Therefore, it is generally cheaper relative to the simple random or stratified sampling as it requires fewer administrative and travel expenses. This approach is ideal only if the characteristic of interest is distributed homogeneously across. An example of cluster sampling is area sampling or geographical cluster sampling. For instance, information may be available on the geographical location of the area, e. Sampling theory chapter 9 cluster sampling shalabh, iit kanpur page 4 estimation of population mean. Households were recruited using a stratified two stage cluster sampling method. Stratified random sampling is a random sampling method where you divide members of a population into strata, or homogeneous subgroups. Start studying systematic, stratified, or cluster sampling learn vocabulary, terms, and more with flashcards, games, and other study tools. In the second stage, which is the clustering sampling stage, one or.
Multistage sampling is a more complex form of cluster sampling. 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 stratified random sampling intends to guarantee that the sample represents specific subgroups or strata. Because the sampling design is clustered and all students from each selected cluster are interviewed, the sampling weights equal the inverse of. Stratified sampling jeff wooldridge labour lectures, eief. Stratified random sampling is a type of probability sampling using which researchers can divide the entire population into numerous nonoverlapping, homogeneous strata. Timss 2007 used a twostage stratified cluster sampling design. There is a big difference between stratified and cluster sampling, which in the first sampling technique, the sample is created out of the random selection of elements from all the strata while in the second method, all the units of the randomly selected clusters form a sample. The individual cities would be the clusters in this case.
In this method, the elements from each stratum is selected in proportion to the size of the strata. In cluster sampling, the clusters are constructed such that they are within heterogeneous and among homogeneous. Here is output from minitab that describes the data from each stratum. Cluster sampling definition, advantages and disadvantages. Estimation of population mean under stratified random sampling note that the population mean is given by x h l h h h l h n i hi l h w x n x h. All observations in the selected clusters are included in the sample. Appendix a illustrates a ranuni method to select stratified samples. A modified clustersampling method for postdisaster rapid. Stratified twostage cluster sample design timss and pirls. Cluster sampling cluster sampling is a sampling method where the entire population is divided into groups, or clusters, and a random sample of these clusters are selected. Stratified sampling jeff wooldridge michigan state university labour lectures, eief october 1819, 2011 1.
Aside from this, sampling makes the collection of data faster because it focuses only on a small part of the population. The clustersampling method can be used to conduct rapid assessment of health and other needs in communities affected by natural disasters. In stratified random sampling or stratification, the strata. In multistage sampling, the cluster units are often referred to as primary sampling units and the elements within the clusters secondary sampling units. In the first stage, about 150 schools were selected according to some variables of interest, such as school types or locations. In terms of sampling fractions we have,f1 f2 fh f which is the overall sampling fraction.
Discuss reasons for stratified or cluster sampling. Cluster sampling is considered less precise than other methods of sampling. Cluster sampling has some parallels to stratified sampling, in that both divide the population into groups clusters or strata and make selections. Difference between cluster and stratified sampling. N in the output denotes numbers of data usually a sample is selected by some probability design from each of the l strata in the population, with selections in different strata independent of each other.
Stratified random sampling is simple and efficient using proc freq and proc. Systematic sampling is probably the easiest one to use, and. For example, one might divide a sample of adults into subgroups by age, like. This sampling method is also called random quota sampling. Koether hampdensydney college tue, jan 31, 2012 robb t. Random cluster sampling 1 done correctly, this is a form of random sampling population is divided into groups, usually geographic or organizational some of the groups are randomly chosen in pure cluster sampling, whole cluster is sampled. As opposed, in cluster sampling initially a partition of study objects is made into mutually exclusive and collectively exhaustive subgroups, known as a cluster. In simple multistage cluster, there is random sampling within each randomly chosen. Final members for research are randomly chosen from the various strata which leads to cost reduction and improved response efficiency. How do systematic sampling and cluster sampling differ. Explanation for stratified cluster sampling the aim of the study was to assess whether the famine scale proposed by howe and devereux provided a suitable definition of famine to guide future humanitarian response, funding, and accountability. Cluster and stratified sampling these notes consider estimation and inference with cluster samples and samples obtained by stratifying the population. Researchers investigated the suitability of a newly developed famine scale as an international definition of famine to guide humanitarian response, funding, and accountability.
