Stratified sampling is often used where there is a great. Comparison of stratified sampling with quota sampling. Population and samples parameters and statistics types of sampling simple random, stratified, systematic and cluster sampling, sampling distributions. Whilst stratified random sampling is one of the gold standards of sampling techniques, it presents many challenges for students conducting. Compared to simple random sampling and stratified sampling, cluster sampling has advantages and disadvantages. Random sampling removes an unconscious bias while creating data that can be analyzed to benefit the general demographic or population group being studied. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study.
Three techniques are typically used in carrying out step 6. Sampling plans zsimple random sample zeach sampling unit has an equal probability of being sampled with each selection. Stratified random sampling helps minimizing the biasness in selecting the samples. Stratified random sampling provides the benefit of a more accurate sampling of a population, but can be disadvantageous when researchers cant classify every member of the population into a subgroup. It also ensures, at the same time that each unit has an equal probability of inclusion in the sample.
Ch7 sampling techniques university of central arkansas. This type of sampling is less likely than probability sampling to produce representative samples. Stratified sampling in this type of sampling method, population is divided into groups called strata based on certain common characteristic like geography. Many of these are similar to other types of probability sampling technique, but with some exceptions. The necessary sample size can be reduced due to lower variability within groups, therefore saving time and money. Sampling and sampling methods volume 5 issue 6 2017 ilker etikan, kabiru bala.
We explored simulated estimates of urban population based on survey data from bo, sierra leone, using two approaches. Stratified random sampling is a random sampling method where you divide members of a population into strata, or homogeneous subgroups. The strata is formed based on some common characteristics in the population data. The advantage and disadvantage of implicitly stratified sampling. In case of stratified sampling, variance between 0, i. In choicebased sampling, 7 the data are stratified on the target and a sample is taken from each stratum so that the rare target class will be more represented in the sample. Systematic sampling is similar to arithmetic progression.
Advantages used when research budget is limited very extensively usedunderstood no need for list of population elements disadvantages variability and bias cannot be measuredcontrolled time consuming. Lets look at the advantages and disadvantages of several other sampling. In stratified sampling, we divide the population into nonoverlapping subgroups called strata and then use simple random sampling method to select a proportionate number of individuals from each strata. Total sample size k i i nn population n units stratum 1. Estimators for systematic sampling and simple random sampling are identical. This method of randomly selecting individuals seeks to select a sample size that is an unbiased representation of the population. Based on n clusters, find the mean of each cluster separately based on all the units in every cluster. Advantages and disadvantages of sampling techniques by. Systematic random sampling, stratified types of sampling, cluster sampling, multistage sampling, area sampling. A manual for selecting sampling techniques in research munich. The strata must be non overlapping and together constitute the whole population.
One of the advantages of using the cluster sampling is economical. Stratified sampling offers several advantages over simple random sampling. This process is experimental and the keywords may be updated as the learning algorithm improves. For example, if a class has 20 students, 18 male and 2 female, and a researcher wanted a sample of 10, the sample would consist of 9 randomly chosen males and 1 randomly chosen. A method of sampling designed to ensure that the sample has certain characteristics, usually that it is representative of the population on key variables. Can help find a more accurate answer to the research question than simple random sampling, because groups are selected accurately. Accordingly, application of stratified sampling method involves dividing population into. Chapter 11 systematic sampling the systematic sampling technique is operationally more convenient than simple random sampling. Proportionate allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. Standard errors sampling from normal and non normal populations central limit theorem finite population multiplier population any well defined set group of objects about which a statistical enquiry is being. Samples are selected independently from each stratum. Quota sampling falls under the category of nonprobability sampling. Stratified sampling selfselection sampling cluster sampling snowball sampling probability sampling 1.
For example, given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling. The advantages and disadvantages of random sampling show that it can be quite effective when it is performed correctly. 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. Simple random sampling in this technique, each member of the population has an equal chance of being selected as subject. Sampling involves the selection of a portion of the population being studied. Stratified sampling is applied when population from which sample to be drawn from the group does not have homogeneous group of stratified sampling technique, in generally it is used to obtain a representative of a good sample. Each of the sampling techniques described in this chapter has advantages and. In some casesnew jersey, for exampleseasonal and annual water use data are. In taking a sample of villages from a big state, it is more administratively convenient to consider the districts as strata so that the administrative set up at district level may be used. The main advantages of stratified sampling are that parameter estimation of each layer can be obtained. Nonprobability sampling the elements that make up the sample, are selected by nonrandom methods. The entire process of sampling is done in a single step with each subject.
