6310.0 - Employee Earnings, Benefits and Trade Union Membership, Australia, Aug 2007
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 14/04/2008
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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 1,929,000. Since this estimate is between 1,000,000 and 2,000,000, table T1 shows that the SE for Australia will lie between 10,550 and 15,300 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 1,914,000 to 1,944,000 and about 19 chances in 20 that the value will fall within the range 1,899,000 to 1,959,000. 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 1,929,000 female part-time employees in main job and table 4 shows mean weekly earnings for the same group as \$412. The SE of 1,929,000 was calculated previously as 15,000. To convert this to an RSE we express the SE as a percentage of the estimate, or 15,000/1,929,000 = 0.8%.

8 The RSE of the estimate of mean weekly earnings is calculated by multiplying this number (0.8%) by the appropriate factor shown in paragraph 6 (in this case 0.9): 0.8 x 0.9 =0.7%. The approximate SE of this estimate of mean weekly earnings of female part-time employees in main job is therefore 0.7% of \$412, that is about \$2.88. 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 \$409.12 to \$414.88, and about 19 chances in 20 that it would have been within the range \$406.24 to \$417.76.

9 Mean and median estimates produced from population estimates smaller than the values in T2 have RSEs larger than 25% and should be used with caution. Table T2 also indicates the size of the population estimates that would produce mean and medians with RSEs greater than 50% which are considered too unreliable for general use.

ALL OTHER ESTIMATES

10 All other estimates produced from population estimates smaller than the values in T2 have RSEs larger than 25% and should be used with caution. T2 also indicates the size of the population estimates that would produce all other estimates with RSEs greater than 50% which are considered too unreliable for general use.

PROPORTIONS AND PERCENTAGES

11 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.

12 Considering the example from the previous page, the 1,929,000 females who were part-time employees in their main job represent 46% of the 4,180,800 female employees. The SE and RSE of 1,929,000 were calculated previously as 15,000 and 0.8% respectively. The SE for 4,180,800 calculated by interpolation is 22,800, which converted to a RSE is 22,800/4,180,800 =0.6%. Applying the above formula, the RSE of the proportion is:

13 Therefore, the SE for the proportion (46%) is 0.2 percentage points (=(46/100)x 0.5). Therefore, there are about two chances in three that the proportion of female part-time employees was between 45.8% and 46.2%, and 19 chances in 20 that the proportion is within the range 45.6% to 46.4%.

DIFFERENCES

14 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:

15 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 estimates (persons) no. no. no. no. no. no. no. no. no. % 100 270 260 190 160 180 100 110 90 100 100.0 200 360 340 280 210 240 150 160 140 170 85.0 300 430 400 340 250 280 180 200 180 240 80.0 500 530 490 440 310 340 220 260 230 340 68.0 700 610 550 510 350 390 250 310 260 430 61.4 1,000 700 640 590 400 450 290 360 290 550 55.0 1,500 830 740 700 470 520 340 420 310 700 46.7 2,000 930 830 790 530 580 370 470 330 830 41.5 2,500 1 000 900 850 550 650 400 500 350 950 38.0 3,000 1 100 950 900 600 700 400 550 350 1 050 35.0 3,500 1 150 1 050 1 000 650 700 450 550 400 1 100 31.4 4,000 1 200 1 100 1 050 700 750 450 600 400 1 200 30.0 5,000 1 300 1 150 1 100 750 800 500 650 450 1 350 27.0 7,000 1 500 1 350 1 250 850 950 550 750 500 1 550 22.1 10,000 1 700 1 500 1 400 950 1 050 650 1 000 600 1 800 18.0 15,000 2 000 1 750 1 550 1 100 1 200 800 1 350 700 2 100 14.0 20,000 2 200 1 950 1 700 1 200 1 350 900 1 750 850 2 300 11.5 30,000 2 550 2 250 1 950 1 400 1 550 1 150 2 400 1 050 2 600 8.7 40,000 2 850 2 500 2 200 1 600 1 700 1 400 3 050 1 250 2 850 7.1 50,000 3 100 2 750 2 400 1 800 1 900 1 600 3 650 1 450 3 050 6.1 100,000 3 950 3 550 3 250 2 700 2 750 2 250 6 300 1 900 3 850 3.9 150,000 4 600 4 350 4 000 3 450 3 650 2 700 . . 2 150 4 500 3.0 200,000 5 300 5 050 4 700 4 050 4 400 3 000 . . 2 300 5 050 2.5 300,000 6 700 6 500 5 950 5 000 5 500 . . . . . . 5 950 2.0 500,000 9 350 9 000 8 050 6 250 7 000 . . . . . . 7 500 1.5 1,000,000 13 900 13 700 11 500 8 000 8 950 . . . . . . 10 550 1.1 2,000,000 18 750 20 250 15 450 . . . . . . . . . . 15 300 0.8 5,000,000 23 900 32 400 . . . . . . . . . . . . 25 550 0.5 10,000,000 . . . . . . . . . . . . . . . . 34 100 0.3 . . not applicable

 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 4 200 3 200 1 600 2 200 1 000 800 1 000 4 900 Median weekly earnings 5 500 4 600 4 100 2 100 2 700 1 200 1 100 1 200 6 000 All other estimates 5 500 4 500 4 200 2 200 2 600 1 300 1 800 1 200 5 600 50% RSE Mean weekly earnings 1 600 1 400 1 000 500 700 300 200 400 1 000 Median weekly earnings 1 800 1 500 1 300 700 900 400 300 400 1 400 All other estimates 1 800 1 500 1 300 700 800 400 600 400 1 300 (a) Refers to the number of people contributing to the estimate.