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Sample,Sampling,Types of sampling,Probabilistic sampling(and its Type),Non Probabilistic sampling(and its Types)
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Sampling
This document defines and compares random and non-random sampling methods. Random sampling involves selecting units randomly so that all units have an equal probability of being chosen, while non-random sampling does not use random selection. Some key random sampling methods described are simple random sampling, stratified random sampling, and multistage sampling. Non-random sampling methods discussed include convenience sampling, judgment sampling, and snowball sampling. Examples are provided for some of the sampling methods.
Sampling Techniqes
This document provides an overview of sampling methods and techniques. It defines key terms like population, sampling, sample, and discusses the qualities of a good sample. The main types of sampling covered are probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Non-probability sampling methods like accidental sampling and purposive sampling are also outlined. Probability sampling uses random selection to give all units an equal chance of being selected, while non-probability sampling does not.
sampling
Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
Population and sample
This document discusses key concepts related to population, sample, and sampling methods. It defines a population as a complete set sharing a common characteristic, while a sample is a subset of a population. There are two main types of sampling: probability sampling, where every member has an equal chance of selection, and non-probability sampling, where chance is not involved. Some common sampling methods are simple random, stratified, cluster, systematic, convenience, snowball, and purposive sampling. The adequacy of a sample depends more on its representativeness than its size alone.
Cluster sampling
Cluster sampling refers to a method where the population is divided into groups called clusters. A simple random sample of these clusters is selected, and then all or a subset of elements within the selected clusters are included in the final sample. It is cheaper than simple random sampling but has a higher chance of sampling error. The key aspects are that the population is divided into clusters, a random sample of clusters is taken, and then data is collected from elements within those clusters.
Sampling and its variability
This document provides an overview of sampling and sampling variability. It defines key terms like population, sample, sampling, and sampling unit. It discusses the need for sampling due to limitations of complete enumeration. The main types of sampling designs covered are probability sampling methods like simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, and multistage sampling as well as non-probability methods. Factors affecting sample size calculation and sampling variability are also outlined.
Sampling
This document discusses sampling methods used in statistics. It describes sampling as selecting individual observations from a target population to make statistical inferences. There are two main types of sampling: probability sampling, where every unit has an equal chance of selection, and non-probability sampling, where some population elements have no chance of selection. Some common probability sampling methods described are simple random sampling, stratified random sampling, and cluster random sampling. Non-probability sampling methods discussed include convenience sampling and snowball sampling. The purposes, advantages, and disadvantages of sampling are also outlined.
Random Probability sampling by Sazzad Hossain
This presentation discusses different types of random or probability sampling methods. There are five main types discussed: simple random sampling, systematic random sampling, stratified random sampling, cluster random sampling, and multistage random sampling. For each method, examples are provided, the steps to implement the method are outlined, and the advantages and disadvantages are summarized. The presentation aims to define and explain these common probability sampling techniques.
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Sampling
This document defines and compares random and non-random sampling methods. Random sampling involves selecting units randomly so that all units have an equal probability of being chosen, while non-random sampling does not use random selection. Some key random sampling methods described are simple random sampling, stratified random sampling, and multistage sampling. Non-random sampling methods discussed include convenience sampling, judgment sampling, and snowball sampling. Examples are provided for some of the sampling methods.
Sampling Techniqes
This document provides an overview of sampling methods and techniques. It defines key terms like population, sampling, sample, and discusses the qualities of a good sample. The main types of sampling covered are probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Non-probability sampling methods like accidental sampling and purposive sampling are also outlined. Probability sampling uses random selection to give all units an equal chance of being selected, while non-probability sampling does not.
sampling
Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
Population and sample
This document discusses key concepts related to population, sample, and sampling methods. It defines a population as a complete set sharing a common characteristic, while a sample is a subset of a population. There are two main types of sampling: probability sampling, where every member has an equal chance of selection, and non-probability sampling, where chance is not involved. Some common sampling methods are simple random, stratified, cluster, systematic, convenience, snowball, and purposive sampling. The adequacy of a sample depends more on its representativeness than its size alone.
Cluster sampling
Cluster sampling refers to a method where the population is divided into groups called clusters. A simple random sample of these clusters is selected, and then all or a subset of elements within the selected clusters are included in the final sample. It is cheaper than simple random sampling but has a higher chance of sampling error. The key aspects are that the population is divided into clusters, a random sample of clusters is taken, and then data is collected from elements within those clusters.