Stratified random sampling can be tedious and time consuming job to those who are not keen towards handling such data. Sampling theory chapter 4 stratified sampling shalabh, iit kanpur page 5 now 1 1 1 1 k stii i k i i i ey ney n ny n y thus yst is an unbiased estimator of y. Reduce the error in cluster sampling by creating strata of clusters. Based on n clusters, find the mean of each cluster separately based on all the units in every cluster. When sampling clusters by region, called area sampling. Stratified random sampling requires more administrative works as compared with simple random sampling. Cluster sampling has been described in a previous question. It is modelled on whos expanded programme on immunization method of estimating immunization coverage, but has been modified to provide 1 estimates of the population remaining in an area, and 2. Cluster sampling a cluster sample is a probability sample in which each sampling unit is a collection or a group of elements. Sampling stratified vs cluster linkedin slideshare. We can also get more precise estimation by changing the sampling scheme.
The sampling frame the sampling frame is the list of ultimate sampling entities, which may be people, households, organizations, or other units of analysis. The sample design is a stratified sample where the strata are students classes. Difference between stratified and cluster sampling schemes in stratified sampling, the strata are constructed such that they are within homogeneous and among heterogeneous. Virtually all sample designs for household surveys, both in developing and developed countries, are complex because. In general terms, the estimate for the population mean used in stratified sampling yst. Understanding stratified samples and how to make them. Stratified random sampling the way in which was have selected sample units thus far has required us to know little about the population of interest in advance of selecting the sample. Measuring all the elements in the selected clusters may be prohibitively expensive, or not even necessary. Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster. A stratified twostage cluster sampling method was used for the inclusion of participants. Randomly sampling a large number of clusters also applies to many panel data sets, where the crosssectional population size is large say, individuals, firms. The sampling weights are the reciprocals of the probabilities of selections.
A stratified sample is one that ensures that subgroups strata of a given population are each adequately represented within the whole sample population of a research study. Surveys are used in all kinds of research in the fields of marketing, health, and sociology. Alternative estimation method for a threestage cluster sampling in finite population. The main focus is on true cluster samples, although the case of applying clustersample methods to panel data is treated, including recent work where the sizes. The special case where from each stratum a simple random sample is drawn is called a stratified random sample. Stratified random sampling definition investopedia.
It is hoped that each cluster by itself is representative of the. The design effect of twostage stratified cluster sampling. Hence, there is a same sampling fraction between the strata. In stratified sampling, a twostep process is followed to divide the population into subgroups or strata. Simple random sampling is the most recognized probability sampling procedure. Alternative estimation method for a threestage cluster. In stratified sampling, the strata are constructed such that they are. There are more complicated types of cluster sampling such as twostage cluster. Accordingly, application of stratified sampling method involves dividing population into. Stratified random sampling is a method of sampling that involves the division of a population into smaller subgroups known as strata.
A market research firm conducts a survey among undergraduate students at a certain university to evaluate three new web designs for a commercial web site targeting undergraduate students at the university. It is sometimes hard to classify each kind of population into clearly distinguished classes. The corresponding numbers for the sample are n, m and k respectively. Cluster sampling note that it is the clusters that are selected at random, not the individuals. Stratified sampling and cluster sampling that are most commonly contrasted by the people. For the first sampling stage, schools are sampled with probabilities proportional to their size pps from the list of all schools in the population that contain eligible. Next, we list the steps from doing a stratified random sample and then determine the advantage of doing a stratified sample over a cluster sample. Cluster sampling is often more economical or more practical than stratified sampling or. Consider the mean of all such cluster means as an estimator of. Difference between stratified and cluster sampling with. Stratified sampling offers significant improvement to simple random sampling. I consider the populations unknown because i couldnt get the exact number of the population. Simple random sampling is the most recognized probability sam. They are usually done by taking a sample of a population because making a survey on the entire population would be expensive.