What is the rationale for creating a multi-item index? What issues should a researcher be aware of when creating one?

Question

What will be an ideal response?

Answer 1

As with nominal variables, the different values of a variable measured at the ordinal level must be mutually exclusive and exhaustive. They must cover the range of observed values and allow each case to be assigned no more than one value. Often, questions that use an ordinal level of measurement simply ask respondents to rate their response to some question or statement along a continuum of, for example, strength of agreement, level of importance, or relative frequency. Like variables measured at the nominal level, variables measured at the ordinal level in this way classify cases in discrete categories and so are termed discrete measures.
A series of similar questions may be used instead of one question to measure the same concept. The set of questions in the Favorable Attitudes Toward Antisocial Behavior Scale in Exhibit 4.5 is a good example. In such a multi-item index or scale, numbers are assigned to reflect the order of the responses (such as 1 for “very wrong,” 2 for “wrong,” 3 for “a little bit wrong,” and 4 for “not wrong at all”); these responses are then summed or averaged to create the index score. One person’s responses to the five questions in Exhibit 4.5 could thus range from 5 (meaning they said each behavior is “very wrong”) to 20 (meaning they said each behavior is “not wrong at all”). However, even though these are numeric scores, they still reflect an ordinal level of measurement because the responses they are based on involve only ordinal distinctions. As with nominal variables, the different values of a variable measured at the ordinal level must be mutually exclusive and exhaustive. They must cover the range of observed values and allow each case to be assigned no more than one value. Often, questions that use an ordinal level of measurement simply ask respondents to rate their response to some question or statement along a continuum of, for example, strength of agreement, level of importance, or relative frequency. Like variables measured at the nominal level, variables measured at the ordinal level in this way classify cases in discrete categories and so are termed discrete measures.