Sampling and its variability
This document provides an overview of sampling and sampling variability. It defines key terms like population, sample, sampling, and sampling unit. It discusses the need for sampling due to limitations of complete enumeration. The main types of sampling designs covered are probability sampling methods like simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, and multistage sampling as well as non-probability methods. Factors affecting sample size calculation and sampling variability are also outlined.
Sampling
This document discusses sampling methods used in statistics. It describes sampling as selecting individual observations from a target population to make statistical inferences. There are two main types of sampling: probability sampling, where every unit has an equal chance of selection, and non-probability sampling, where some population elements have no chance of selection. Some common probability sampling methods described are simple random sampling, stratified random sampling, and cluster random sampling. Non-probability sampling methods discussed include convenience sampling and snowball sampling. The purposes, advantages, and disadvantages of sampling are also outlined.
Random Probability sampling by Sazzad Hossain
This presentation discusses different types of random or probability sampling methods. There are five main types discussed: simple random sampling, systematic random sampling, stratified random sampling, cluster random sampling, and multistage random sampling. For each method, examples are provided, the steps to implement the method are outlined, and the advantages and disadvantages are summarized. The presentation aims to define and explain these common probability sampling techniques.
Probability sampling
This document defines probability sampling and describes several probability sampling techniques. It begins by explaining that probability sampling selects subjects with a known probability, giving every unit in the population an equal chance of being selected. It then outlines several specific probability sampling techniques: random sampling, systematic random sampling, stratified random sampling, cluster random sampling, and multi-stage sampling. For each technique, it provides a brief definition and example. The document aims to explain how probability sampling allows researchers to generalize results to the larger population.
Complex random sampling designs
This document discusses various complex random sampling designs, including systematic sampling, stratified sampling, cluster sampling, multi-stage sampling, sampling with probability proportional to size, and sequential sampling. It provides details on how each design is implemented and their relative advantages and disadvantages. Complex random sampling designs combine elements of probability and non-probability sampling to select samples.
Jayesh sampling technique semi (1) (1)
This document discusses sampling techniques used in research. It defines key terms like population, sample, sampling frame, and stratified random sampling. Stratified random sampling involves dividing the population into homogeneous subgroups or strata first, then randomly selecting subjects proportionally from each strata. This ensures representation from different subgroups. Some advantages are it reduces bias, allows for comparisons between strata, and gives higher statistical precision than simple random sampling. Probability sampling methods like simple random and stratified random sampling are more reliable if a complete sampling frame is available.
Sampling and its Types
This document discusses different types of sampling methods used in statistics. It defines sampling as selecting observations from a population to describe and make inferences about the population. There are two main types of sampling: probability sampling, where units have a known chance of being selected, and non-probability sampling, where chance of selection is unknown. Probability sampling methods include simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multistage sampling. Non-probability sampling methods include convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling.
I. lecture sampling
This document discusses different sampling techniques that can be used in a thesis. It defines key terms like population, sample, parameter, and statistic. It explains that sampling is necessary when it is impossible or too costly to study the entire population. The document outlines probability sampling methods like simple random sampling, systematic sampling, stratified sampling, multistage sampling, and cluster sampling. It also discusses non-probability sampling techniques such as convenience sampling, purposive sampling, and quota sampling. Probability samples aim for randomness while non-probability samples rely on availability or purpose.
Probability Sampling
This document discusses probability sampling methods for surveys. It defines key terms like sampling unit and frame. Probability sampling ensures each unit has a known chance of selection and samples are drawn independently. The stages are identifying a sampling frame, determining sample size, selecting a technique like simple random or systematic sampling, and checking representativeness. Sample size is a tradeoff between accuracy and cost. Common techniques include simple random, systematic, stratified random, cluster, and multi-stage sampling.
Unit 2
The document discusses different sampling methods and terminology used in sampling theory. It defines key terms like population, sample, parameter, and statistics. It then describes four main sampling methods - simple random sampling, stratified random sampling, systematic sampling, and cluster sampling. For each method it provides examples, advantages, limitations and the procedures used to select samples.
sampling simple random sampling
This document discusses sampling from a population. A population includes all items related to an inquiry, while a sample is a representative subset of the population. Simple random sampling (SRS) is the process of drawing a sample from a population where each unit has an equal chance of being selected. There are two types of SRS: with replacement, where selected units can be selected again; and without replacement, where selected units are not returned before selecting the next unit. Random number tables and lottery methods are two common techniques used to select simple random samples from large populations.
