TECHNICAL NOTE DATA QUALITY
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 in this publication are subject to both non-sampling and 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 reduce non-sampling error by careful design and testing of the questionnaire, efficient operating procedures and systems, and the use of appropriate methodology.
4 Some of the items collected in the Business Characteristics Survey are dynamic in nature and the concepts measured are subject to evolution and refinement over time. This is most evident in the items related to innovation statistics where substantial change has been made. As noted in the Explanatory Notes, changes have been made to the questions, survey scope and survey procedures, however, it is not possible to measure the impact of all of these changes on data quality.
5 While all attempts are made to ensure that the questions are unambiguous and not subject to misinterpretation, some of the concepts measured in the BCS require the provider to make a subjective judgement or assessment. It is not possible to accurately quantify the impact of these issues on data quality.
6 The 2006-07 Business Characteristics Survey had a response rate of 97%.
7 The difference between estimates obtained from a sample of businesses, and the estimates that would have been produced if the information had been obtained from all businesses, is called sampling error. The expected magnitude of the sampling error associated with any estimate can be estimated from the sample results. One measure of sampling error is given by the standard error (SE), which indicates the degree to which an estimate may vary from the value that would have been obtained from a full enumeration (the 'true' figure). There are about two chances in three that a sample estimate differs from the true value by less than one standard error, and about nineteen chances in twenty that the difference will be less than two standard errors.
8 An example of the use of standard error on the total proportion of innovating businesses is as follows. In this release, the estimated proportion of total innovating businesses is 32.4%. The standard error of this estimate was 0.95%. There would be about two chances in three that a full enumeration would have given a figure in the range 31.4% to 33.4%, and about nineteen chances in twenty that it would be in the range 30.5% to 34.3%. Detailed standard errors are available on request.
9 In this publication (and associated data cubes), indications of sampling variability are measured by relative standard errors (RSEs). The relative standard error is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer also to the size of the estimate. Relative standard errors are shown in the Relative Standard Error table in this section.
10 To avoid inconsistencies between the way very low and very high proportions are annotated, a value of 50% of the SE has been used in the calculation of RSE. Relative standard errors for estimates in this publication have been calculated using the actual standard error and the survey estimate (referred to as x) in the following manner:
Using the previous example, the standard error for the estimated proportion of innovating businesses was 0.95%. Multiplied by 100 and then divided by 50 gives an RSE calculated on this basis of 1.9%. It is these figures that appear in the table appended below.
For the tables in this publication (and associated data cubes), estimates with RSEs 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 RSEs 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.
For estimates of proportion the symbol '^' means that the estimate from full enumeration could lie more than a decile away so the estimate should be used with caution. For example, a proportion estimate of 30% annotated with '^' means the full enumeration value could lie beyond the range 20% to 40%. The symbol '*' means the estimate from full enumeration could lie more than a quartile away and is subject to sampling variability too high for most practical purposes. A proportion estimate of 30% annotated with '*' means the full enumeration value could lie beyond the range 5% to 55%. Proportion estimates annotated with the symbol '**' have a sampling error that causes the estimates to be considered too unreliable for general use.
Readers of this release should note that most of the data have an RSE of less than 10%.
Relative Standard Error - Summay indicators of Innovation - 2006-07
200 or more persons
|Estimated number of businesses as at 30 June 2007 |
|Businesses with introduced or implemented innovation (innovating businesses) |
|Businesses with innovative activity which was: |
|still in development |
|Businesses with any innovative activity (innovation-active businesses) |
This page last updated 25 August 2010