Sampling is one of the most important methods that determine the accuracy of research. If the samples chosen for a study are not a good representative of a population, it may falsify the results. Stratified sampling is probability (random) sampling that deals with diversified data. It ensures that the selected population-representative must not skip any important characteristic or member. Thus, this article aims to clarify the concept of stratified sampling, its usefulness, and its techniques.
What are sampling techniques?
Sampling is a method that allows researchers to gather information about a population under study based on the information provided by the subsets; instead of investigating every individual. These subsets assist researchers in reducing the cost, effort, and time required to complete a study. There are several types of sampling techniques. These sampling techniques are divided into two groups, non-probability and probability sampling.
Probability sampling starts with selecting all suitable individuals, so each one has an equal chance to participate in a study. It aims to promote generalisation in results. At the same time, in non-probability sampling, only a few eligible candidates are selected instead of all. Thus, we often miss some individuals who may be potential candidates for a study in the latter one.
Probability sampling further includes simple random sampling, systematic sampling, stratified sampling and clustered sampling. Similarly, a non-probability sampling includes convenience sampling, quota sampling, judgment or purposive sampling, and snowball sampling. All these sampling techniques differ based on the selection of samples, sample size and grouping techniques. Therefore, getting dissertation help online becomes necessary to overcome all issues related to the sampling techniques.
What is meant by stratified sampling?
Stratified sampling is the one in which we divide the total population (large sample size) into smaller groups (strata). The strata formation is based on certain characteristics of the population data, for example, gender, age, education and achievements. The subgroup of a population must be formed so that every member of a population must fit in one stratum.
This subgrouping is then followed by other probability samplings such as cluster or simple random sampling methods to complete the sampling process. A random sampling of sub-population in stratified sampling allows researchers to measure each statistically. Researchers use this type of sampling when the characteristics of a population are so diverse, and one should need some method to confirm each one is included in a study.
How do you do stratified sampling?
Stratified sampling is quite similar to other types of random sampling. It also starts from the random collection of data from all available sources. Following the collection of data demands, a researcher chooses the characteristics of stratification and division of the population into each stratum. After dividing into sub-groups, selecting the appropriate sample size and random sampling is done either by cluster or simple random sampling to complete the stratified sampling process.
Step 1: Define the population and subgroups:
Like other types of probability sampling, it also starts by defining the population of interest. It includes the selection of characteristics for stratification so we can place each member of a population in a stratum. We can calculate the number of strata for sampling based on these characteristics.
Step 2: Divide the total population into strata
After defining the characteristics of each subgroup, the next step must be to assign all members of a population a group based on its characteristics. Remember, each sub-group must be mutually exclusive, so there should not be any overlapping characteristics between different strata.
Step 3: Select the appropriate sample size for each stratum
Before selecting the sample size for each stratum, you must decide whether your sample will be proportionate or disproportionate. Proportionate samples are those in which the sample size of each sub-group (stratum) and subgroup proportion in the population are equal.
In contrast, disproportionate sampling occurs when the number of characters in strata is unequal. After this, you can select on sample size. For calculating the proportion of members from each group, you can use the formula (Sample size of the strata = size of total population/population size × layer size). Moreover, the sample size must be large enough to draw useful statistical conclusions about each stratum.
Step 4: Use a simple random sampling process
After completing all these steps, you can select any probability sampling methods (cluster sampling, systematic sampling or simple random sampling to sample data within each stratum.
These four steps will allow you to get stratified samples that are good representatives of a particular population.
When would you use stratified sampling?
For using stratified sampling, you must first divide the population into subgroups so that every member of the population can easily be classified into exactly one stratum. It is best to use it when you think that different subgroups will result in different mean values for the variables under study. Moreover, when the population under study is very diverse or has a different characteristic, stratified sampling helps the researcher sort them logically and ensure the availability of all characteristics in a stratum. Thus, researchers can use this type of random sampling when the population is so diverse, and they have to include numerous characteristics of a population in a study.
Why is stratified sampling used?
Stratified sampling is useful as it allows the researchers to collect a sample that can best represent the total population under study by categorising it into different sub-groups. It is a statistical sampling method, but still, you cannot use it for every data set. For applying this sampling method to a data set, one should have a large population that can be divided into different groups based on characteristics.
Otherwise, stratified sampling does not work well and could not bring useful consequences. Other types of random sampling involve the random collection of data that may mix certain individuals with unique characteristics. A stratified sampling includes collecting data from the entire population, and grouping further ensures that not even a single characteristic is missed.
Final thoughts:
Consequently, stratified sampling divides the population into subgroups based on different characteristics. You can do this type of sampling in four simple steps that include a selection of the population, choosing the characteristics of the subgroup, deciding the appropriate sample size for each stratum and applying other probability sampling techniques to complete the sampling. Thus, the stratified samples are the best representatives that include all potential population members.
