This method of samplingĀ is called Stratified Random Sampling and it is a kindĀ of Probability Sampling. Stratified sampling designs can be either proportionate or disproportionate. Stratified Random Sampling ensures that no any section of the population are underrepresented or overrepresented. For the purposes of this example, we will use gender (male/female) as our strata. It also requires that a record of the population being studied is made available. The method involves seven steps in coming up with the sample, making it a lengthy process. Good research papers may be your ultimate goal, but achieving this can amount to a complex task that calls for careful consideration. We need to stratify the population. Let's imagine that we choose a sample size of 100 students. If you are a non-statistician, these can be confusing. If you choose to use stratified random sampling, you proceed as follows: Number of items to be represented by a single sample, = Total Number of Elements in the Population $\div$ The number of Samples to be taken, Now, the numbers of samples to be taken from each of the stratum. Whilst stratified random sampling is one of the 'gold standards' of sampling techniques, it presents many challenges for students conducting dissertation research at the undergraduate and master's level. 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. Relative to the simple random sample, the selection of units using a stratified procedure can be viewed as superior because it improves the potential for the units to be more evenly spread over the population. As a result, there is a higher precision level which is magnified by a homogeneous population. As a result, the sample size is increased, leading to extra expenses and extended time of study. As an undergraduate and master's level dissertation student, you may simply not have sufficient time to do this. As a result, we would end up with two lists, one detailing all male students and one detailing all female students. These strata are expressed as H. For example, imagine we were interested in comparing the differences in career goals between male and female students at the University of Bath. 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). You can read about this later in the article under Disadvantages (limitations) of stratified random sampling. 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. As a result, it may be difficult and time consuming to bring together numerous sub-lists to create a final list from which you want to select your sample. Stratified random sampling is a type of probability sampling technique [see our article Probability sampling if you do not know what probability sampling is]. What is the difference between internal & external validity of research study design? 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. In proportionate sampling, the sample size is proportional to the stratum size. 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). If the choices made are wrong, the information collected becomes invalid for use in drawing conclusions. 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. Stratified random sampling can aid in attaining the precision needed, but it also poses some challenges. Advantages and disadvantages (limitations) of stratified random sampling, STEP TWO: Choose the relevant stratification, STEP FOUR: List the population according to the chosen stratification, STEP SIX: Calculate a proportionate stratification, STEP SEVEN: Use a simple random or systematic sample to select your sample. We explain how this is achieved in the next section: Creating a stratified random sample. To give an authentic conclusion, sample statistics, such as mean, standard deviation, standard error and significance levels, are used.