TECHNICAL NOTE DATA QUALITY
INTRODUCTION
1 Since the estimates in this publication are based on information obtained from occupants of a sample of dwellings, they are subject to sampling variability. That is, they may differ from those estimates that would have been produced if all dwellings 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 of dwellings was included. There are about two chances in three (67%) that a sample estimate will differ by less than one SE from the number that would have been obtained if all dwellings had been included, and about 19 chances in 20 (95%) that the difference will be less than two SEs. Another measure of the likely difference is the relative standard error (RSE), which is obtained by expressing the SE as a percentage of the estimate.
2 Due to space limitations, it is impractical to print the SE of each estimate in the publication. Instead, a table of SEs is provided to enable readers to determine the SE for an estimate from the size of that estimate (see table T1). The SE table is derived from a mathematical model, referred to as the 'SE model', which is created using data from a number of past Labour Force Surveys. It should be noted that the SE model only gives an approximate value for the SE for any particular estimate, since there is some minor variation between SEs for different estimates of the same size.
CALCULATION OF STANDARD ERROR
3 An example of the calculation and the use of SEs in relation to estimates of persons is as follows. Table 5 shows the estimated number of female part-time employees in main job was 1,868,900. Since this estimate is between 1,000,000 and 2,000,000, table T1 shows that the SE for Australia will lie between 11,600 and 17,150 and can be approximated by interpolation using the following general formula:
4 Therefore, there are about two chances in three that the value that would have been produced if all dwellings had been included in the survey will fall within the range 1,852,500 to 1,885,300 and about 19 chances in 20 that the value will fall within the range 1,836,100 to 1,901,700. This example is illustrated in the diagram below.
5 In general, the size of the SE increases as the size of the estimate increases. Conversely, the RSE decreases as the size of the estimate increases. Very small estimates are thus subject to such high RSEs that their value for most practical purposes is unreliable. In the tables in this publication, only estimates with RSEs of 25% or less are considered reliable for most purposes. Estimates with RSEs greater than 25% but less than or equal to 50% are preceded by an asterisk (e.g. *3.4) to indicate they are subject to high SEs and should be used with caution. Estimates with RSEs of greater than 50%, preceded by a double asterisk (e.g. **0.3), are considered too unreliable for general use and should only be used to aggregate with other estimates to provide derived estimates with RSEs of less than 25%.
MEANS AND MEDIANS
6 The RSEs of estimates of mean and median weekly earnings (see paragraphs 18 and 19 of the Explanatory Notes) are obtained by first finding the RSE of the estimate of the total number of persons contributing to the estimate (see table T1) and then multiplying the resulting number by the following factors:
mean weekly earnings: 0.9
median weekly earnings: 1.0
7 The following is an example of the calculation of SEs where the use of a factor is required. Table 5 shows an estimate of 1,868,900 female part-time employees in main job and table 4 shows mean weekly earnings for the same group as $374. The SE of 1,868,900 was calculated previously as 16,400. To convert this to an RSE we express the SE as a percentage of the estimate, or 16,400/1,868,700 = 0.9%.
8 The RSE of the estimate of mean weekly earnings is calculated by multiplying this number (0.9%) by the appropriate factor shown in paragraph 6 (in this case 0.9): 0.9 x 0.9 =0.8%. The approximate SE of this estimate of mean weekly earnings of female part-time employees in main job is therefore 0.8% of $374, that is about $2.99. Therefore, there are two chances in three that the mean weekly earnings for female part-time employees that would have been obtained if all dwellings had been included in the survey would have been within the range $371.01 to $376.99, and about 19 chances in 20 that it would have been within the range $368.02 to $379.98.
PROPORTIONS AND PERCENTAGES
9 Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. A formula to approximate the RSE of a proportion is given below. This formula is only valid when x is a subset of y.
10 Considering the example from the previous page, the 1,868,900 females who were part-time employees in their main job represent 47% of the 3,979,400 female employees. The SE and RSE of 1,868,900 were calculated previously as 16,400 and 0.9% respectively. The SE for 3,979,400 calculated by interpolation is 25,100, which converted to a RSE is 25,100/3,979,400 =0.6%. Applying the above formula, the RSE of the proportion is:
11 Therefore, the SE for the proportion (47%) is 0.2 percentage points (=(47/100)x 0.5). Therefore, there are about two chances in three that the proportion of female part-time employees was between 46.8% and 47.2%, and 19 chances in 20 that the proportion is within the range 46.6% to 47.4%.
DIFFERENCES
12 Published estimates may also be used to calculate the difference between two survey estimates (of numbers or percentages). Such an estimate is subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x-y) may be calculated by the following formula:
13 While this formula will only be exact for differences between separate and uncorrelated characteristics or subpopulations, it is expected to provide a good approximation for all differences likely to be of interest in this publication.
