Categorize all the journal entries into different classes of entries (Strata) eg: Decide for the size of the sample (Assume 2000 in this case). Since the units selected for inclusion within the sample are chosen using probabilistic methods, stratified random sampling allows us to make statistical conclusions from the data collected that will be considered to be valid. 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. Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. Imagine that a researcher wants to understand more about the career goals of students at the University of Bath. If the choices made are wrong, the information collected becomes invalid for use in drawing conclusions. Imagine, for instance, you are appointed as the Head of the Investigation Team for a suspected fraud in a company in a fiscal year, you have been provided with 100,000 journal entries that were entered during the period of suspicion. When we select a limited number of elements from large group of elements (population) for sampling, we want to make sure that the samples taken correctly represent the population. If the choices made are wrong, the information collected becomes invalid for use in drawing conclusions. In proportionate sampling, the sample size is proportional to the stratum size. The above figure shows how different types of items are distributed in a random population. 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. David holds a Master of Arts in international journalism from the University of Westminster, UK. Stratified sampling designs can be either proportionate or disproportionate. Stratified sampling designs can be either proportionate or disproportionate. Design use can call for a large sample size, which increases the cost, especially in cases where the lists needed are classified and have to be bought. Sampling: Definition, Advantages and Disadvantages, Probability Sampling, Advantages, Disadvantages, Simple Random Sampling, Advantages, Disadvantages. 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. This saves resources. Unlike the simple random sample and the systematic random sample, sometimes we are interested in particular strata (meaning groups) within the population (e.g., males vs. females; houses vs. apartments, etc.) For the purposes of this example, we will use gender (male/female) as our strata. Stratified random sampling can aid in attaining the precision needed, but it also poses some challenges. The aim of the stratified random sample is to reduce the potential for human bias in the selection of cases to be included in the sample. There may be no single list detailing the population you are interested in. At times, the list is not obtainable and developing it makes the work harder since the strata must be collectively and mutually exclusive. However, we could have also determined the sample size we needed using a sample size calculation, which is a particularly useful statistical tool. It is sometimes hard to classify each kind of population into clearly distinguished classes. Decisions on stratification are made prior to the study. Clearly, a student could only be classified as either male or female. In terms of human populations (as opposed to other types of populations; see the article: Sampling: The basics), some of these populations will be expensive and time consuming to contact, even where a list is available. If you are a non-statistician, these can be confusing. Let's say that the university has roughly 10,000 students. The advantages and disadvantages (limitations) of stratified random sampling are explained below. 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). As a result, the sample size is increased, leading to extra expenses and extended time of study. Calculate the number of samples to be taken from each of the Stratum. As a result, there is a higher precision level which is magnified by a homogeneous population. 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. 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. It is sometimes hard … 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. Furthermore, where the samples are the same size, a stratified random sample can provide greater precision than a simple random sample. [see our article, Sampling: The basics, if you are unsure about the terms unit, sample, strata and population]. Therefore, to calculate the number of female students required in our sample, we multiply 100 by 0.60 (i.e., 0.60 = 60% of the population of students at the university), which gives us a total of 60 female students. In our example, the population is the 10,000 students at the University of Bath. What is the difference between internal & external validity of research study design? Stratified sampling designs can be either proportionate or disproportionate. As this method provides greater precision, greater level of accuracy can be achieved even by using small size of samples. Stratified sampling designs can be either proportionate or disproportionate. It is best suited for strata with varying characteristics because it can only … In the case of human populations, to avoid potential bias in your sample, you will also need to try and ensure that an adequate proportion of your sample takes part in the research. Stratified Random Sampling requires more administrative works as compared with Simple Random Sampling. 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. Imagine that of the 10,000 students, 60% of these are female and 40% male. This saves resources. Faced by a dilemma on which design to use, you may be made to keenly consider the variances and costs within the strata in making the decision.