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Sampling biostatistics.pptx

The document provides an overview of sampling in survey work, outlining its key components such as selection and estimation procedures. It distinguishes between probability and non-probability sampling methods, detailing various sampling techniques including simple random sampling, stratified sampling, and cluster sampling. Additionally, it addresses errors associated with sampling, advantages of sampling over census methods, and specific non-probability sampling types like convenience and snowball sampling.

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Sampling
introduction • Most survey work involves sampling from finite populations. • There are two parts to any sampling strategy (design). – First, there is a selection procedure, the manner in which sampling units are selected from a population. – Second, there is an estimation procedure that prescribes how inferences are to be drawn from sample to the population
• Sampling is procedure or process of selecting some units from the population with some common characteristics and is primarily concerned with the collection of data of some selected units of the population. • Census is another method of data collection and is defined as a complete enumeration of the population.
Definition of sampling terms Sample It is a part of population, which is selected at random Sampling • Sampling is a process of selecting a sample from the population Sampling unit (element) • Any basic item which is selected for the purpose of sampling – Example: children <5 years etc
Definition of sampling terms Sampling Frame • A complete list of population from which a sample is to be selected – Example: Voters list, Name of students in a university Sampling fraction • Ratio between sample size and population size – Example: 100 out of 2000 (5%)
Sampling Error • Since sample is a part of population, the result based on the sampled observations will not be equal to that of population values. There must be some difference, which is inevitable. This difference is known as error. • This error is arising due to drawing inferences about the population on the basis of sampled observations, therefore, it is termed as sampling error. • For Instance the prevalence of tuberculosis based on a sample cannot be identical to its prevalence in the population.
Note • The sampling error usually decreases as the sample size increases.
Non sampling error • Error arising from the causes not associated with the sampling process is known as non- sampling error. • It is common, both to complete enumeration and sample surveys and Includes – (i) response error – (ii) non-response error – (iii) measurement and coding error – (iv) improper method for statistical analysis – (v) non- coverage of population – (vi) interviewers error – vii) data entry error etc. • As the sample size increases, non-sampling error increases.
Advantages to select a sample from a population It Includes • A sample is a part of population; the information can be collected more cheaply and more rapidly as compared to complete enumeration. • A sample makes it possible to concentrate on individual units and to obtain relevant information comprehensively and accurately. • Selection of appropriate sampling design reduces non-sampling error. • More precise results can be obtained by survey and sampling experts.
Types of Sampling • There are, generally, two types of sampling, i.e. – (i) probability sampling. – (ii) non- probability sampling.
Probability Sampling • A probability sample or a random sample is one in which the probability of selection of each unit in the population is known. • The probability of selection of each unit may or may not be independent. • If a sample is selected at random then it is known as a probability sample.
Probability sampling • Simple random sampling • Stratified sampling • Systematic sampling • Cluster sampling
Simple random Sampling • In simple random sampling each and every unit of the population has an equal probability of its being included in the sample. • It is applied to the population when it containing homogenous material. • Random sample can be drawn by a) Lottery system b) Random marking method
Systematic Random Sampling • This is the form of the random sampling, involving a system. The system is one of regularity. The sampling frame is chosen and a name or unit is chosen at random. Then from this chosen name or unit every nth item is selected throughout the list.
Example: Systematic sampling • N = 1200, and n = 60  sampling fraction = 1200/60 = 20 • List persons from 1 to 1200 • Randomly select a number between 1 and 20 (ex : 8)  1st person selected = the 8th on the list  2nd person = 8 + 20 = the 28th etc .....
Systematic Sampling Select some starting point and then select every K th element in the population
Stratified random Sampling • This is form of random sampling in which all peoples or items in the sampling frame are divided into groups or categories which are mutually exclusive (that is, a person or unit can be in one group only) these groups are called ‘strata’. • With in each of these group (stratum) a simple random sample is selected.
Cluster Sampling • In many situations, the sampling frame for elementary units of the population is not available, moreover it is not easy to prepare it. But the information is available for groups of elements so called clusters. • For instance, the list of houses may available but not the persons residing in them. In this situation houses are known as clusters and selection has to be made of houses in the sample. • Such a sampling procedure is known as cluster sampling.
Cluster Sampling divide the population into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters
Non-Probability Sampling • A sample selected by a non-random process is termed as a non- probability sample. • Judgment samples (purposive samples) and quota samples are examples of non-probability samples. • These types of selection procedures are useful when the population units are highly variable and the sample is small. • In these selection procedures, there is no way to check the precision and to obtain the precise estimates. • There is no way to determine the sampling, non-sampling errors.
Convenience sample • A convenience sample simply includes the individuals who happen to be most accessible to the researcher. • This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Example – You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.
Quota Sampling • A type of non-probability sample in which the researcher establishes quotas for recruitment of subjects into the sample so that the sample will similar to the population on selected characteristics.
Snow Ball Sampling • If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. • Example – You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible.
Purposive sampling • This type of sampling, also known as judgment sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research. • It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion.
Purposive sampling Example • You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.
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