Items and responses. Likert scales require a minimum of two response categories, such as "agree" or "disagree." Generally, though, three or five categories are used. Using five categories gives a greater range of choices for respondents and increases the range or variability of total scores for the scale. But there is a limit to the number of categories one might use. Research has shown that five or seven categories will produce the best data and that increasing the number of categories above seven confuses some respondents. As you see in Box 7.3, we decided to use five commonly used response categories. We used SA for strongly agree, A for agree, Un for uncertain, D for disagree, and SD for strongly disagree.

Scoring. Each category is given a numerical value, which becomes the score for that statement. The process of assigning values to attributes of indicators is called scoring. For items with five response categories, the categories are given assigned values from 0 to 4 or from 1 to 5. Either set of numbers can be used. Values assigned to the responses, however, have to be adjusted depending on whether the item expresses a favorable or unfavorable viewpoint. The set of items in Box 7.3 includes both favorable and unfavorable items for measuring attitudes toward wives working outside the home. Item 1 gives a favorable view and the response, "strong agree," is given a value of 4 (or 5). Item 2 presents an unfavorable view. To be consistent, "strongly disagree" is given a value of 4 (or 5). And so on with the remaining items. Switching scoring, depending on whether the item is favorable or unfavorable, allows us to add scores for each item to get a total score for all items.

In scoring, a decision has to be made whether to give the most favorable responses the high or low item score. For the set of items in Box 7.3, we gave the highest score (4) to the most favorable or positive responses and 0 to the most negative ones. By scoring this way, high total scores indicate high positive views. In the illustration (Box 7.3) the total or composite scale was 23, rather high in relation to a possible high score of 28 (7 times 4 for the most favorable response to each item). Results then can be easily interpreted: Higher scores will represent higher levels of whatever is being measured. Presenting results in this way will also help readers of your report quickly understand what you are reporting.

Deriving initial composite scores. In Box 7.3, we show the values assigned to each response category so you see how scores are assigned. When used in a questionnaire, however, scores for items would not be shown. Seeing the numbers, some respondents may try to answer to get what they think is a high or low score. The interviewer or respondent would see only empty brackets in front of each response category.

Item analysis. Initial total scores are only tentative scores because another step, called an item analysis, is necessary to select the items to be included in the final index. Either of two ways can be used.   One way relies on calculating the coefficient of correlation between the scores for each item and the composite scores. Unless you have access to a computer with a statistical analysis package this method will require doing a large number of tedious calculations. The other method is based on comparing the mean (the average) or median responses for each item for respondents who had the lowest and highest composite scores. These two extreme groups are usually defined as the lowest and highest 25% of the composite scores.  

As a way of illustration, imagine that 125 persons answered the 7 items listed in Box 7.3 and their total scores assumed the distribution shown in Table 7.1. In this figure, "X" stands for the values of the total scores, which range from a low of 5 to a high of 24. The letter "f" stands for the frequency or how many times each score occurred. With this distribution, we want to find the lowest 25% and the highest 25% of the scores.

Twenty five percent of 125 is 31.25 which can be rounded to 31. So, we want to select the lowest and highest 31 scores. To find the lowest 31 scores, we add frequencies for the scores beginning with the lowest one, which was 5, and continue until we reach or come as close to 31 as we can. Adding down the frequency column we get an accumulated frequency of 22 at the score of 15. When we add the frequency of 11 for the score of 16 we get an accumulated frequency of 33. This is more than we want, but is as close as we can get to 31. Repeating this process from the high end of the distribution, we keep adding frequencies, beginning with the frequency of 2 for the score of 24, the highest score, to the frequencies for each of the next lower scores.   At the score of 21, we have an accumulated frequency of 29. When we add the frequency for the next lower score of 20, we would go way over the desired number of 31. Therefore,    we use the score of 21 as the cut off point for the high scoring group. Thus, the lowest scoring group includes scores from 5 through 16 and the highest scoring group scores from 21 through 24.