intensity But remember, just adding more items doesn’t make it a scale

16. Typology The classification of observations in terms of the attributes on two or more variables. For example The classification of newspapers using two variables: ideology and population density could lead us to the following typology liberal-urban; liberal-rural; conservative- urban; or conservative-rural newspapers