A method of selecting sample members from a larger population according to a random starting point and a fixed, periodic interval. Typically, every "nth" member is selected from the total population for inclusion in the sample population. Systematic sampling is still thought of as being random, as long as the periodic interval is determined beforehand and the starting point is random. 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. This has to be accounted for when we select a sample from the population in order that we obtain a sample that is representative of the population. This is achieved by stratified sampling. A non-stratified sample does not take separate samples from strata or sub-groups of a population.
Tools:
Software to implement such a design is available from R project package sp.
Pros and Cons:
The following pros and cons of systematic non-stratified designs should assist you in determining if it is appropriate for your monitoring needs.
Site Selection
Pros:
- Relatively simple to implement for some indicators and situations (some exceptions are large and complex sampling areas)
- May provide representative sample
- Available sample selection procedures are relatively easy to implement for small sampling areas
- Provide spatial balance
Cons:
- May not provide representative sample. Spatial alignment of sites may correspond to spatial alignment of attributes of interest, which will lead to biased resource representation.
- Difficult to replace dropped sites and maintain spatial balance
- May create unreasonable sampling effort if the sampling rate is set too high
- Errors in the sampling frame may result in the sampling frame excluding some sites that are in the target population or including some sites that are not in the target population
- Sampling frames based on different GIS scales (e.g., 1:100000 and 1:24000) may result in different estimates for the target population
Statistical Inference
Pros:
- Procedures for estimating characteristics of target population are simple and well-known
- May result in unbiased estimates and associated unbiased estimates of variance
- Can use standard statistical methods (with assumptions) for other analyses (e.g., regression)
- Precision/power depends on sample size and metric variability
Cons:
- May result in biased estimates and associated unbiased estimates of variance
- There is no exact estimator for variances; there are only approximations
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2.1.6 Survey-based Design
