For each transit site, impact scores are calculated from the survey data results, and are as displayed as shown in Tables and (CTA Red Line), Tables and (CTA Blue Line), Tables and (Combined CTA Rail) Tables and (Sun Tran, Albuquerque), and Tables and (GLTC, Lynchburg, VA). First, data for whether or not a customer has experienced a problem with each attribute is cross-tabulated with mean overall satisfaction. Thus, for example as shown in Table , the mean overall satisfaction of those CTA Red Line customers (sample size=300) who have experienced a problem with "trains being overcrowded". | CHAPTER 8. AN ILLUSTRATION OF COMPARATIVE QUANTITATIVE RESULTS USING ALTERNATIVE ANALYTICAL TECHNIQUES Based on TCRP B-11 Field Test Results CTA CHICAGO ILLINOIS RED LINE SERVICE 8A. CTA Red Line - Computation of Impact Scores For each transit site impact scores are calculated from the survey data results and are as displayed as shown in Tables and CTA Red Line Tables and CTA Blue Line Tables and Combined CTA Rail Tables and Sun Tran Albuquerque and Tables and GLTC Lynchburg VA . First data for whether or not a customer has experienced a problem with each attribute is cross-tabulated with mean overall satisfaction. Thus for example as shown in Table the mean overall satisfaction of those CTA Red Line customers sample size 300 who have experienced a problem with trains being overcrowded within the last 30 days is while the mean overall satisfaction of those customers who have not experienced a problem with trains being overcrowded is . The gap score is the difference between the two means . The percent of Red Line customers who have experienced a problem with trains being overcrowded within the last 30 days is as shown in Table . To combine the effects of these two results we multiply the gap score by the problem occurrence rate .753 to arrive at an overall impact score of for the attribute. Impact scores for each attribute are then placed in descending order Table and the results are a display of the most problematic service attributes from top to bottom. The logical assumption is that reducing the percent of customers who have a negative experience with the impact or driver attributes will have the greatest possible upward effect on overall satisfaction with the transit system. However Table shows a more complete picture from the data. The darkly shaded cells show the attributes that are above the median rank for each category. The ranking columns with ranks of 1 to 10 for
