For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study. Extra care has to be taken to control biases when determining sampling techniques. There are two main types of sampling: The difference between the two types is whether or not the sampling selection involves randomization.
Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. Following is a discussion of probability and non-probability sampling and the different types of each. Probability Sampling — Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur:.
Non-probability Sampling — Does not rely on the use of randomization techniques to select members. This is typically done in studies where randomization is not possible in order to obtain a representative sample. Bias is more of a concern with this type of sampling.
The different types of non-probability sampling are as follows:. The following Slideshare presentation, Sampling in Quantitative and Qualitative Research — A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.
Examples of Data Collection Methods — Following is a link to a chart of data collection methods that examines types of data collection, advantages and challenges. Qualitative and Quantitative Data Collection Methods - The link below provides specific example of instruments and methods used to collect quantitative data. In order to save time and money, an analyst may take on a more feasible approach by selecting a small group from the population. The small group used is referred to as a sample size , which is a subset of the population that is used to represent the population.
There are a number of ways a sample may be selected from a population, one of which is the stratified random sampling method. A stratified random sampling involves dividing the entire population into homogeneous groups which are called strata singular is stratum.
Random samples are then selected from each stratum. For example, consider an academic researcher who would like to know the number of MBA students that got a job within three months of graduation in He will soon find that there were almost , MBA graduates for the year. He might decide to just take a simple random sample of 50, grads and run a survey, or better still, divide the population into strata and take a random sample from the strata.
To do this, he could create population groups based on gender, age range, race, country of nationality, and career background. A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. These subsets of the strata are then pooled to form a random sample. Stratified random sampling ensures that each subgroups of a given population is adequately represented within the whole sample population of a research study.
Stratification can be proportionate or disproportionate. In a proportionate stratified method, the sample size of each stratum is proportionate to the population size of the stratum.
For example, if the researcher wanted a sample of 50, using age range, the proportionate stratified random sample will be obtained using this formula: Assuming a population size of , MBA grads per year,. This is known as proportionate stratification as opposed to disproportionate stratification , where the sample size of each of the stratum is not proportionate to the population size of the same stratum.
With stratified random sampling, there would an equal chance probability that each female or male student could be selected for inclusion in each stratum of our sample. However, in line with proportionate stratification, the total number of male and female students included in our sampling frame would only be equal if 5, students from the university were male and the other 5, students were female. Since this is unlikely to be the case, the number of units that should be selected for each stratum i.
We explain how this is achieved in the next section: Creating a stratified random sample. To create a stratified random sample, there are seven steps: In our example, the population is the 10, students at the University of Bath. The population is expressed as N. Since we are interested in all of these university students, we can say that our sampling frame is all 10, students.
If we were only interested in female university students, for example, we would exclude all males in creating our sampling frame, which would be much less than 10, If we wanted to look at the differences in male and female students, this would mean choosing gender as the stratification , but it could similarly involve choosing students from different subjects e. We need to identify all 10, students at the University of Bath.
If you were actually carrying out this research, you would most likely have had to receive permission from Student Records or another department in the university to view a list of all students studying at the university. You can read about this later in the article under Disadvantages limitations of stratified random sampling.
As with the simple random sampling and systematic random sampling techniques, we need to assign a consecutive number from 1 to NK to each of the students in each stratum. As a result, we would end up with two lists, one detailing all male students and one detailing all female students. Let's imagine that we choose a sample size of students. The sampl e is expressed as n.
This number was chosen because it reflects the limit of our budget and the time we have to distribute our questionnaire to students. However, we could have also determined the sample size we needed using a sample size calculation, which is a particularly useful statistical tool. This may have suggested that we needed a larger sample size; perhaps as many as students.
We need to ensure that the number of units selected for the sample from each stratum is proportionate to the number of males and females in the population. To achieve this, we first multiply the desired sample size n by the proportion of units in each stratum.
Therefore, to calculate the number of female students required in our sample, we multiply by 0. If we do the same for male students, we get 40 students i. This means that we need to select 60 female students and 40 male students for our sample of students. Now that we have chosen to sample 40 male and 60 female students, we still need to select these students from our two lists of male and female students see STEP FOUR above.
Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or strata.
Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata.
Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling or stratification, the strata are formed based on members' shared attributes or . “According to Webster (), to research is to search or investigate exhaustively. It is a careful or diligent search, studious inquiry or examination especially investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light.
Stratified random sampling. Stratified random sampling is a type of probability sampling technique [see our article Probability sampling if you . Stratified random sampling is a type of probability sampling using which a research organization can branch off the entire population into multiple non-overlapping, homogeneous groups (strata) and randomly choose final members from the various strata for research which reduces cost and improves.