Australian Bureau of Statistics 

4159.0.55.002  General Social Survey: User Guide, Australia, 2010
Previous ISSUE Released at 11:30 AM (CANBERRA TIME) 07/12/2011 
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SAMPLING ERROR
Measures of sampling error One measure of sampling variability is the Standard Error (SE) which indicates the extent to which an estimate might have varied by chance because only a sample of persons was included. There are approximately two chances in three that a sample estimate will differ by less than one standard error from the number that would have been obtained if all persons had been included in the survey, and about nineteen chances in twenty that the difference will be less than two standard errors. 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 to which it relates: Very small estimates may be subject to such high RSEs so as to seriously detract from their value for most reasonable purposes. Only estimates with RSEs less than 25% are considered sufficiently reliable for most purposes. However, estimates with RSEs of 25% or more are included in all published 2010 GSS output. Estimates with an RSE of 25% to 50% are preceded by an asterisk (e.g. *3.4) to indicate that the estimate should be used with caution. Estimates with RSEs over 50% are indicated by a double asterisk (e.g.**0.6) and should be considered unreliable for most purposes. RSEs for estimates from the 2010 GSS are available in 'actual' form, i.e. the RSE for each estimate produced can be calculated using the replicate weights. Replicate weighting is a process whereby a small group of persons or households in the sample are assigned a zero weight and then the remaining records are reweighted to the survey benchmark population. For the 2010 GSS this process was repeated 60 times to produce 60 replicate weights. These replicate weights are used for calculating the variances of the estimate for each replicate group and the original estimate, by squaring the difference and summing these differences over all of the 60 replicate groups. The difference between the replicate estimate and the original estimate is then used in calculating the standard error of the estimate.
This page last updated 16 September 2015