Sampling
The document discusses various sampling techniques used in survey research. It defines population, sample, census, and sampling. Probability and non-probability sampling methods are described. Probability methods ensure each unit has a known chance of selection and include simple random sampling, systematic sampling, stratified sampling, cluster sampling, area sampling, and multistage sampling. Non-probability methods rely on availability or human judgment and include accidental, convenience, judgment, purposive, and quota sampling. Advantages and limitations of different techniques are also provided.
SAMPLE DESIGN, Probability sampling
This document discusses sampling and sample design. It defines sampling as selecting a subset of individuals from a larger population for statistical analysis. There are different sample design techniques, including probability and non-probability sampling methods. Probability methods like simple random sampling, systematic sampling, and stratified sampling allow researchers to precisely determine the relationship between the sample and population. Effective sample design considers the population, sample units, sampling frame, sampling technique, sample size, and execution of the sampling process. The document provides details on various sampling techniques and their advantages and disadvantages.
Sampling in public health dentistry
Sampling is a procedure used to make inferences about a larger population by studying a representative subset of it. There are two main types of sampling: probability sampling, where units have a known, non-zero chance of being selected; and non-probability sampling, where units are selected through convenience. Some common sampling methods include simple random sampling, stratified random sampling, cluster sampling, and multistage sampling. The goal is to select a sample that efficiently and cost-effectively represents the population while addressing requirements like representativeness, measurability, and feasibility.
Research methodology ppt
The document discusses various sampling methods used in statistical analysis including probability samples like simple random sampling, systematic random sampling, and stratified random sampling as well as non-probability samples. It covers the basic principles, processes, advantages and disadvantages of different sampling techniques. Probability sampling methods aim to provide a representative sample while non-probability relies on the researcher's selection.
Presentation on stratified sampling
Stratified sampling is a technique where the population is divided into subgroups or strata, and then a random sample is selected proportionally from each strata. This ensures adequate representation of specific subgroups of interest. There are two main types: proportional, where each strata is sampled at the same rate relative to its population size, and disproportionate, where strata can be sampled at different rates. Stratified sampling provides benefits like more accurate estimates for different population strata and improved overall representativeness of the sample. However, it also has disadvantages like difficulty defining strata and more complex analysis compared to simple random sampling.
Sampling techniquesmod5
Here are the steps to solve this problem using stratified random sampling:
1. Divide the population into strata based on the barangays.
2. Calculate the sample size for each stratum proportionately based on the total sample size (1000 residents) and population size of each stratum.
3. Randomly select the calculated sample size from each stratum.
Barangay Population Proportion of sample Sample size
Mapayapa 2,000 0.2 200
Malinis 1,000 0.1 100
Mahangin 1,500 0.15 150
Mabunga 2,500 0.25 250
Sampling and methods of doing sampling Assignment
This document discusses different methods of sampling, including simple random sampling, systematic sampling, and sampling with unequal probabilities. It provides examples to illustrate each method. The purpose of sampling is to make inferences about a whole population by examining a small sample. Sampling provides statistical information about a population more efficiently and quickly than a complete census. While sampling has advantages in cost, time, and scope compared to a full survey, it also has limitations such as potential for high errors with small samples or when very high accuracy is required.
Research 1: Sampling
1. The document defines sampling as selecting respondents from a population to answer questions and provide data for a research study.
2. It discusses the history of sampling beginning with a pioneering 1920s survey in the US, and the discovery of probability and non-probability sampling strategies.
3. Probability sampling aims for an unbiased sample representing the population, using techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling does not use random selection.
Kamrizzaman sir 4, 5 & 6 chapter, 5011
This document provides information on various survey methods and concepts. It discusses sampling methods like probability sampling (simple random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling) and non-probability sampling (convenience sampling, purposive sampling, quota sampling). It also covers survey design types, importance of sampling, acceptable response rates, defining populations, steps in survey research, and increasing response rates. Classification of survey research methods includes temporal classification into cross-sectional and longitudinal surveys.
The basic of educational research sampling
This document discusses different sampling methods used in educational research. It defines key terms like population, target population, and accessible population. The main sampling methods covered are random sampling methods like simple random sampling, stratified random sampling, cluster random sampling, and two-stage random sampling. The steps for each random method are provided. Non-random methods like systematic sampling, convenience sampling, and purposive sampling are also briefly discussed along with their advantages and disadvantages.
