Data integrity

In a Formula One Racetrack Project, carried out by IFM Sports Marketing Surveys, the data were obtained by face-to-face and telephone interviews. As the questionnaire was developed and finalised, a preliminary plan was drawn up of how the findings could be analysed. The questionnaires were edited by a supervisor as they were being returned from Australia, Brazil, France, Germany, Italy, Japan, Spain and the UK. The questionnaires were checked for incomplete, inconsistent and ambiguous responses. Questionnaires with problematic responses were queried with supervisors in each of the eight countries. In some circumstances, the supervisors were asked to re-contact the participants to clarify certain issues. Thirty-five questionnaires were discarded because the proportion of incomplete or unsatisfactory responses rendered them too poor to use. This resulted in a final sample size of 2,050.
A codebook was developed for coding the questionnaires; this was done automatically as the questionnaire was designed using the SNAP software (www.snapsurveys. com). Data were transcribed by being directly keyed in as the telephone interview mode was conducted. In the face-to-face mode, data were transcribed using personal digital assistants (PDAs) as the interviews were conducted. The software has a built-in error check that identified out-of-range responses; 10% of the data were verified for other data entry errors. The data were cleaned by identifying logically inconsistent responses. Most of the rating information was obtained using five-point scales, so responses of 0, 6 and 7 were considered out of range and a code of 9 was assigned to missing responses. If an out-of-range response was keyed in, the SNAP software issued an audible warning and prohibited any continuation of data entry. New variables were created that were composites of original variables. Finally, a data analysis strategy was developed.

A patient arrives at the Emergency Room at Hello-Hospital about every 40 ± 19 minutes. Each patient will be treated by either Doctor Slipup or Doctor Gutcut. Twenty percent of the patients are classified as NIA (need immediate attention) and the test as CW (can wait). NIA patients are given the highest priority (3), see a doctor as soon as possible for 40 ± 37 minutes, but then their priority is reduced to 2 and they wait until a doctor is free again, when they receive further treatment for 30 ± 25 minutes and are then discharged. CW patients initially receive the priority 1 and are treated (when their torn comes) for 15 ± 14 minutes; their priority is then increased to 2, they wait again until a doctor is free and receive I 0 ± 8 minutes of final treatment, and are then discharged. Simulate for 20 days of continuous operation, 24 hours per day. Precede this by a 2-day initialization period to load the system with patients. Report conditions at times 0 days, 2 days, and 22 days. Does a 2-day initialization appear long enough to load the system to a level reasonably close to steady-state conditions? (a) Measure the average and maximum queue length of NIA patients from arrival to first seeing a doctor. What percent do not have to wait at all? Also tabulate and plot the distribution of this initial waiting time for NIA patients. What percent wait less than 5 minutes before seeing a doctor? (b) Tabulate and plot the distribution of total time in system for all patients. Estimate the 90% quantile-that is, 90% of the patients spend less than x amount of time in the system. Estimate x. (c) Tabulate and plot the distribution of remaining time in system from after the first treatment to discharge, for all patients. Estimate the 90% quantile. (Note: Most simulation packages provide the facility to automatically tabulate the distribution of any specified variable.)