I am doing a secondary analysis of the British Election study and am testing attitudinal variables (Likert scale) for their suitability in creating an attitudinal scale. I have selected 15 variables which I am testing for suitability for an Internationalism scale. Having run a “maximum likelihood” factor analysis, SPSS has established that these variables are contributing to four distinct factors. The literature and text books I have access to talk about how to decide how many items and factors to retain, but I can’t find any information regarding what I should do next.

Would someone be able to tell me whether what I propose to do is completely wrong and point in the right direction (either with their response or pointing me towards a text book that might help):

Because SPSS found four factors, I will create four subscales for the four the factors I have identified. I will then take an average score of these four factors and combine to create an overall score for internationalism. Is this correct so far? Should I only keep items that load greater than .6?

Once I recode new variables for each subscale, should I run a new factor analysis to see if they load on a single factor? Is this nonsense and would such a test yield identical results as the first factor analysis? (My brain hurts trying to figure this out).

Some subscales are comprised of two variables, others from 7, etc, providing I take the mean score for each factor, is this okay? It seams problematic to take an overall average of all items together, as those factors comprised of more items would load more on the overall scale, right? Furthermore, is it possible to weigh each subscale differently with regards to the overall scale? So for instance, I regard the “cultural openness” scale to be more important than another factor per theory, can I multiply this factor by day, 3, and then take the overall average (so this factor would contribute 3 times as much to the overall scale)? E.g 4 factors, A,B,C,D, so score=(3A+B+C+D)/6?

Finally, my reasoning for wanting to create a unidimensional scale is because I am expected to run multivariate regressions on it (so I'm not really interested in investigating four separate subscales that I believe to be contributing to a single concept). Is it statistically sound to score 5-level variables into scores of (0, 25, 50, 75, 100) so I can get a percentage score, so when analyzing the regression tables I can talk about differences in terms of percentage points on my internationalism scale? Alternatively, can I calculate a summative score of 1-5 and then multiply by 20 to get a percentage score? Is this entering dodgy territory?

Thanks so much in advance, any help, pointers or clarification would be enormously appreciated - TJ