True about cluster sampling are all except
Sample size is same as that of simple random sampling Repeat from Nov 08 Sampling When a large propoion of individuals or items or units have to be studied, we take a sample. It is easier and more economical to study the sample than the whole population or universe. A sample is a subset of the population- the pa that is actually being observed or studied. It is impoant to ensure that the group of people or items include in the sample are representative of the whole population to be studied. Various sampling methods are used to select the sample groups. Sanzpling methods Simple random sampling: This is done by assigning a number to each unit in the population to be sampled and then using lottery method or a table of random numbers to select units to be included in the sample. The principle here is that every unit of the population has an equal chance of being selected. This method is applicable when the population is small, homogenous and readily available such as patients coming to hospital or lying in wards. Systematic sampling: In this method the first unit of the sample is selected at random and the subsequent units are selected in a systematic way i.e every fifth or tenth unit at regular intervals. For example in a malaria survey to take a 5 percent sample, all the houses are numbered first. Then the first house is selected at random between 1 and 5. then every 5(h house is selected from that point on. This method is popularly used in those cases when a complete list of population from which samples is to be drawn, is available. It is more often applied to field studies when the population is large, scattered and not homogenous. Stratified sampling: This method is used when the population is heterogenous with regards to characteristic under study. The population under study is first divided into homogenous groups or classes called strata and then the sample is drawn from each stratum at random in propoion to its size. This method of sampling gives representation to all strata of society or population such as selecting sample from defined areas, classes, religions, ages or sexes. For example a population to be sampled consists of people of different religions like Hindus, Muslims, Sikhs, Christians etc. In stratified sampling the population is first divided into religious groups and then samples are drawn from each group in propoion to its size. This method is paicularly useful where one is interested in analyzing the data by ceain characteristic of the population, viz Hindus, Muslims, Christians, age-groups etc. Cluster sampling: A cluster is a randomly selected group. This method is used when units of population are natural groups or clusters such as villages, wards, blocks, slums, schools etc. In this technique clusters are chosen at random from the entire population, and then these clusters are sampled. Each cluster should be a small scale representation of the total population. In single-stage cluster sampling, all the elements from each of the selected clusters are used. In two-stage cluster sampling, a random sampling technique is applied to the elements from each of the selected clusters. The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so analysis is done on a population of clusters (at least in the first stage). In stratified sampling, the analysis is done on elements within strata. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are studied. The main objective of cluster sampling is to reduce costs by increasing sampling efficiency. This contrasts with stratified sampling where the main objective is to increase precision. Cluster sampling is most often used to evaluate vaccination coverage in Expanded Programme of Immunization (EPI) and Universal Immunization Programme (U1P), where only 210 children, taking 7 from each of the 30 clusters are examined. Cluster sampling gives a high standard error but the data collection in this method is simpler and involves less time and cost than in other sampling techniques. Multistage sampling: In this method sampling procedure is carried out in several stages using random sampling techniques. Multistage sampling is used when the population units arranged in hierarchal groups. For examples farmers in India may be grouped into various states, then into districts, then into talukas, fuher into villages within the talukas and then farmers within the villages. Multistage sampling of this population of farmers would consist of first randomly selecting districts within each state, then selecting a subset of talukas within the selected districts, then selecting a subset of villages and finally selecting a sample of farmers within the villages. Multiphase sampling: In this method, pa of information is collected from the whole sample and pa from the subsample. For example in a tuberculosis survey, in the first phase, physical examination or Mantoux test may be done in all the cases; in the second phase X-ray of the chest may be done in Mantoux positive cases and in those with clinical symptoms: finally the sputum may be examined in X-ray positive cases. Thus number of samples in the second and third phases would become successively smaller and smaller. Survey by multiphase sampling would involve less cost and labour and would be more purposeful.
sample size is same as that of simple random sampling
it is a two stage sampling
it is cheaper than other methods of sampling
it has the disadvantage of higher sampling error