A simple random sample takes a small, basic portion of the entire population to represent the entire data set. Have you observed what happens here ? Example. Time consuming and tedious & data need to be available for strata. Population is nothing but a whole group which we are focusing on for taking the survey for obtaining a certain kind of information. In this type of sampling, we divide the populations into certain classes or categories on the basis of their characteristics / features such as gender, age etc. It becomes necessary to know why do we do sampling why not just do the population count as in whole/census. (For related reading, see: What are the criteria for a simple random sampling? As per the concept you randomly pick 5 wards and then surveyed the people of 5 wards completely. Let’s have a look on this issue. For populations with important distinguishing characteristics, stratified sampling can create a more representative sample. Simple random samples involve the random selection of data from the entire population so each possible sample is equally likely to occur. ), A stratified sample can ensure representation of certain strata for inclusion in the population. Highly representative,  unbiased & can be inferred statistically. Before getting this term lets look at what else need to be understood. Suppose we have survey 200 people in a college out of 10,000 and we have already a data of constituents like we know how many are teachers (25%), staffs (20%), UG students (35%), PG students (20%). The difference between these types of samples has to do with the other part of the definition of a simple random sample. So it is proceeding in a systematic way. Even in census, sampling plays an important role. Hmm it’s a tricky question! Researchers must individually track and verify the data for each stratum for inclusion, which can take a lot more time compared with random sampling. The researchers must take care to ensure the strata do not overlap. Survey – Methods, Templates & Questionnaire, Copyright infringement take down notification template, Regional Planning - Need, Importance & Implementation, Population elements = homogeneous on important parameters, Time consuming and tedious, need complete data -set (may not be updated), Easier than previous one & evenly distributed sample. Member of NOSPlan Let’s see an example. As the name suggests it has something to do with ‘strata’ which means layer, here, we can call it as classes/categories. We must remember that data/survey of an entire population can’t be gathered/facilitated. Are perfect competition models in economics useful? The sample subsets are then combined to create a random sample. Example. Population here not only means people population can be a population of houses – total houses, population of pen – total pen, basically anything.. School of Planning and Architecture, Bhopal. and then we sample out the population proportionally, confused right, wait let’s see through an example. You all have heard this term many times either in Television, Newspaper, or through your professor. I hope you know that arithmetic progression (AP) or a geometric procession (GP) follows a certain pattern or we can say a certain system to acquire their next element in their respective series like what would come next 2, 4, 6, ___ ??? Now we get to know, that we need sampling for various purposes and reasons. Simple random sampling and stratified sampling are both types of probability sampling where each sample has a known probability of being selected. Simple random samples involve the random selection of data from the entire population so each possible sample is equally likely to occur. You were asked to conduct a survey in your city you divided the locality into zones or let’s say you followed the way of wards i.e you decide to conduct sampling in the currently divided zones by the municipality i.e through the way of wards. What is the difference between a simple random sample and a stratified random sample? In addition, by including sufficient sample points from each stratum, the researchers can conduct a separate analysis on each individual stratum. The population is the total set of observations or data. Ltd. Understanding Sampling – Random, Systematic, Stratified and Cluster. Out of the total population whom we are going to take survey, in other terms, how to select what members of the population to sample!! This method has no bias in selecting the sample from the population, so each population element has an equal chance of being included in the sample. This is different from judgmental sampling, where the units to be sampled are handpicked by the researcher. Sample is nothing but a data collection from a part of the whole population. Copyright ©2014 - 2020 Some Rights Reserved. Cluster Sampling, Cluster means ‘Bunch’, ‘Collections’. A random sample is taken from each stratum in direct proportion to the size of the stratum compared to the population. A bunch of grapes, A collection of cars etc. This would be our strategy in order to conduct a stratified sampling. In contrast, stratified random sampling divides the population into smaller groups, or strata, based on shared characteristics. Example. Planning Tank - An associate of Out of Scale India Pvt. I hope you all must have heard about lottery system. 8 right. Shivanshu Shekhar The sampling method is the process used to pull samples from the population. Stratified random samples group the population elements into strata based on certain criteria, then randomly choose elements from each stratum in proportion to the stratum’s size versus the population. Example. ** Note – This article focuses on understanding part of probability sampling techniques through story telling method rather than going conventionally. Let’s move on to our next approach i.e. Simple random samples and stratified random samples differ in how the sample is drawn from the overall population of data. – A polling on a national level via digital platform or as you have seen in certain videos in youtube also that youtubers asking for our suggestions in the form of MCQ in polling button featured in youtube. Overlapping strata would increase the likelihood that some data are included, thus skewing the sample. Key Takeaways Simple random and stratified random samples are statistical measurement tools. I hope you all know that our district/city has ward system (as in India), it may have been demarcated similarly in other countries by different system. Even relatively smaller population many other issues can come through, what if, the data is urgently needed. Remember at your home mom while cooking vegetables put some salt in certain quantity and after sometimes check if it is good enough for serving purpose or not and then proceeding further as per the requirements. Yes, here there are slips of paper having written some numbers/names/anything and then we have to choose among those slips it is random sampling as we have got no idea about about what we choose its a total luck and no bias is observed here. Are there critics of the human development index (HDI). It is generally observed that random sampling is the best way of doing sampling due to not involving any biased factor. But did you notice here we are out in the field for taking sampling and suddenly realized wait! ‘Time & Money’ motivates to take sampling of an entire population instead of census. This often requires a smaller sample size, which can save resources and time. A stratified sample can provide a more accurate representation of the population based on the characteristic used to divide the population into strata.

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