![]() ![]() Here, the total population is stratified according to region – peri-urban, rural, and deep rural. In particular, it examines views regarding individual compensation for participation and post-trial benefits to the community in which the trial took place. In Grady et al (2008) the study examines the views of research participants in a hypothetical HIV vaccine study. For example, geographical regions can be stratified into similar regions by means of some known variables such as habitat type, elevation, or soil type. (Investigators oversample in the smaller strata in order to increase their sample size, which is necessary to conduct proper statistical analyses.) In practice, stratified random sampling along with other more complex sampling techniques are employed in large-scale surveys, especially governmental censes, to reduce some of the logistical costs associated with collecting information from a sample. In stratified sampling, the population is partitioned into non-overlapping groups, called strata and a sample is selected by some design within each stratum. Stratified random sampling is used instead of simple random sampling when the categories of the strata are thought to be too distinct and too important to the research interest, and/or when investigators wish to oversample a particularly small group of interest. As the sampling variance of the estimate of mean or total depends on within strata variation, the stratification of. ![]() every element in the population can be assigned to only one stratum), and no population element can be excluded in the construction of strata. Each strata should be mutually exclusive (i.e. This way a randomised probabilistic sample is selected within each stratum. For such populations, stratification allows for achieving the. Compare disproportionate stratified sampling. The second step is to take a simple random sample within each stratum. It is particularly useful when the target population is composed of distinct clusters or segments. Stratification is an ex-ante statistical technique that ensures that sub-groups of the population are represented in the final sample and treatment groups. A probability sampling method in which different strata in a population are identified and in which the number of elements drawn from each stratum is proportionate to the relative number of elements in each stratum. The strata are chosen to divide a population into important categories relevant to the research interest.įor example, if interested in school achievement we may want to first split schools into rural, urban, and suburban as school achievement on average may be quite distinct between these regions. There are four major types of probability sample designs: simple random sampling, stratified sampling, systematic sampling, and cluster sampling (see Figure 5.1). Stratified random sampling is a probabilistic sampling method, in which the first step is to split the population into strata, i.e. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |