RELIABILITY OF THE ESTIMATES
1 When interpreting the results of a survey it is important to take into account factors that may affect the reliability of the estimates. Estimates provided in this publication are subject to non-sampling and sampling errors.
NON-SAMPLING ERRORS
2 Non-sampling errors may arise as a result of errors in the reporting, recording or processing of the data and can occur even if there is a complete enumeration of the population. These errors can be introduced through inadequacies in the questionnaire, treatment of non-response, inaccurate reporting by respondents, errors in the application of survey procedures, incorrect recording of answers and errors in data capture and processing.
3 The extent to which non-sampling error affects the results of the survey is difficult to measure. Every effort is made to minimise non-sampling error by careful design and testing of the questionnaire, efficient operating procedures and systems, and the use of appropriate methodology.
4 The BUIT survey is dynamic in nature and the concepts measured are subject to evolution and refinement over time. This results in regular changes to questions used to measure the various attributes and features of IT use. The potential impact of these changes on survey outputs are assessed during questionnaire testing and where these changes impact on data continuity, they are referred to in the publication commentary or Explanatory Notes.
5 The 2002-03 survey had a lower than usual response rate of approximately 88%. The average for the previous three survey periods was 93%. The lower response rate in 2002-03 was addressed by utilising improved and more comprehensive methods of imputation.
STANDARD ERRORS
6 The estimates presented in this publication are based on information obtained from a sample of businesses in the surveyed population. Consequently, the estimates are subject to sampling variability, that is, they may differ from the figures that would have been obtained if all units had been included in the survey. One measure of the likely difference is given by the standard error (SE), which indicates the extent to which an estimate might have varied by chance because only a sample was taken. There are about two chances in three that a sample estimate will differ by less than one SE from the figure that would have been obtained if a census had been conducted, and approximately 19 chances in 20 that the difference will be less than two SEs.
7 In this publication, sampling variability is measured by the relative standard error (RSE) which is obtained by expressing the SE as a percentage of the estimate to which it refers. The RSE is a useful measure in that it provides an immediate indication of the sampling error in percentage terms, and this avoids the need to refer also to the size of the estimate.
8 To illustrate, the estimated percentage of businesses with a web presence is 23% and the RSE is 2.7%, giving a standard error of 0.6 percentage points (2.7% of 23%). Therefore, there would be two chances in three that, if all units had been included in the survey, a figure in the range of 22.4% to 23.6% would have been obtained, and 19 chances in 20 (i.e. a confidence interval of 95%) that the figure would have been within the range of 21.8% to 24.2%. For more information about RSEs for estimates presented in this publication, please telephone the contact shown on the front page.
9 Most published estimates have RSEs less than 10%. Estimates that have a RSE between 10% and 25% are annotated with the symbol '^'. These estimates should be used with caution as they are subject to sampling variability too high for some purposes. Estimates with an RSE between 25% and 50% are annotated with the symbol '*', indicating that the estimates should be used with caution as they are subject to sampling variability too high for most practical purposes. Estimates with an RSE greater than 50% are annotated with the symbol '**' indicating that the sampling variability causes the estimates to be considered too unreliable for general use.
10 The sampling variability for estimates at the state/territory or industry level is higher than that for Australian level aggregates. Within states/territories, the sampling variability, and therefore the RSEs of estimates for Tasmania, Northern Territory and the Australian Capital Territory are higher than for other states. Survey estimates for these states should therefore be viewed with more caution than those for other states.
11 Estimates of RSEs for the 2002-03 key indicators shown in the Summary of Findings' tables are given below.
- Businesses using a computer: 0.6%
- Businesses using the Internet: 0.9%
- Businesses with a web presence: 2.7%
- Businesses who placed orders via the Internet or web: 2.4%
- Businesses who received orders via the Internet or web: 3.8%
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