Easy method as convenience sampling allows the pollster to draw samples from the zone where she gets comfortable, the sampling method becomes easier for the pollster as compared to stratified random sampling, systematic random sampling and others. And, because variance between stratified sampling variance is lower than that of srs. Entire population sampling unit unbiased estimator simple random sample stratify random sample these keywords were added by machine and not by the authors. Stratified sampling the stratum weight, h, is the probability that a randomly selected unit belongs to stratum h, i. A manual for selecting sampling techniques in research 4 preface the manual for sampling techniques used in social sciences is an effort to describe various types of sampling methodologies that are used in researches of social sciences in an easy and understandable way. Cluster sampling definition, advantages and disadvantages. The first two theorems apply to stratified sampling in general and are not restricted to stratified random sampling.
The elements in the population are divided into layersgroups strata based on their values on oneseveral auxiliary variables. Which of the following are advantages of stratified over. Download pdf show page numbers stratified random sampling usually referred to simply as stratified sampling is a type of probability sampling that allows researchers to improve precision reduce error relative to simple random sampling srs. This is contrary to probability sampling, where each member of the population has a known, nonzero chance of being selected to participate in the study necessity for nonprobability sampling can be explained in a way that for some studies it is not. Stratified type of sampling divide the universe into several sub. The cluster method comes with a number of advantages over simple random sampling and stratified sampling. Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata.
Advantages a it is a good representative of the population. Stratified sampling is a sampling technique where the researcher divides or stratifies the target group into sections, each representing a key group or characteristic that should be present in the final sample. Raj, p4 all these four steps are interwoven and cannot be considered isolated from one another. Stratified random sampling ensures that no any section of the population are underrepresented or overrepresented. Jan 27, 2020 advantages of stratified sampling using a stratified sample will always achieve greater precision than a simple random sample, provided that the strata have been chosen so that members of the same stratum are as similar as possible in terms of the characteristic of interest. In order to fully understand stratified sampling, its important to be. Explicit stratified sampling, on the other hand, might involve sorting people into a. Nov 22, 20 researchers investigated the suitability of a newly developed famine scale as an international definition of famine to guide humanitarian response, funding, and accountability. Apart from that, the statistical analyses used in the case of cluster sampling are also more complex than the ones used in case of stratified sampling. Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. 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. If the population is homogeneous with respect to the characteristic under study, then the method of simple random sampling will yield a homogeneous sample, and. Optimum allocation in stratified random sampling leads to a constrained optimization problem which is usually solved by any of the following three approaches.
That is, data are collected for all water users withdrawing amounts greater than a specified threshold that varies from state to state. Sampling theory chapter 9 cluster sampling shalabh, iit kanpur page 4 estimation of population mean. Samples are then pulled from these strata, and analysis is performed to make inferences about the greater population of interest. Can lead to higher precision because there is less variability within the groups given that similar characteristics are grouped together. The sampling distribution of means for stratified sampling is generally less concentrated than that obtained from simple random sampling. Number of sampling units in ith strata 1 k i i nn ni. Apr, 2019 stratified random sampling provides the benefit of a more accurate sampling of a population, but can be disadvantageous when researchers cant classify every member of the population into a subgroup. Several states with extensive water use databases rely upon the census approach. Stratified random sampling intends to guarantee that the sample represents specific subgroups or strata. Disadvantages a it is a difficult and complex method of samplings. We may select all ssus for convenience or few by using a specific element sampling techniques such as simple random sampling.
The advantages and disadvantages limitations of stratified random sampling are explained below. Sampling methods chapter 4 it is more likely a sample will resemble the population when. In this method of sampling, the first unit is selected with the help of random numbers, and the remaining units. Stratified sampling presented by waiton sherekete and tafara mapetese 1 2. We may select the psus by using a specific element sampling techniques, such as simple random sampling, systematic sampling or by pps sampling. Choicebased sampling is one of the stratified sampling strategies. This approach is ideal only if the characteristic of interest is distributed homogeneously across the population. Distinctive features as with all sampling methods, stratified sampling is used when there. Since cluster sampling selects only certain groups from the entire population, the method requires fewer resources for the sampling process. In probability sampling each element in the population has a known nonzero chance of being selected through the use of a random selection procedure such as simple random sampling. In this method, the elements from each stratum is selected in proportion to the size of the strata. Simple random sampling, systematic sampling, stratified sampling fall into the category of simple sampling techniques. Sampling techniques free download as powerpoint presentation.
