Sampling Design in research

The ultimate test of a sample design is how well it represents the characteristics of the population it purports of represent. In measurements terms, the sample must be valid. Validity of a sample depends upon two considerations.

Accuracy

First is the matter of accuracy – the degree to which bias is absent from the sample. An accurate (unbiased) sample is one in which the underestimators and the overestimators are balances among the members of the sample. There is no systematic variance with an accurate sample. Systematic variance has been defined as “the variation in measures due to some known or unknown influences that “cause” the scores to lean in one direction more than another”. It has been observed that homes on the corner of the block are often larger and more valuable than those within blocks. Thus, a sample that selects corner homes only will cause us to overestimate home values in the area.

Types of Sample Design

A variety of sampling techniques is available. The one selected depends on the requirements of the project, its objectives, and funds, available. The different approaches may be classified by their representation basis and the element selection techniques.

Representation

The members of a sample are selected either on a probability basis or by another means. Probability sampling is based on the concept of random selection – a controlled procedure that assures that each population element is given a known nonzero chance of selection.

In contrast, nonprobability sampling is nonrandom and subjective. That is, each member does not have a known nonzero chance of being included. Allowing interviewers to choose sample members “at random” (meaning as they wish or wherever they find them) is not random sampling. Only probability samples provide estimates of precision.

Element Selection

Sample may also be classified by whether the elements are selected individually and directly from the population – viewed as a single pool – or whether additional controls are placed on element selection. When each sample element is drawn individually from the population at large, it is an unrestricted sample. Restricted sampling covers all other forms of sampling.