stratified random sampling advantages and disadvantages

Imagine that of the 10,000 students, 60% of these are female and 40% male. A stratified random sample can only be carried out if a complete list of the population is available. Stratified sampling designs can be either proportionate or disproportionate. Categorize all the journal entries into different classes of entries (Strata) eg: Decide for the size of the sample  (Assume 2000 in this case). The stratified random sample also improves the representation of particular strata (groups) within the population, as well as ensuring that these strata are not over-represented. To give an authentic conclusion, sample statistics, such as mean, standard deviation, standard error and significance levels, are used. Furthermore, imagine extending the sampling requirements such that we were also interested in how career goals changed depending on whether a student was an undergraduate or graduate. Now, you can examine the number of items as stated above and reach your conclusion. 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. This means that we need to select 60 female students and 40 male students for our sample of 100 students. Decisions on stratification are made prior to the study. Analysing the data is also complex because you have to consider the number and size of strata population, size of total population and sample population. The advantages and disadvantages (limitations) of stratified random sampling are explained below. 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 10,000 students. Good research papers may be your ultimate goal, but achieving this can amount to a complex task that calls for careful consideration. Assuming that your list has all the contact details of potential participants in the first instance, managing the different ways (postal, telephone, email) that may be required to contact your sample may be challenging, not forgetting the fact that your sample may also be geographical scattered. It is sometimes hard … You can read about this later in the article under Disadvantages (limitations) of stratified random sampling. One way of selecting samples from the population is by dividing the whole population into small strata consisting up of elements with some similar characteristics and then choosing such number of samples from each of them so as to proportional to the size of the stratum. We explain how this is achieved in the next section: Creating a stratified random sample. In our example, the population is the 10,000 students at the University of Bath. The method involves seven steps in coming up with the sample, making it a lengthy process. Stratified sampling designs can be either proportionate or disproportionate. The above figure shows how different types of items are distributed in a random population. As a result, there is a higher precision level which is magnified by a homogeneous population. Stratified random sampling can aid in attaining the precision needed, but it also poses some challenges. Disproportionate stratification provides for varying sample size for each stratum. If we do the same for male students, we get 40 students (i.e., 40% of students are male, where 100 x 0.40 = 40). Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. If the choices made are wrong, the information collected becomes invalid for use in drawing conclusions. David holds a Master of Arts in international journalism from the University of Westminster, UK. Stratified random sampling can aid in attaining the precision needed, but it also poses some challenges. This may have suggested that we needed a larger sample size; perhaps as many as 400 students. The method involves seven steps in coming up with the sample, making it a lengthy process. Many lists will not be in the public domain and their purchase may be expensive; at least in terms of the research funds of a typical undergraduate or master's level dissertation student. We need to identify all 10,000 students at the University of Bath. Furthermore, where the samples are the same size, a stratified random sample can provide greater precision than a simple random sample. These 10,000 students are our population (N). As a result, we would end up with two lists, one detailing all male students and one detailing all female students. It is best suited for strata with varying characteristics because it can only … If you are a non-statistician, these can be confusing. A disadvantage is when researchers can’t classify every member of the population into a subgroup. This saves resources. With the stratified random sample, there is an equal chance (probability) of selecting each unit from within a particular stratum (group) of the population when creating the sample. In order to select a sample (n) of students from this population of 10,000 students, we could choose to use a simple random sample or a systematic random sample. Since this is unlikely to be the case, the number of units that should be selected for each stratum (i.e., the number of male and female students selected) will vary. As a result, the stratified random sample provides us with a sample that is highly representative of the population being studied, assuming that there is limited missing data.

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