Therefore, systematic sampling is used to simplify the process of selecting a sample or to ensure ideal dispersion of sample units throughout the population. Sampling theory chapter 4 stratified sampling shalabh, iit kanpur page 2 notations. Stratified random sampling provides better precision as it takes the samples proportional to the random population. Study on a stratified sampling investigation method for. Stratified sampling an overview sciencedirect topics. Hence, there is a same sampling fraction between the strata. Highly representative of all groups of the population. In cases where the estimates of the population characteristics are needed not only for the entire population but also for its different subpopulations, one should treat such subpopulations as strata. Then samples are selected from each group using simple random sampling method and then survey is conducted on people of those samples. Statisticians attempt for the samples to represent the population in question. Stratified random sampling provides the benefit of a more accurate sampling of a population, but can be disadvantageous when researchers. Sampling theory chapter 4 stratified sampling shalabh, iit kanpur page 7 3. A manual for selecting sampling techniques in research. The advantages of performing sampling are lower cost and faster data collection than measuring.
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. Number of sampling units to be drawn from ith stratum. Raghunath arnab, in survey sampling theory and applications, 2017. Administrative convenience can be exercised in stratified sampling. In nonprobability sampling also known as nonrandom sampling not all members of the population has a chance of participating in the study. Proportional stratified sampling pdf stratified sampling offers significant improvement to simple random. If controls can be in place to remove purposeful manipulation of the. This approach is ideal only if the characteristic of interest is distributed homogeneously across. Understanding stratified samples and how to make them. Sampling and sampling distributions bias of an estimator.
Stratified sampling is a probability sampling procedure in which the target population is first separated into mutually exclusive, homogeneous segments strata, and then a simple random sample is selected from each segment stratum. Stratified sampling meaning in the cambridge english. With ess, unbiased estimation of the standard errors of survey estimates is possible, provided that sampling strata membership is identified on the survey dataset. There is a need for better estimators of population size in places that have undergone rapid growth and where collection of census data is difficult.
Suppose that the sampling strategy to be used for a particular survey is required to involve both a stratified sampling design and the classical ratio estimator, but that, within each stratum, a choice is allowed between simple random sampling and simple balanced sampling. Optimum allocation in stratified random sampling via. Stratified random sampling usually referred to simply as stratified. Ensures a high degree of representativeness of all the strata or layers in the population. N i is the number of sampling units in stratum i n i is the sample size in stratum i n is the total number of sampling units in the population. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset a statistical sample of individuals from within a statistical population to estimate characteristics of the whole population. Stratified sampling of neighborhood sections for population. Disproportionate stratified sampling depends upon considerations.
The sampling frame is the list of ultimate sampling entities, which may be people, households, organizations, or other units of analysis. Two advantages of sampling are lower cost and faster data collection than measuring the. Stratified sampling is a process used in market research that involves dividing the population of interest into smaller groups, called strata. When using proportional allocation in stratified random sampling, one samples from each of the strata in proportion to their respective variabilities. Sampling strategies and their advantages and disadvantages. Study on a stratified sampling investigation method for resident. Stratified sampling is used in most largescale surveys because of its various advantages, some of which are described below. Chapter 5 choosing the type of probability sampling 1 stratified sampling what is stratified sampling. This work is licensed under a creative commons attribution. Sampling methods presentation download as powerpoint presentation. Pdf the advantage and disadvantage of implicitly stratified sampling. Explicit stratified sampling ess and implicit stratified sampling iss are alternative methods for controlling the distribution of a survey sample, thereby potentially. The sample size is larger the method used to select the sample utilizes a random process nonrandom sampling methods often lead to results that are not representative of the population example.
70 44 98 189 1130 668 368 827 467 1193 1338 1130 103 228 1187 1228 281 1003 1085 69 748 988 1222 1236 142 461 1080 841 1471 1010 577 538 1279 1432 909 953 8 943 1161 1072 1331 148 1273 1465