CHART-SF Properties

All CHART subscale scores could be reduced by fewer questions to reach 90% explained variance except Economic Self-Sufficiency, which using the main variables could only explain 45%. A possible explanation for the lack of predictive ability for the Economic subscale was the alarmingly high rate of refusal for economic questions. 40% of the people failed to respond to either the question about income or expenditures not covered by insurance. In light of the missing data, it was determined that those questions would change with the addition of response categories rather than open-ended questions. These overall findings show that:

• Using the fewest number of items to predict the largest amount of variation explained is a reasonable method for shortening a questionnaire.

• CHART-SF sub-scales closely approximate the scores of the subscales gathered by the Original CHART.

• CHART-SF takes less time to administer than the Original CHART

CHART-SF is a valuable tool for determining handicap for populations in which time is at a minimum. Although using a tool with fewer items may decrease the precision for smaller groups, the use of CHART-SF in larger populations will obviate the lack of precision by the change in confidence intervals

The following tables (388K) represents all the questions asked as a part of CHART by sub-scale and entered into the regression model. The order of the questions represents the order the variables entered in the regression, meaning the most predictive questions are listed first. The percent explained variance is located on the right side of the tables by R-squared. Each of the CHART-SF scoring for the indicated variable is located in between the table and the graph for the respective subscale. Below each of the tables is a scatter plot of the Original CHART subscale score by the CHART Short Form subscale score. A 'sunflower' in which each petal represents one case marks each point. Additionally, a regression line is shown along with 95% confidence intervals.