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. These attributes can be sex, age, income, level of education etc. according
to aims and objectives of the study. 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 figure 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: proportionate and disproportionate. Application of proportionate stratified random sampling technique involves determining sample size in each stratum in a proportionate manner to the entire population. For example, if the entire population for a research is 5000 people, in proportionate stratified random sampling the group can be divided into five strata with 1000 people in each stratum. In disproportionate stratified random sampling, on the contrary, numbers of subjects recruited from each stratum does not have to be proportionate to the total size of the population. If disproportionate stratified random sampling is applied in a research with 5000 people, the population can be divided into five strata with following unequal numbers of population in each stratum: 1000, 1500, 1200, 800 and 500. Accordingly, the application of proportionate stratified random sampling generates more accurate primary data compared to disproportionate sampling. Application of Stratified Sampling: an ExampleSuppose, your dissertation aims to explore leadership styles exercised by medium-level managers at Bayerische Motoren Werke Aktiengesellschaft (BMW AG). 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. 1. 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 depend on aims and objectives of the study. In our case, BMW Group employees are employed across four business segments – automotive, motorcycles, financial services and other entities[1]. Accordingly, each segment can be adapted as stratum to draw sample group members. 2. Numbering each subject within each stratum with a unique identification number. 3. 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 results in the sample group of 16 respondents – BMW Group medium level managers that proportionately represent all four business segments of the company.
Advantages of Stratified Sampling
Disadvantages of Stratified Sampling
My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of sampling methods. 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. John Dudovskiy [1] Annual Report (2020) What random sampling technique is applied when the population is divided into different strata or classes wherein each class must be represented in the study?Stratified sampling
In this method, the population is first divided into subgroups (or strata) who all share a similar characteristic. It is used when we might reasonably expect the measurement of interest to vary between the different subgroups, and we want to ensure representation from all the subgroups.
Which sampling method divide the population into groups?Cluster sampling divides the population into groups, then takes a random sample from each cluster. Both systematic sampling and cluster sampling are forms of random sampling, known as probability sampling, which stands in contrast to non-probability sampling.
What is the sampling technique in which the population is partitioned into several strata and then samples are randomly selected separately from each stratum?With stratified sampling one should: partition the population into groups (strata) obtain a simple random sample from each group (stratum) collect data on each sampling unit that was randomly sampled from each group (stratum)
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