Appendix B explains how to use the data obtained using the items in Appendix A. There are three sections. The first section concerns the scoring of the data. The second section concerns the computation of subjective norms and ∑nimi. The third section concerns the prediction of subjective norms from ∑nimi.
The first step is to score the subjective norm items. As an example, consider the first of these items from Appendix A. The blanks indicating that the participant believes that his or her important others think he or she should perform the behavior should be scored in the positive direction whereas those blanks indicating that the participant believes that his or her important others think he or she should not perform the behavior should be scored in the negative direction, with a neutral response getting a score of 0. Thus, a check mark on the first blank in the positive direction ("extremely") should be scored +3, the second blank ("quite") should be scored +2, the third blank ("slightly") should be scored +1, the fourth blank ("neutral") should be scored 0, the fifth blank ("slightly") should be scored -1, the sixth blank ("quite") should be scored -2, and the seventh blank ("extremely") should be scored -3. Similar scoring should be used for all of the subjective norm items.
Once the items have been scored and entered into a data file, the next step is to check on their internal consistency. This can be done in two ways. First, the items can be correlated (e.g., using the CORRELATIONS command on SPSS), and any items that are not highly correlated with the other items can be dropped out. Secondly, it is possible to compute Cronbach's alpha (e.g., using the RELIABILITY command on SPSS) to ensure that the items have high internal consistency. As a general rule of thumb, Cronbach's alpha should exceed .7. If Cronbach's alpha does not exceed .7, it might be desirable to drop out the worst item, which is the one that correlates least with the other items. Cronbach's alpha should then be re-computed to ensure that it exceeds .7.
Assuming that the items are internally consistent, the next step is to actually compute the subjective norm value. There are at least two ways of doing this. The first way, and the simplest way, is to compute the mean of all of the items that compose the subjective norm. This mean, then, is the participant's subjective norm score. The second way is to perform a factor analysis on the subjective norm items. The factor analysis should result in only one factor. If not, it suggests that one of the items is not consistent with the others and should be dropped (see foregoing paragraph). If there is only one factor, the factor score can be saved, and used as the subjective norm score. Typically, the correlation between these two ways of computing subjective norms exceeds .95, and so either method can be used.
Scoring the normative beliefs and motivations to comply
Like the subjective norm items, normative beliefs should be scored from +3 (indicating an extreme belief that the important other is in favor of the behavior) to -3 (indicating an extreme belief that the important other is not in favor of the behavior). In Appendix A, there were 5 important others (doctor, spouse, father, mother, and best friend). After scoring, each important other should have a score between +3 and -3. The motivations to comply are already numbered and so the number that corresponds to the marked blank is the participant's score on the item. Thus, each of the participant's important others should have two scores, a normative belief score and a motivation to comply score.
The next step is to use these scores to compute ∑nimi. This is done as follows. First, multiply the normative belief score for the first important other by the corresponding motivation to comply. For instance, suppose that the normative belief pertaining to the participant's doctor is +2 and the participant's motivation to comply with his or her doctor is +3. In that case, the product is 2 x 3 = 6. Similar computations for each of the important others should result in a single product for each of them. For example, in the case of the five important others mentioned in Appendix A, there should be five products. Finally, these products are added together and the result is a single number that represents ∑nimi for that participant.
For an example of how to do these computations, suppose that a participant's normative belief scores are +3, +2, -2, 0, and -1, respectively, for each of his or her important others. In addition, suppose that this participant's motivations to comply are +5, +2, +3, +1, and +3, respectively. In that case, ∑nimi = 15 + 4 - 6 + 0 - 3 = +10.
These numbers can be tabled as follows.
|Sum of Products
Predicting Subjective Norms from Normative Beliefs and Motivations to Comply
If normative beliefs and motivation to comply determine subjective norms, then ∑nimi should correlate with subjective norms (e.g., the CORRELATION SPSS command can be used). Assuming that an acceptable correlation is obtained, and the definition of "acceptable" is a matter of judgment, it might be useful to know which normative beliefs and motivations to comply are most important. The easiest way to do this is to perform a multiple regression analysis where each normative belief-motivation to comply product is entered separately to predict subjective norms (the REGRESSION SPSS command can be used). The multiple regression analysis will result in regression weights for each of the products. Those products with the highest regression weights are likely to provide the most fruitful area for intervention.
As an example, suppose that the correlation between ∑nimi and subjective norms is .65, thereby indicating that normative beliefs and motivations to comply are good predictors of subjective norms. But the researcher wishes to know which particular normative beliefs and motivations to comply are most important for determining subjective norms. So the researcher performs the recommended multiple regression analysis, and finds regression weights for the normative belief-motivation to comply products pertaining to the doctor (.10), spouse (.61), father (.02), mother (.06), and best friend (.03). In this example, the normative belief-motivation to comply products pertaining to spouses better predict subjective norms than do any of those pertaining to other important others. Therefore, it would be better to focus intervention efforts on normative beliefs and motivations to comply that pertain to people's spouses than on those that pertain to doctors, fathers, mothers, and best friends.