Stratified sampling
The document discusses stratified random sampling, which is a statistical sampling technique where the population is first divided into homogeneous subgroups or strata, then a random sample is drawn from each stratum. The key steps are to 1) identify and define the population, 2) determine sample size, 3) identify variables and subgroups for representation, 4) classify population members into subgroups, and 5) randomly select an appropriate number of individuals from each subgroup. Stratified random sampling can reduce bias and variability compared to simple random sampling. However, it requires knowing the names of all population members and may be difficult if some selected cannot be reached.
Multistage random sampling
Multistage sampling is a complex form of cluster sampling that uses multiple sampling methods together in stages. It first divides the population into primary sampling units and randomly selects some of these units. The selected units are then divided into secondary sampling units where another random sample is selected. This process can continue for third and fourth stages if needed. Multistage sampling is commonly used in large surveys to efficiently select samples across geographical areas in multiple stages.
Sample Designs and Sampling Procedures
sample designs and sampling procedures
,
sampling terminology
,
two major categories of sampling
,
simple random sampling
,
systematic sampling
,
cluster sampling
,
stratified sampling
,
why non probability sampling
,
errors
Assignment sampling techniques
This document is a 9-page assignment on sampling techniques submitted by Danish Alam to Dr. Fahd Amjad. It defines key sampling terms and describes two main types of sampling: probability sampling and non-probability sampling. Probability sampling techniques discussed include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling techniques examined include convenience sampling, judgment sampling, snowball sampling, ad hoc quota sampling, and case study sampling. Diagrams are provided showing the types of probability and non-probability sampling.
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Probability sampling
This document defines probability sampling and describes several probability sampling techniques. It begins by explaining that probability sampling selects subjects with a known probability, giving every unit in the population an equal chance of being selected. It then outlines several specific probability sampling techniques: random sampling, systematic random sampling, stratified random sampling, cluster random sampling, and multi-stage sampling. For each technique, it provides a brief definition and example. The document aims to explain how probability sampling allows researchers to generalize results to the larger population.
Complex random sampling designs
This document discusses various complex random sampling designs, including systematic sampling, stratified sampling, cluster sampling, multi-stage sampling, sampling with probability proportional to size, and sequential sampling. It provides details on how each design is implemented and their relative advantages and disadvantages. Complex random sampling designs combine elements of probability and non-probability sampling to select samples.
Jayesh sampling technique semi (1) (1)
This document discusses sampling techniques used in research. It defines key terms like population, sample, sampling frame, and stratified random sampling. Stratified random sampling involves dividing the population into homogeneous subgroups or strata first, then randomly selecting subjects proportionally from each strata. This ensures representation from different subgroups. Some advantages are it reduces bias, allows for comparisons between strata, and gives higher statistical precision than simple random sampling. Probability sampling methods like simple random and stratified random sampling are more reliable if a complete sampling frame is available.
Sampling and its Types
This document discusses different types of sampling methods used in statistics. It defines sampling as selecting observations from a population to describe and make inferences about the population. There are two main types of sampling: probability sampling, where units have a known chance of being selected, and non-probability sampling, where chance of selection is unknown. Probability sampling methods include simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multistage sampling. Non-probability sampling methods include convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling.
I. lecture sampling
This document discusses different sampling techniques that can be used in a thesis. It defines key terms like population, sample, parameter, and statistic. It explains that sampling is necessary when it is impossible or too costly to study the entire population. The document outlines probability sampling methods like simple random sampling, systematic sampling, stratified sampling, multistage sampling, and cluster sampling. It also discusses non-probability sampling techniques such as convenience sampling, purposive sampling, and quota sampling. Probability samples aim for randomness while non-probability samples rely on availability or purpose.
Probability Sampling
This document discusses probability sampling methods for surveys. It defines key terms like sampling unit and frame. Probability sampling ensures each unit has a known chance of selection and samples are drawn independently. The stages are identifying a sampling frame, determining sample size, selecting a technique like simple random or systematic sampling, and checking representativeness. Sample size is a tradeoff between accuracy and cost. Common techniques include simple random, systematic, stratified random, cluster, and multi-stage sampling.
Unit 2
The document discusses different sampling methods and terminology used in sampling theory. It defines key terms like population, sample, parameter, and statistics. It then describes four main sampling methods - simple random sampling, stratified random sampling, systematic sampling, and cluster sampling. For each method it provides examples, advantages, limitations and the procedures used to select samples.
sampling simple random sampling
This document discusses sampling from a population. A population includes all items related to an inquiry, while a sample is a representative subset of the population. Simple random sampling (SRS) is the process of drawing a sample from a population where each unit has an equal chance of being selected. There are two types of SRS: with replacement, where selected units can be selected again; and without replacement, where selected units are not returned before selecting the next unit. Random number tables and lottery methods are two common techniques used to select simple random samples from large populations.
