---

 

Computing New Variables

Because a concept is usually much richer than any single measure of it, both reliability and validity may be enhanced by developing a number of measures of the same underlying concept and then combining them into a scale or index.  

 

Sometimes an index can be created simply by adding the values of the individual measures that make it up.  A number of interest groups employ just such an approach in developing "legislative scorecards" for members of the United States Congress and various state legislatures.  A liberal group, the Americans for Democratic Action (ADA), has been doing this for many years. Its ratings for the U.S. House of Representatives and the U.S. Senate are determined by choosing key roll calls, and then calculating for each member the percentage of votes on these roll calls cast in a liberal direction.  The American Conservative Union (ACU) produces a similar ratings, but with higher scores indicating a conservative record.  ADA and ACU scores are not perfect mirror images of one another since each organization chooses its own key votes, and each decides which positions are "liberal" or "conservative."

 

Another type of additive index is the Likert scale.  This scale is constructed from a series of related statements with which respondents can choose from a range of  possible responses, such as 1) “strongly agree,” 2) “agree,” 3) “neutral,” 4) “disagree,” and 5) “strongly disagree.”  Responses can be added to form a composite measure.  For example, one might measure attitudes toward President Bush by asking for responses to a series of 10 statements in Likert form.  A respondent who strongly agreed with all pro-Bush statements and strongly disagreed with all anti-Bush statements would receive a score of 50, while a respondent giving all strongly anti-Bush responses would receive a score of 10

 

With indexes such as the ADA, ACU, or Likert scales, the question arises as to whether all of the items included in the index really measure at least part of the underlying concept.  One way to test this is to make the generally reasonable assumption that the composite index is more valid and reliable than any one of the items that make it up.  We can then correlate each individual measure with the score on the composite index.  A low correlation would indicate that a particular item is not closely related to the index.  That item could then be dropped, and the index recalculated. 

 

For Further Study

 

Arnold, William E., James C. McCroskey, and Samuel V. O. Prichard, 1967. “The Likert-Type Scale,” Today's Speech,15,  31-33.  Available online at http://www.jamescmccroskey.com/publications/25.htm.  Accessed October 24, 2003.

 

Fitzgerald, John, “Stems and Scales,” http://www.coolth.com/likert.htm.  Accessed: October 24, 2003.