When the goal of a research study is to describe and compare different subgroups in a known population, the best strategy is to use proportionate stratified random sampling. Dr. Ziser conducts an experiment on life satisfaction using participants above the age of 80.
What is stratified random sampling in research?
Stratified random sampling is a method of sampling that involves the division of a population into smaller sub-groups known as strata . In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics such as income or educational attainment.
When stratified sampling is used?
Stratified sampling is used when the researcher wants to understand the existing relationship between two groups . The researcher can represent even the smallest sub-group in the population.
What is stratified proportionate sampling?
Proportionate stratified sample means that size of sample strata is proportional to the size of population strata ; in other words, probability of unit being selected from the stratum is proportional to relative size of that stratum in population.
What is systematic sampling in research?
Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval . This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.
What is the importance of stratified random sampling?
In short, it ensures each subgroup within the population receives proper representation within the sample . As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling.
What is an example of stratified random sampling?
A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age , like 18–29, 30–39, 40–49, 50–59, and 60 and above.
What are the advantages and disadvantages of stratified sampling?
| Stratified Sampling | Advantages Free from researcher bias beyond the influence of the researcher produces a representative sample Disadvantages Cannot reflect all differences complete representation is not possible | Evaluation This way is free from bias and representative |
Which sampling method is best?
Simple random sampling : One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance.
How do you do random sampling in research?
- Step 1: Define the population. Start by deciding on the population that you want to study. ...
- Step 2: Decide on the sample size. Next, you need to decide how large your sample size will be. ...
- Step 3: Randomly select your sample. ...
- Step 4: Collect data from your sample.
How do you use proportionate stratified random sampling?
Proportionate Stratified Random Sampling:
That means each strata sample has the same sampling fraction . If you have 4 strata with 500, 1000, 1500, 2000 respective sizes and the research organization selects 1⁄2 as sampling fraction. A researcher has to then select 250, 500, 750, 1000 members from the respective stratum.
What is an example of cluster?
An example of Multiple stage sampling by clusters – An organization intends to survey to analyze the performance of smartphones across Germany . They can divide the entire country's population into cities (clusters) and select cities with the highest population and also filter those using mobile devices.
What are the disadvantages of stratified sampling?
One major disadvantage of stratified sampling is that the selection of appropriate strata for a sample may be difficult . A second downside is that arranging and evaluating the results is more difficult compared to a simple random sampling.
What is systematic sampling and example?
Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling.
Where is systematic sampling used?
Use systematic sampling when there's low risk of data manipulation . Systematic sampling is the preferred method over simple random sampling when a study maintains a low risk of data manipulation.
What is systematic random sampling with example?
Systematic random sampling is the random sampling method that requires selecting samples based on a system of intervals in a numbered population. For example, Lucas can give a survey to every fourth customer that comes in to the movie theater .
