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A Guide to Small Area Estimation - Version 1.1 05/05/2006 Figure 4.3 : Trade-off between Quality and Cost / Time / Effort Statistical expertise Robustness of results Understanding results Interpretability Issues Validity of assumptions User requirements Availability of resources Timeliness/deadlines Quality Simple models Complex models Level of precision Finer disaggregation Good auxiliary data Cost/time/effort As you can see from Figure 4.3, good quality auxiliary data is a crucial prerequisite for obtaining quality small area estimates. Quality is of course a relative term and depends very much upon the clients’ decision making requirements. Assuming that we have good quality auxiliary data, we would expect more sophisticated methods to provide results of a higher level of quality, as indicated by the upward slope of the cost-quality curve. The same curve also indicates that somewhere in the continuum there exists an optimal point (a level of precision) whereby any additional effort/cost/time from that point on, has either marginal or declining effects on quality. More elaborate techniques may give only marginal improvements in accuracy but decrease timeliness, an important dimension of quality. Overall quality may also be eroded when exceedingly smaller areas or finer disaggregations of the data are demanded. For example, in the disability analysis, disaggregating disability by type of impairment, level of severity and age group, in addition to the small area level, leads to poor quality estimates, especially for the rarest impairment types such as sensory. There are also other issues, as shown just above the cost-curve in Figure 4.3, that may have significant bearing in relation to quality and cost of small area estimates. For example, the use of more complex models may require a higher level of subject matter knowledge and expertise to assist in understanding and interpreting model results. Such knowledge is also important for testing the validity of assumptions inherent in the model and in checking the robustness and sensitivity of model results. Finally, there are important points that have to be made in relation to the quality versus cost issue discussed above. Firstly, that simplicity is an important aspect of quality in that it aids the interpretability of small area output. We do not intend to imply from Figure 4.3 that simpler methods always imply poor quality estimates. More complex methods should only be attempted where there are likely to be demonstrable gains in Australian Bureau of Statistics 34
A Guide to Small Area Estimation - Version 1.1 05/05/2006 the accuracy of small area estimates. Secondly , the use of sophisticated methods may not necessarily lead to higher costs. For instance, once a strong analytic capability to undertake small area estimation has been established (in terms of statistical skill and other resources) any increase in cost, effort and time for undertaking complex small area methods may be marginal. Australian Bureau of Statistics 35
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