SAE Manual Sections 1 to 4_1 (May 06).pdf - National Statistical ...

A Guide to Small Area Estimation - Version 1.1 05/05/2006
Clearly, as indicated in questions 2 to 3 of Figure 4.2, the choice of any of these or other
models depends on the following important interrelated factors:
i.
ii.
iii.
iv.
v.
the level at which the small area estimates are required. Are small area estimates
required at area-level or at some other sub-population such as age by sex group.
the nature of the auxiliary data available related to the variable of interest. Again,
these may include whether the data is at the unit-level (person-level), area-level or
both.
the nature of the variable of interest, i.e., whether it is continuous, binary or count
data.
users quality requirements for small area estimates
access to statistical expertise
Small area models can be fitted either at area-level or person-level. Area level models are
fitted when the variable of interest and associated covariates in the auxiliary data are
observed at the level of the specific geographic area, which is referred in Figure 4.1 as
area-level analysis. On the other hand a unit/person-level analysis refers to
unit/person-level model that makes use of individual/unit level data in the analysis. When
a model is fitted using unit/person-level data then the predictions based on this model
must be aggregated to produce area-level estimates. It is also possible to fit a unit/person
level model involving both individual and area-level covariates.
Choosing the right model for the right type of data is crucial in the modelling process.
For example, if the auxiliary information consists of data observed at area or unit level
and the variable of interest is of a continuous nature, then it will be appropriate to use a
linear model to estimate the variable of interest. Alternatively, if we have unit level data
where the variable of interest is binary (e.g., 1= person has a disability and 0 = person
has no disability) which is usually the case in many small area models, then we would go
for a model that captures the binary nature of the observations, such as the logistic
regression model. Similarly, if our data provides, say, area level count data of people
with a disability then a suitable choice would be the Poisson model which is appropriate
for count data models. It is also possible to use two or more models (e.g., unit-level and
area-level models) provided that the dataset is amenable to such analyses . For instance,
as we will see in the examples of Section 5 , the logistic and Poisson models are used to
predict person-level and area-level disability proportions, respectively.
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