Sampling stratified random

While biochemical markers of bone metabolism may be sensitive to the effects of glucocorticoids in the short term, the relation between changes in these markers and intermediate measures, such as bone mineral density, and the more important clinical outcomes of fractures, is unknown. In a random stratified sample of 3222 women in the perimenopausal age range (47-56 years), including 119 women with asthma, bone mineral density was measured to determine whether asthma was a risk factor of osteoporosis and to investigate the effect of inhaled glucocorticoids (102). The subjects had predominantly adult-onset asthma, as the age at diagnosis was over 40 years. There were 26 patients who were treated mainly with... 

There are four major types of sampling methods random, stratified, systematic, and cluster. Random is by far the most commonly employed method in toxicology. It stresses the fulfillment of the assumption of avoiding bias. When the entire pool of possibilities is mixed or randomized (procedures for randomization are presented in a later section), then the members of the group are selected in the order that are drawn from the pool. 

Stratified samples consist of portions obtained from identified subparts (strata) of the parent population. Within each stratum, the samples are taken randomly. 

Data are not random but are representative in other ways. This may mean, for example, that the data are a stratified sample applicable to the real-world situation for the assessment scenario of interest. In this case, frequentist methods can be used to make inferences for the strata that are represented by the data (e.g. particular exposed subpopulations), but not necessarily for all aspects of the scenario. However, for the components of the scenario for which the data cannot be applied, there is a lack of representative data. For example, if the available data represent one subpopulation, but not another, frequentist methods can be applied to make inferences about the former, but could lead to biased estimation of the latter. Bias correction methods, such as comparison with benchmarks, use of surrogate (analogous) data or more formal application of expert judgement, may be required for the latter. 

STRATIFIED SAMPLING OPPOSED TO COMPLETELY RANDOM SAMPLING... 

In statistical terms, if the whole population to be sampled is homogeneous, that is, can be described by a single set of parameters, simple random sampling is as efficient as stratified sampling. But, if the population consists of a set of appreciably different subpopulations and so cannot be described by a single set of parameters. 

Stratified Sampling Opposed to Completely Random Sampling 574... 

Stratified sampling is commonly used in a location which is known to have contaminants heterogeneously distributed. This is therefore the most common approach to sampling. In this type of sampling, the site is sub-divided into smaller areas, each of which is fairly homogeneous, and thus more accurate sampling can take place. Each sub-area is then randomly sampled. The sub-dividing of the... 

Stratified sampling is appropriate to the case in which a certain logical or natural grouping may be expected within the population. Examples are the element or compound distribution in soil due to the water circulation, or the element distribution in biological tissues. Such a layer in the soil may be called a stratum. Here we want to know the mean of the stratum the samples within the stratum are selected by a random method. Further on, the number of samples selected from any stratum should be proportional to the product of the total number of particles in the stratum N and the standard deviation a-,. 

In stratified sampling the population to be examined is first divided into a number of portions called strata, followed by random sampling within each stratum. The overall variance, is... 

---