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6222.0 - Job Search Experience, Australia, Jul 2008 Quality Declaration 
Previous ISSUE Released at 11:30 AM (CANBERRA TIME) 12/01/2009   
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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.

3 The LFS sample size in July 2008 was approximately one-third smaller than the sample size in July 2007. This is due to an 11% sample reduction that was implemented from November 2007 to June 2008 based on the 2006 sample design, and an additional 24% sample reduction implemented in July 2008. In combination, the two sample reductions are expected to increase the standard errors for estimates from the supplementary surveys by approximately 22% at the broad aggregate level, relative to the 2001 sample design (standard errors will vary at lower aggregate levels). Detailed information about the sample reduction is provided in Information Paper: Labour Force Survey Sample Design, Nov 2007 (Second edition) (cat. no. 6269.0).


CALCULATION OF STANDARD ERROR

4 An example of the calculation and the use of SEs in relation to estimates of persons is as follows. Table 4 shows the estimated number of unemployed women in Australia who were looking for full-time work was 128,500. Since this estimate is between 100,000 and 150,000, table T1 shows that the SE for Australia will lie between 4,900 and 5,700 and can be approximated by interpolation using the following general formula:

Equation: Calculation of standard errors

5 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 123,100 to 133,900 and about 19 chances in 20 that the value will fall within the range 117,700 to 139,300. This example is illustrated in the diagram below.

Diagram: Confidence intervals of estimates

6 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%. Table T2 presents the levels at which estimates have RSEs of 25% and 50%.


MEANS AND MEDIANS

7 The RSEs of estimates of mean duration of unemployment and median duration of unemployment are obtained by first finding the RSE of the estimate of the total number of persons contributing to the mean or median (see table T1) and then multiplying the resulting number by the following factors for Australian estimates:
  • mean duration of unemployment: 1.6
  • median duration of unemployment: 2.5

8 The following is an example of the calculation of SEs where the use of a factor is required. Table 4 shows that the estimated median duration of unemployment for unemployed women in Australia was 10 weeks and shows that the number of unemployed women was estimated as 216,000. The SE of 216,000 can be calculated from table T1 (by interpolation) as 6,600. To convert this to an RSE we express the SE as a percentage of the estimate or 6,600/216 000 =3.1%.

9 The RSE of the estimate of median duration of unemployment for unemployed women is calculated by multiplying this number (3.1%) by the appropriate factor shown in the previous paragraph (in this case 2.5): 3.1 x 2.5 = 7.8%. The SE of this estimate of median duration of unemployment for unemployed women is therefore 7.8% of 10 weeks, i.e. almost one week. Therefore, there are two chances in three that the median duration of unemployment for women that would have been obtained if all dwellings had been included in the survey would have been within the range 9 to 11 weeks and about 19 chances in 20 that it would have been within the range 8 weeks to 12 weeks.

10 Table T2 represents the minimum size of estimates, based on the SE model described in paragraph 2, required to have RSEs of less than 25% and 50% respectively. For example, an estimate of median duration of unemployment for Australia based on less than 35,100 people will have an RSE of at least 25%, and an estimate of median duration of unemployment for Australia based on less than 12,500 will have an RSE of at least 50%. For all other estimates, (i.e. those estimates based purely on number of people in a specific category), an estimate of less than 8,600 for the Australian total will have an RSE of at least 25% and an estimate of less than 5,700 will have an RSE of at least 50%.


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:

Equation: Calculation of relative standard errors of proportions and percentages

12 Considering the example from the previous page, of the 128,500 unemployed women who were looking for full-time work, 19,800 or 15.4% had been unemployed for one year or more. The SE of 19,800 may be calculated by interpolation as 2,900. To convert this to an RSE we express the SE as a percentage of the estimate, or 2,900/19,800 = 14.7%. The SE for 128,500 was calculated previously as 5,400, which converted to an RSE is 5,400/128,500 = 4.2%. Applying the above formula, the RSE of the proportion is:

Equation: Example calculation of relative standard errors of proportions

13 Therefore, the SE for the proportion of unemployed women looking for full-time work who had been unemployed for one year or more is 2.2 percentage points (=(15.4/100)x14.1). Therefore, there are about two chances in three that the proportion of unemployed women looking for full-time work who have been unemployed for one year or more is between 13.2% and 17.6% and 19 chances in 20 that the proportion is within the range 11.0% to 19.8%.


