If you can define a reasonable similarity measure on the values, you can use any distance based algorithm, such as:
- Hierarchical clustering
- DBSCAN
- OPTICS
- K-Medoids (k-means for arbitrary distances)
Given that you only have 5 values, you could just manually define a similarity matrix for these 5 values; then decide on a combination rule to merge multiple attributes, e.g. mean.
Adilah,
Attitudes toward online reading can be assessed with components 1, 3, 4, and 5. Attitude cannot be assessed with component 2 because this component represents self-behavior and the response options for component two are about frequency of behaviors, not attitudes.
Before assessing attitudes, I recommend running a cronbach's alpha test of internal consistency on each set of questions representing the components. The outcomes will tell you whether responses to each question is adequately related to other questions within the same component. If one question does not seem to fit too well, consider dropping it from a component. Make sure that you reverse score negatively phrased items, if there are any, before running cronbach's alpha. Cronbach's alpha is found under Scale in the SPSS Analyze drop down list -- choose reliability analysis.
Next aggregate the outcomes within each component. In other words, sum the responses for each question and then divide by the number of questions. For example, if component 3 (anxiety) is composed of items/questions 1, 2, 3, 4, 5, 6, 7, 8, then for each participant add up all 8 scores and then divide by 8. This will give you an overall component score for anxiety. Your anxiety score can then be compared using factors like gender and race. Note that although the data are ordinal, as you pointed out, when you combine items into an overall score, it is appropriate to use parametric statistics with analyses on the overall component scores.
If you want to determine overall attitude you would need to repeat the above process combining all items except #2. Determining weak vs. strong influence of components on overall attitude could be tricky because an attitude in either the left or right direction can be equally strong. I suppose that components with overall scores closest to the mean (between disagree and agree) would qualify as those being the weakest because it suggests neutral attitude.
Best Answer
A frequency table is a good place to start. You can do the count, and relative frequency for each level. Also, the total count, and number of missing values may be of use.
You can also use a contingency table to compare two variables at once. Can display using a mosaic plot too.