TECHNICAL NOTE 2 DATA QUALITY


RELIABILITY

1 The estimates in this release are based on information obtained from a sample survey (EAS) and from administrative data collected by the Australian Taxation Office (ATO). Any collection of data can be affected by factors that affect the reliability of the resulting statistics, regardless of the methodology used. These factors result in non-sampling error. In addition to non-sampling error, sample surveys are also subject to inaccuracies that arise from the fact that a sample was selected rather than conducting a census. This type of error is called sampling error.


Sampling error

2 The majority of data contained in this publication have been obtained from a sample of businesses. As such, these data are subject to sampling variability; that is, they may differ from the figures that would have been produced if the data had been obtained from all businesses in the population. One measure of the likely difference is given by the standard error, which indicates the extent to which an estimate might have varied by chance because the data were obtained from only a sample of units. There are about two chances in three that a sample estimate will differ by less than one standard error from the figure that would have been obtained if all units had been included in the collection, and about nineteen chances in twenty that the difference will be less than two standard errors.

3 Sampling variability can also be measured by the relative standard error (RSE), which is obtained by expressing the standard error 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 percentage errors likely to have occurred due to the effects of random sampling, and this avoids the need to refer also to the size of the estimate. Selected data item RSEs for the Transport, Postal and Warehousing industry division level for Australia are shown in the table below. Detailed relative standard errors can be made available on request.

4 To illustrate, the estimate of total income from transport services for Water Transport in 2010-11 was $3,758m. The RSE of this estimate is shown as 0.9%, giving a standard error of approximately $33.8m. Therefore, there are two chances in three that, if all units had been included in the survey, a figure in the range of $3,724.2m and $3,791.8m would have been obtained, and nineteen chances in twenty (i.e., a confidence interval of 95%) that the figure would have been within the range of $3,690.4m and $3,825.6m.

			Relative standard errors
			Income from transport services	Total income	Total transport running expenses	Total expenses
			%	%	%	%
Transport, postal and warehousing
	Road transport		2.1	1.7	2.9	1.5
		Road freight transport	2.5	2.0	3.2	1.8
		Road passenger transport	2.1	1.5	2.9	1.4
	Rail transport		0.0	0.0	0.0	0.0
	Water transport		0.9	0.9	1.0	0.9
	Air and space transport		0.6	0.5	1.0	0.5
	Other transport		0.9	0.9	1.9	1.0
	Postal and courier pick-up and delivery services		1.8	1.3	2.9	1.4
	Transport support services		2.6	2.2	4.2	2.3
	Warehousing and storage services		1.1	0.9	1.1	0.8
	Total transport, postal and warehousing		1.0	0.8	1.4	0.8

Non-sampling error

5 Error other than that due to sampling may occur in any type of collection, whether a full census or a sample, and is referred to as non-sampling error. All data presented in this publication are subject to non-sampling error. Non-sampling error can arise from inadequacies in available sources from which the population frame was compiled, imperfections in reporting by providers, errors made in collection such as in recording and coding data and errors made in processing data. Errors also occur when information cannot be obtained from all businesses selected. The imprecision due to non-sampling error cannot be quantified and should not be confused with sampling variability, which is measured by the standard error.

6 Although it is not possible to quantify non-sampling error, every effort is made to reduce it to a minimum. Collection forms are designed to be easy to complete and assist businesses to report accurately. Efficient and effective operating procedures and systems are used to compile the statistics. The ABS compares data from different ABS and non-ABS sources, to ensure consistency and coherence.

7 Differences in accounting policy and practices across businesses and industries can also lead to some inconsistencies in the data used to compile the estimates. Although much of the accounting process is subject to standards, there remains a great deal of flexibility available to individual businesses in the accounting policies and practices that they adopt.

8 The above limitations are not meant to imply that analysis based on these data should be avoided, only that the limitations should be borne in mind when interpreting the data presented in this publication. This publication presents a wide range of data that can be used to analyse business and industry performance. It is important that any analysis be based upon the range of data presented rather than focusing on one variable.

QUALITY INDICATORS

9 In the 2010-11 Economic Activity Survey (EAS), there was an 87.5% response rate from all businesses in the Transport, Postal and Warehousing industry division that were surveyed and found to be operating during the reference period. Data were imputed for the remaining 12.5% of operating businesses. This imputation contributed 6.9% to the estimate of Income from transport services for the Transport, Postal and Warehousing industry division.

DATA COMPARABILITY

Comparison with other ABS statistics

10 In some cases estimates given in this publication may differ slightly from those from other sources. These differences may be the result of sampling or non-sampling error, or may result from differences in scope, coverage, definitions or methodology.

Historical comparisons

11 Care should be taken when comparing the 2010-11 estimates with transport related estimates released as part of Australian Industry, 2009-10 (cat. no 8155.0) because of changes in question wording, sample design and conceptual definitions. Refer to In this issue for more detail on data comparability between the two years.