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Sep 1
Stratified random sampling is a sampling method in which the population is divided into subgroups, or strata, based on certain characteristics that are relevant to the research objective. Random samples are then taken from each stratum in proportion to the size of the stratum in the population. This method ensures that each subgroup is represented in the sample, allowing for more accurate and reliable results.
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Google Gemini AI
Sep 1
Stratified random sampling is a probability sampling method in which the population is divided into separate, distinct, and non-overlapping subgroups, or strata, based on relevant characteristics. A random sample is then selected from each stratum, considering the proportion of the stratum in the overall population.
Here's a step-by-step breakdown of stratified random sampling:
1. Define the Population: Identify the entire population of interest.
2. Stratify the Population: Divide the population into homogeneous subgroups, or strata, based on specific characteristics that are believed to influence the variable of interest. Common stratification variables include age, gender, income, location, educational level, etc.
3. Determine Proportion in Strata: Calculate the proportion of each stratum within the overall population. This will determine the number of samples allocated to each stratum.
4. Select a Sampling Method: Choose a random sampling method, such as simple random sampling or systematic random sampling, to select participants from within each stratum.
5. Draw a Random Sample from Each Stratum: Based on the proportion of each stratum, randomly select the desired number of participants from each subgroup.
The goal of stratified random sampling is to reduce variability within strata and increase the precision of the sample. By ensuring that all relevant subgroups are represented proportionately, the sample becomes more representative of the population.
Advantages of Stratified Random Sampling:
- Improved precision and reduced variability
- Ensures representation of all subgroups
- Suitable when the population has diverse characteristics
- Can provide more accurate estimates
Disadvantages of Stratified Random Sampling:
- Can be time-consuming and resource-intensive
- Requires good knowledge of the population and stratification variables
- Doesn't completely eliminate sampling bias if strata are not truly homogeneous
Here's a step-by-step breakdown of stratified random sampling:
1. Define the Population: Identify the entire population of interest.
2. Stratify the Population: Divide the population into homogeneous subgroups, or strata, based on specific characteristics that are believed to influence the variable of interest. Common stratification variables include age, gender, income, location, educational level, etc.
3. Determine Proportion in Strata: Calculate the proportion of each stratum within the overall population. This will determine the number of samples allocated to each stratum.
4. Select a Sampling Method: Choose a random sampling method, such as simple random sampling or systematic random sampling, to select participants from within each stratum.
5. Draw a Random Sample from Each Stratum: Based on the proportion of each stratum, randomly select the desired number of participants from each subgroup.
The goal of stratified random sampling is to reduce variability within strata and increase the precision of the sample. By ensuring that all relevant subgroups are represented proportionately, the sample becomes more representative of the population.
Advantages of Stratified Random Sampling:
- Improved precision and reduced variability
- Ensures representation of all subgroups
- Suitable when the population has diverse characteristics
- Can provide more accurate estimates
Disadvantages of Stratified Random Sampling:
- Can be time-consuming and resource-intensive
- Requires good knowledge of the population and stratification variables
- Doesn't completely eliminate sampling bias if strata are not truly homogeneous
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