Sampling: Stratified random sampling
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. Accordingly, application of stratified sampling method involves dividing population into different subgroups strata and selecting subjects from each strata in a proportionate manner. The table below illustrates simplistic example where sample group of 10 respondents are selected by dividing population into male and female strata in order to achieve equal representation of both genders in the sample group.
Stratified sampling can be divided into the following two groups: Accordingly, application of proportionate stratified random sampling generates more accurate primary data compared to disproportionate sampling.
Application of Stratified Sampling: You have selected semi-structured in-depth interviews with managers as the most appropriate primary data collection method to achieve the research objectives. Application of stratified random sampling contains the following three stages.
Identification of relevant stratums and ensuring their actual representation in the population.
Apart from gender as illustrated in example above, range of criteria that can be used to divide population into different strata include age, the level of education, status, nationality, religion and others.
Specific patterns of categorization into different stratums depends aims and objectives of the study.
An overview of stratified random sampling, explaining what it is, its advantages and disadvantages, and how to create a stratified random sample. Stratified Random Sampling. In statistics, stratified sampling is a method of sampling from a population. In statistical surveys, Stratified sampling strategies. A stratified random sample is a random sample in which members of the population are State the advantages and disadvantages of using stratified random sampling;. Sampling method in thesis - Download as Powerpoint Presentation .ppt), PDF File .pdf), Text File .txt) or view presentation slides online. Sampling method in. Sampling techniques for thesis writing A- Probability Sampling 1- Simple Random Sampling 2- Stratified Random Sampling 3- Systematic Random Sampling 4- Cluster.
In our case, BMW Group employees are employed across four business segments — automotive, motorcycles, financial services and other entities . Accordingly, each segment can be adapted as stratum to draw sample group members.
Numbering each subject within each stratum with a unique identification number.
Selection of sufficient numbers of subjects from each stratum. It is critically important for samples from each stratum to be selected in a random manner so that the relevance of bias can be minimized.
As it is illustrated in the table below, following the procedure described above go here in the sample group of 16 respondents, BMW Group medium level managers that proportionately represent all four business segments of the company.
Thanks to the choice of stratified random sampling adequate representation of all subgroups can be ensured. Disadvantages of Stratified Sampling The application of stratified random sampling requires the knowledge of strata membership a priori. The requirement to be able to easily distinguish between strata in the sample frame may create difficulties in practical levels.
Research process may take longer and prove to be more expensive due to the extra stage in the sampling procedure.
The choice of stratified sampling method adds certain complexity to the analysis plan. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words.
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