2.1 Target population and sampling units
Gambino & Nascimento Silva (2009) mentions that, since household surveys became widespread in the 1940s, several trends associated with technological advances have emerged both in statistical agencies and in society in general, and these trends accelerated with the introduction of the computer. In this context, sampling arises as a response to the need to obtain precise statistical information about a target population without conducting a complete census. As Gutiérrez (2016) notes, sampling consists of conducting partial investigations of a population in order to infer results for the whole.
In recent decades, this methodology has become consolidated in different fields, especially in the government sector, through the production of official statistics that make it possible to monitor public policies and the Sustainable Development Goals. Likewise, the use of sampling has spread to academia, the private sector, and the media, where it is a fundamental tool for generating and analyzing information.
Every survey is associated with a finite population composed of individuals or elements about which information is desired. The set of units for which estimates and results will be produced is called the target population. Surveys also define units of analysis, which correspond to the different levels of disaggregation for which statistics of interest are presented, such as persons, households, or dwellings.
In household surveys in Latin America, it is common to use multistage sampling designs. To do this, different sampling units are selected that ultimately make it possible to reach the households that will be part of the sample. For example, primary sampling units (UPM) may correspond to census sectors built from the most recent population census, while secondary sampling units (USM) may be the dwellings located within those sectors.
To carry out a systematic household selection process, it is essential to have a sampling frame that serves as a link between the sampling units and the households that make up the target population. This frame is the set in which all the elements that make up the study population are identified and from which the sample is selected. In household surveys with complex sample designs, sampling frames are usually based on geographic areas. That is, they are built from spatial structures that link households or persons to delimited areas of the territory. This type of frame facilitates territorial organization into more manageable units and makes it possible to implement multistage selection processes while maintaining the principles of probability sampling.
Another fundamental characteristic of household surveys is clustering. In practice, many surveys first select primary sampling units, such as census sectors or enumeration areas, and then select households within each of them. This type of design reduces operational costs and facilitates fieldwork; however, it also has important implications for the precision of estimates. When households belonging to the same cluster share similar characteristics, the additional information contributed by each new observation within the cluster decreases.
For example, suppose a survey selects 100 clusters and, within each one, 10 households, resulting in a total sample of 1,000 households. If households in the same cluster display very similar behaviors (for example, if all have access to electricity), the effective variability of the sample is considerably reduced. In practical terms, the precision obtained could be equivalent to that of a simple random sample of only 100 households. Analyzing the 1,000 households as if they were completely independent observations, while ignoring the clustering structure, leads to an underestimation of standard errors and, consequently, to artificially narrow confidence intervals and potentially erroneous conclusions about the statistical significance of the results.