What are the limitations of simple random sampling (SRS)?
Simple Random Sampling (SRS) is a widely used and unbiased sampling method, but it has certain limitations, particularly in practical applications. Here are the key limitations of SRS:
1. Need for a Complete Population List:
SRS requires a complete list of all individuals or units in the population (a sampling frame). In large or hard-to-define populations, creating such a list can be difficult, time-consuming, or impossible.
2. Impractical for Large Populations:
In large populations, the process of randomly selecting individuals can become inefficient and costly. SRS may also require a lot of time and effort to locate and survey individuals, especially if they are geographically dispersed.
3. Lack of Representation in Small Samples:
When the sample size is small, SRS may not always capture the diversity of the population. There is a risk that key subgroups might be underrepresented purely by chance, leading to biased or less accurate results.
4. Higher Sampling Error Compared to Stratified Sampling:
Since SRS does not consider specific characteristics of the population, it can result in higher variability between samples. Stratified sampling, which divides the population into subgroups (strata) and samples within those groups, often yields more precise estimates with lower sampling error.
5. Difficulty in Locating Selected Individuals:
Once individuals are randomly selected, it may be difficult to locate or contact them, especially if they are mobile or their contact information is outdated, leading to non-response or incomplete data collection.
6. No Control Over Subgroup Representation:
SRS does not guarantee that key subgroups (e.g., age, income, gender) will be proportionally represented in the sample. This can be problematic if certain groups are underrepresented, especially in populations with diverse characteristics.
7. Time-Consuming for Data Collection:
Because of the randomness in selecting participants, there is no guarantee that the selected individuals are conveniently located or easily accessible, which can increase the time and cost involved in conducting the survey.
8. Inefficient for Heterogeneous Populations:
In highly heterogeneous populations, SRS might not capture the variability across different subgroups, which can lead to less accurate estimates. Stratified or cluster sampling may be more efficient in such cases to ensure representation of all subgroups.
In Summary:
While SRS is simple and free from selection bias, its limitations include the need for a complete population list, inefficiency with large or diverse populations, higher sampling error, and potential challenges in locating individuals or ensuring subgroup representation. Other sampling methods, like stratified or cluster sampling, may be more suitable in cases where these issues are significant.
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