STANDARD ERRORS
T1 STANDARD ERRORS OF ESTIMATES |
| |
| | | | | | | | | AUST. | |
| NSW | Vic. | Qld | SA | WA | Tas. | NT | ACT | SE | RSE | |
Size of estimate (persons) | no. | no. | no. | no. | no. | no. | no. | no. | no. | % | |
| |
100 | 290 | 250 | 250 | 150 | 160 | 100 | 100 | 140 | 100 | 100 | |
200 | 380 | 330 | 330 | 210 | 220 | 140 | 150 | 180 | 180 | 90 | |
300 | 440 | 390 | 390 | 250 | 260 | 180 | 200 | 200 | 240 | 80 | |
500 | 540 | 470 | 470 | 300 | 330 | 220 | 260 | 230 | 350 | 70 | |
700 | 620 | 540 | 540 | 350 | 380 | 260 | 310 | 260 | 430 | 61 | |
1,000 | 710 | 620 | 610 | 400 | 440 | 300 | 360 | 280 | 540 | 54 | |
1,500 | 830 | 730 | 710 | 470 | 520 | 340 | 430 | 320 | 690 | 46 | |
2,000 | 920 | 810 | 790 | 530 | 590 | 370 | 480 | 340 | 820 | 41 | |
2,500 | 1 000 | 900 | 850 | 550 | 650 | 400 | 500 | 350 | 900 | 36 | |
3,000 | 1 100 | 950 | 900 | 600 | 700 | 400 | 550 | 400 | 1 000 | 33 | |
3,500 | 1 150 | 1 000 | 950 | 650 | 750 | 450 | 550 | 400 | 1 100 | 31 | |
4,000 | 1 200 | 1 050 | 1 000 | 700 | 750 | 450 | 600 | 400 | 1 200 | 30 | |
5,000 | 1 300 | 1 150 | 1 100 | 750 | 850 | 500 | 650 | 450 | 1 300 | 26 | |
7,000 | 1 500 | 1 300 | 1 250 | 850 | 950 | 550 | 800 | 500 | 1 550 | 22 | |
10,000 | 1 700 | 1 500 | 1 400 | 950 | 1 100 | 650 | 1 000 | 600 | 1 800 | 18 | |
15,000 | 2 000 | 1 750 | 1 600 | 1 100 | 1 250 | 800 | 1 350 | 750 | 2 100 | 14 | |
20,000 | 2 200 | 1 950 | 1 800 | 1 200 | 1 400 | 950 | 1 650 | 850 | 2 300 | 12 | |
30,000 | 2 600 | 2 300 | 2 050 | 1 450 | 1 600 | 1 250 | 2 250 | 1 100 | 2 650 | 9 | |
40,000 | 2 850 | 2 550 | 2 250 | 1 700 | 1 750 | 1 500 | 2 850 | 1 350 | 2 900 | 7 | |
50,000 | 3 100 | 2 800 | 2 450 | 1 900 | 1 950 | 1 750 | 3 400 | 1 500 | 3 100 | 6 | |
100,000 | 4 050 | 3 600 | 3 400 | 2 900 | 3 050 | 2 600 | 6 050 | 2 050 | 4 000 | 4 | |
150,000 | 4 800 | 4 350 | 4 250 | 3 700 | 4 100 | 3 200 | 8 600 | 2 350 | 4 700 | 3 | |
200,000 | 5 550 | 5 200 | 5 100 | 4 400 | 4 950 | 3 650 | 11 100 | 2 450 | 5 300 | 3 | |
300,000 | 7 100 | 6 800 | 6 800 | 5 450 | 6 250 | 4 300 | 16 050 | 2 550 | 6 350 | 2 | |
500,000 | 9 950 | 9 300 | 9 550 | 6 900 | 7 950 | 5 150 | . . | 2 550 | 8 100 | 2 | |
1,000,000 | 14 950 | 13 700 | 13 500 | 9 000 | 10 050 | 6 250 | . . | . . | 11 600 | 1 | |
2,000,000 | 21 350 | 19 350 | 16 550 | 11 000 | 11 400 | . . | . . | . . | 17 150 | 1 | |
5,000,000 | 31 500 | 28 550 | 17 350 | 13 000 | 11 500 | . . | . . | . . | 29 250 | 1 | |
10,000,000 | 39 750 | 36 450 | 15 250 | . . | . . | . . | . . | . . | 39 200 | - | |
15,000,000 | . . | . . | . . | . . | . . | . . | . . | . . | 44 050 | - | |
| |
. . not applicable |
- nil or rounded to zero (including null cells) |
T2 LEVELS AT WHICH ESTIMATES HAVE RSE'S OF 25% AND 50% (a) |
| |
| NSW | Vic. | Qld | SA | WA | Tas. | NT | ACT | Aust. | |
| no. | no. | no. | no. | no. | no. | no. | no. | no. | |
25% RSE | |
| |
Mean weekly earnings | 4 900 | 4 100 | 3 200 | 1 600 | 2 200 | 1 000 | 800 | 1 000 | 4 800 | |
Median weekly earnings | 5 500 | 4 500 | 4 100 | 2 100 | 2 800 | 1 300 | 1 200 | 1 200 | 5 900 | |
All other estimates | 5 400 | 4 400 | 4 100 | 2 200 | 2 600 | 1 300 | 1 900 | 1 200 | 5 500 | |
50% RSE | |
| |
Mean weekly earnings | 1 600 | 1 300 | 1 100 | 500 | 700 | 300 | 200 | 400 | 1 000 | |
Median weekly earnings | 1 800 | 1 500 | 1 400 | 700 | 900 | 400 | 300 | 500 | 1 400 | |
All other estimates | 1 800 | 1 400 | 1 400 | 700 | 800 | 400 | 500 | 400 | 1 200 | |
| |
(a) Refers to the number of persons contributing to the estimate. |
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