![]() Stratified randomization is considered a subdivision of stratified sampling, and should be adopted when shared attributes exist partially and vary widely between subgroups of the investigated population, so that they require special considerations or clear distinctions during sampling. In statistics, stratified randomization is a method of sampling which first stratifies the whole study population into subgroups with same attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup are selected unbiasedly during any stage of the sampling process, randomly and entirely by chance. Spatial alignment of sites within a stratum may correspond to spatial alignment of attributes of interest, which will lead to biased resource representation.Graphic breakdown of stratified random sampling May not provide representative sample.Precision/power depends on sample size and metric variability.Enable standard statistical methods to be used (with assumptions).Sampling equal numbers from strata that vary widely in physical size may be used to equate the statistical power of tests of differences between strata.May reduce sampling variance when strata constructed for that purpose.May result in unbiased estimates and variance estimates.Sampling frames based on different GIS scales (e.g., 1:100000 and 1:24000) may result in different estimates for the target population.Errors in the sampling frame may result in the sampling frame excluding some sites that are in the rarget population or including some sites that are not in the target population.May not know if homogeneous subgroups exist or if homogeneous subgroups are same for all variables.Requires additional information about population to stratify.May create unreasonable sampling effort if the sampling rate is set too high.Difficult to replace dropped sites and maintain spatial balance.Spatial alignment of sites may correspond to spatial alignment of attributes of interest, which will lead to biased resource representation. ![]() ![]() Relatively simple to implement for some indicators and situations (some exceptions are large and complex sampling areas).Can reduce cost to obtain same sampling error compared to non-stratified sample.May enable survey to ensure important subpopulations are included.The following pros and cons of systematic stratified designs should assist you in determining if it is appropriate for your monitoring needs. Software to implement such a design is available from R project package sp. estimates of the population parameters may be wanted for each sub-population.the cost per observation in the survey may be reduced.Some reasons for using systematic stratified sampling over simple random sampling are: Stratified systematic sampling techniques are generally used when the population is heterogeneous, or dissimilar, or where certain homogeneous, or similar, sub-populations can be isolated (strata). Systematic sampling with no strata is most appropriate when the entire population from which the sample is taken is homogeneous. When we sample a population with several strata, we generally require that the proportion of each stratum in the sample should be the same as in the population. ![]() Stratified systematic sampling accounts for these differences by selecting a systematic sample within each of these sub-populations. There may often be factors which divide up the population into sub-populations (groups / strata) and we may expect the measurement of interest to vary among the different sub-populations. Stratified systematic sampling is still thought of as being random, as long as the periodic interval is determined beforehand and the starting point is random. A method applied to each stratum of a target population where sample members are selected within the stratum according to a random starting point and a fixed, periodic interval. Typically, every "nth" member is selected from the stratum for inclusion in the sample population.
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