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:

Equation: Calculation of differences between estimates

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

NSW
Vic.
Qld.
SA
WA
Tas.
NT
ACT
Australia
Size of estimate (persons)
no.
no.
no.
no.
no.
no.
no.
no.
no.
%

100
340
330
250
200
250
130
90
120
120
120.0
200
450
430
370
270
330
180
140
190
220
110.0
300
540
510
450
320
390
220
170
240
300
100.0
500
660
620
570
390
480
270
220
310
440
88.0
700
760
710
670
450
550
310
260
350
550
78.6
1,000
880
810
780
520
630
360
300
380
700
70.0
1,500
1 030
950
930
600
730
410
350
420
890
59.3
2,000
1 150
1 060
1 040
670
820
450
390
440
1 050
52.5
2,500
1 250
1 150
1 150
750
900
500
400
450
1 200
48.0
3,000
1 350
1 250
1 200
800
950
500
450
500
1 300
43.3
3,500
1 450
1 300
1 300
800
1 000
550
450
500
1 400
40.0
4,000
1 500
1 400
1 350
850
1 050
550
500
550
1 500
37.5
5,000
1 650
1 500
1 450
950
1 150
600
550
600
1 700
34.0
7,000
1 850
1 700
1 650
1 050
1 300
700
650
650
1 950
27.9
10,000
2 150
1 950
1 850
1 200
1 500
800
800
800
2 300
23.0
15,000
2 500
2 250
2 050
1 350
1 700
950
1 150
950
2 650
17.7
20,000
2 750
2 500
2 250
1 500
1 900
1 150
1 450
1 100
2 950
14.8
30,000
3 200
2 900
2 600
1 800
2 150
1 450
2 000
1 450
3 350
11.2
40,000
3 550
3 200
2 850
2 050
2 400
1 700
2 500
1 700
3 650
9.1
50,000
3 850
3 500
3 150
2 300
2 650
1 950
3 000
1 900
3 900
7.8
100,000
4 900
4 550
4 300
3 450
3 900
2 750
5 250
2 550
4 900
4.9
150,000
5 750
5 550
5 300
4 400
5 150
3 300
7 200
2 900
5 700
3.8
200,000
6 600
6 450
6 200
5 200
6 150
3 700
9 000
3 050
6 400
3.2
300,000
8 300
8 300
7 850
6 400
7 750
4 200
12 250
3 200
7 600
2.5
500,000
11 650
11 500
10 600
8 000
9 850
4 850
. .
3 200
9 550
1.9
1,000,000
17 300
17 500
15 150
10 200
12 600
5 550
. .
. .
13 450
1.3
2,000,000
23 300
25 850
20 350
12 100
14 550
. .
. .
. .
19 550
1.0
5,000,000
29 700
41 350
27 450
13 650
15 200
. .
. .
. .
32 600
0.7
10,000,000
31 800
57 000
32 100
. .
. .
. .
. .
. .
43 500
0.4
15,000,000
. .
. .
. .
. .
. .
. .
. .
. .
49 100
0.3

. . not applicable

T2 Levels at which estimates have relative standard errors 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 duration of unemployment
13 900
11 700
10 600
5 400
7 700
2 800
1 900
2 900
18 100
Median duration of unemployment
36 800
31 800
27 800
14 800
22 300
10 300
6 400
8 500
35 100
All other estimates
7 800
6 700
6 300
3 200
4 400
1 700
1 300
1 700
8 600

50% RSE

Mean duration of unemployment
4 600
3 900
3 700
1 800
2 600
1 000
600
1 200
5 700
Median duration of unemployment
12 400
10 700
10 200
5 100
7 600
3 400
2 100
2 800
12 500
All other estimates
2 500
2 200
2 100
1 000
1 400
600
400
700
2 300

(a) Refers to the number of people contributing to the estimate.



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