1. LECTURE 4
ATTITUDE SCALING

2. THURSTONE SCALING
Post WWI work on interests and attitudes during 1920s- both industrial and psychological research interests
Thurstone proposed model based on jnd
Assumed interval scale possible for respondents to respond to different stimuli
Developed procedure to generate about 20 statements that a respondent would agree or disagree with, sum the items based on their interval scale value (positive or negative items, scored appropriately)

3. THURSTONE SCALING Example: Attitude toward Abortion
10 statements positive toward abortion
10 statements negative toward abortion
Respondent agreeing with positive statement receives 1 point for each
Respondent disagreeing with negative statement receives 1 point each
Possible range

4. THURSTONE SCALING
A. Select single concept, idea, or construct for scaling
eg. War, marriage, abortion, mathematics
B. Collect statements about the concept: non-factual, opinion-oriented C. Select about for analysis. eg:
I like arithmetic most of the time.
Abortions should never be performed under any circumstances.
War is usually a good thing, everything considered.

5. THURSTONE SCALING
D. Place statements along 11 point continuum from (-) 1= most negative statement to (+) 11= most positive statement, with 6= neutral or nonjudgmental statement.
- use subjects to do placement
- evaluate distribution of each statement:

6. THURSTONE SCALING Median of item as scale value:
for example, statement: Abortions should never be performed. n: 
%ile
score

7. THURSTONE SCALING
for example, statement: Abortions should be performed only to save the life of the mother. n: 
%ile
score

8. THURSTONE SCALING Variability: Eliminate items with ranges > 6 or 7
Examine conditional distributions of adjacent or close items:
Give items to respondents to endorse each statement (+) agree or (-) disagree
Examine joint endorsements of one item (a) with another (b), using an index such as Ia,b = nab / nb

9. THURSTONE SCALING
The distribution of the aIb ‘s should decrease around item a on either side of its scale value;
that is, items with similar scale values should have a high similarity index, while items further away on the scale should have scale values that drop away with distance.
Throw out items with poor characteristics.

10. ItemScale Value = 5.5 I n d e x of S i m l a r t y

11. LIKERT SCALING
Renses Likert (1930’s) researched the Thurstone procedure
- Placement, scale valuation procedure is cumbersome
- Likert replaced it with:
1=strongly disagree
2=disagree
3=uncertain
4=agree
5=strongly agree

12. LIKERT SCALING Give items to sample of target population
Use classical techniques to select items:
item mean, SD, interitem correlation
Theoretical justification: items are samples of the normal distribution shifted along the 1-5 continuum so that the mean is at the scale value

13. Item with scale value 1.5

14. Item with scale value 3.0

15. RATING SCALES Derivatives of Likert scaling
Alternative adjective set (1..5)
Requires distributional validation
Even # points is problematic
Less well investigated

16. RATING SCALE VALUES Number of scale values: 1 to 5 based on Likert
7, 9, or 11 can be useful for finer discriminations
Dependent on population, concept being assessed

17. SEMANTIC DIFFERENTIAL
Osgood, Suci, & Tannenbaum
Bipolar adjective pairs
Represent:
Strength: strong-weak, heavy-light
Value: good-bad, useful-useless
Activity: fast-slow, hot-cold
Two factors usually found: Evaluation and Activity/Strength

18. SOME ADDITIONAL IDEAS NON-NORMAL DISTRIBUTIONS: POISSON AND COUNT DATA
FREQUENCY
SCORE

19. SOME ADDITIONAL IDEAS
NON-NORMAL DISTRIBUTIONS:
POISSON AND COUNT DATA

20. DISTRIBUTIONAL ISSUES
CENSORED: PART OF THE NORMAL (eg. LIKERT ITEMS)- SUMMING SHOULD CREATE NORMAL
INDIVIDUAL ITEMS CAN BE ANALYZED AS CENSORED NORMAL SCORES
OF INTEREST IN RELIABILITY AND VALIDITY STUDIES