2.6.1.2.3 Non-stratified independent random sample with equal probability pros & cons

A sampling technique where a group of subjects (a sample) for study is selected from a larger group (a population). Each individual is chosen entirely by chance and each member of the population has an equal chance of being included in the sample. 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 on the Aquatic Resource Monitoring web site or directly from R project package spsurvey.

Pros and Cons:

The following pros and cons of non-stratified independent random sample with equal probability designs should assist you in determining if it is appropriate for your monitoring needs.

Site Selection

Pros:

• Provides representative sample of the target population
• Available sample selection procedures are relatively easy to implement
• Allows replacement of sites if sites are dropped (for valid reasons)

Cons:

• Does not incorporate characteristics of or information known about target population
• Does not ensure spatial balance of sample
• Can require larger sample sizes than other spatial designs to achieve desired sampling error
• Can increase field operation costs due to inaccessibility of sites
• 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
• Results in unbiased estimates and associated unbiased estimates of variance.
• Can use standard statistical methods (e.g., regression) for other analyses
• Precision/power depends on sample size and metric variability

Cons:

• Typically, other spatial designs provide lower precision for the same sample size
• Can be expensive to achieve desired sampling error

 

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