Factor analysis details
Reliability of the 41-item MLCDP
The Cronbach’s alpha reliability of the 41-item MLCDP was 0.84, indicating good reliability. The “corrected items-total correlation” values for 7 items were <0.2 (not discriminating well) and therefore deleted, changing Cronbach’s alpha from 0.84 to 0.85. The 7 deleted items were B9 “I decided to become self-employed”, C3 “I decided not to have any children”, D6 “I wanted to move abroad but decided not to”, D7 “I decided to move from one country to another”, D15 “I decided to wear a wig/toupee”, E4 “I decided to be more physically active” and E5 “I decided to give up driving”.
Factor Analysis of 34-item MLCDP
Exploratory factor analysis of the remaining 34 items of the MLCDP was carried out on the sample of 210 . In the correlation matrix for the 34 items of the MLCDP there were correlation coefficients r = 0.3 and above  between several variables, confirming the suitability of the data for carrying out factor analysis. The Kaiser-Meyer-Olkin (KMO) [21, 22] value was 0.73, greater than the recommended minimum of 0.6  and Bartlett’s test of sphericity  was significant at p = 0.0001 confirming the sampling adequacy of the data. Kaiser’s criterion and Cattell’s scree test statistical techniques were used to determine how many factors could be extracted.
There were 12 components with Eigenvalues >1, that were retained for further analysis . These 12 components explained 68.6% of the variance (Table 5) . The component matrix for the 34 items MLCDP showed the loadings of 34 items on 12 components. Most of the items loaded strongly (0.3 and above) on the first six components and these items were considered for future analysis. 20 items had values of >0.40.
In the scree plot  (Figure 1), four components were retained for further analysis. There is a sharp drop after the first factor indicating that the first factor accounts for most of the variance (10.5%). Using the factor rotation technique, the varimax rotated component matrix  revealed that after rotation, thirty items loaded strongly (>0.40) (Table 6) on one of the four components, indicating a strong correlation between the item and the corresponding components. Most items loaded on component 1 (11), component 2 (7) and component 3 (7). Only two items failed to load on any component, and were therefore deleted.
The four factors identified from the rotation accounted for 38.4% of the total variance, along with their percentage of variance explained. The first four factors accounted for 10.5%, 10.2%, 9.8% and 7.8% of the total variance explained. After rotation, the pattern of the percentage of variance of individual components and their cumulative percentage changed from the total variance explained earlier. However, cumulative total variance explained (38.4%) did not change after rotation.
To summarise this first factor analysis, in the rotated component matrix, two items failed to load into any component. There were two items with weak loading (<0.4) and one item loaded on multiple components with no significant difference in their values. These five items were removed, leaving a 29-item MLCDP.
Factor Analysis of 29-item MLCDP
Factor analysis of the 29-item MLCDP was carried out to see whether these remaining items fitted well together in their components. From the scree plot examination (Figure 2) three factors were extracted for further analysis. This scree plot was relatively improved compared to the previous scree plot where four factors were extracted. Varimax rotation was applied, confirming the initial structure of the scale (Table 7). All 29 items loaded to 3 extracted components. 26 items loaded highly (0.4 and above), 3 items loaded weakly (<0.4). Six items loaded on two components, and for 3 of these, the values on two components were very close (weak complex variables). Five or more strongly loading items (0.5 or above) are desirable to create a solid factor . Component 1 (factor) consisted of 12 items of which only 2 items loaded weakly (range = 0.31 to 0.69). Component 2 consisted of 10 items with factor loading ranging from 0.46 to 0.67, with no weak loading. Component 3 consisted of 7 items with loadings ranging from 0.38 to 0.72, with only one weak loading. Total variance explained of the extracted component, demonstrated that the 3 factors accounted for 36.5% of the total variance. Although there were 5 more items in the first rotation and four components were extracted, there was not much difference compared to the percentage of total variance explained after the first rotation (38.4%). The first factor accounted for the highest proportion of variance, 12.7%. The second and third factors accounted for a similar proportion of the variance, 11.9% and 11.8% respectively.
The majority of items were close to each other in their corresponding components. For example, component 1 consisted of 12 items of which 9 items dealt with MLCDs related to social and physical aspects of patients’ lives. Six of the 29 items that weakly loaded or had complex variables were considered for deletion, leaving a 23-item MLCDP. Therefore as a result of factor analysis 18 MLCDP items were suggested for deletion from the original 41-item MLCDP, but were considered again at the next refinement stage of the scale. Items which did not conceptually fit in their extracted corresponding factors were also discussed at this stage.
In order to ensure that all perspectives were considered in the decisions relating to retention and deletion of items, factor analysis of the full original 41-item MLCDP was also carried out. Items were deleted after the final varimax rotation based on the same criteria used for the previous analysis. The final rotated component matrix revealed that four items failed to load on any component, five items loaded weakly (<0.4) and two items loaded on two components with little difference between their values. These 11 deleted items were compared with the 18 items deleted as a result of the factor analysis of the 32-item MLCDP (Table 8). Nine of the items were the same, which supported the initial factor analysis approach.