Sampling
The document discusses various sampling techniques used in survey research. It defines population, sample, census, and sampling. Probability and non-probability sampling methods are described. Probability methods ensure each unit has a known chance of selection and include simple random sampling, systematic sampling, stratified sampling, cluster sampling, area sampling, and multistage sampling. Non-probability methods rely on availability or human judgment and include accidental, convenience, judgment, purposive, and quota sampling. Advantages and limitations of different techniques are also provided.
SAMPLE DESIGN, Probability sampling
This document discusses sampling and sample design. It defines sampling as selecting a subset of individuals from a larger population for statistical analysis. There are different sample design techniques, including probability and non-probability sampling methods. Probability methods like simple random sampling, systematic sampling, and stratified sampling allow researchers to precisely determine the relationship between the sample and population. Effective sample design considers the population, sample units, sampling frame, sampling technique, sample size, and execution of the sampling process. The document provides details on various sampling techniques and their advantages and disadvantages.
Sampling in public health dentistry
Sampling is a procedure used to make inferences about a larger population by studying a representative subset of it. There are two main types of sampling: probability sampling, where units have a known, non-zero chance of being selected; and non-probability sampling, where units are selected through convenience. Some common sampling methods include simple random sampling, stratified random sampling, cluster sampling, and multistage sampling. The goal is to select a sample that efficiently and cost-effectively represents the population while addressing requirements like representativeness, measurability, and feasibility.
Research methodology ppt
The document discusses various sampling methods used in statistical analysis including probability samples like simple random sampling, systematic random sampling, and stratified random sampling as well as non-probability samples. It covers the basic principles, processes, advantages and disadvantages of different sampling techniques. Probability sampling methods aim to provide a representative sample while non-probability relies on the researcher's selection.
Presentation on stratified sampling
Stratified sampling is a technique where the population is divided into subgroups or strata, and then a random sample is selected proportionally from each strata. This ensures adequate representation of specific subgroups of interest. There are two main types: proportional, where each strata is sampled at the same rate relative to its population size, and disproportionate, where strata can be sampled at different rates. Stratified sampling provides benefits like more accurate estimates for different population strata and improved overall representativeness of the sample. However, it also has disadvantages like difficulty defining strata and more complex analysis compared to simple random sampling.
Sampling techniquesmod5
Here are the steps to solve this problem using stratified random sampling:
1. Divide the population into strata based on the barangays.
2. Calculate the sample size for each stratum proportionately based on the total sample size (1000 residents) and population size of each stratum.
3. Randomly select the calculated sample size from each stratum.
Barangay Population Proportion of sample Sample size
Mapayapa 2,000 0.2 200
Malinis 1,000 0.1 100
Mahangin 1,500 0.15 150
Mabunga 2,500 0.25 250
Sampling and methods of doing sampling Assignment
This document discusses different methods of sampling, including simple random sampling, systematic sampling, and sampling with unequal probabilities. It provides examples to illustrate each method. The purpose of sampling is to make inferences about a whole population by examining a small sample. Sampling provides statistical information about a population more efficiently and quickly than a complete census. While sampling has advantages in cost, time, and scope compared to a full survey, it also has limitations such as potential for high errors with small samples or when very high accuracy is required.
Research 1: Sampling
1. The document defines sampling as selecting respondents from a population to answer questions and provide data for a research study.
2. It discusses the history of sampling beginning with a pioneering 1920s survey in the US, and the discovery of probability and non-probability sampling strategies.
3. Probability sampling aims for an unbiased sample representing the population, using techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling does not use random selection.
Kamrizzaman sir 4, 5 & 6 chapter, 5011
This document provides information on various survey methods and concepts. It discusses sampling methods like probability sampling (simple random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling) and non-probability sampling (convenience sampling, purposive sampling, quota sampling). It also covers survey design types, importance of sampling, acceptable response rates, defining populations, steps in survey research, and increasing response rates. Classification of survey research methods includes temporal classification into cross-sectional and longitudinal surveys.
The basic of educational research sampling
This document discusses different sampling methods used in educational research. It defines key terms like population, target population, and accessible population. The main sampling methods covered are random sampling methods like simple random sampling, stratified random sampling, cluster random sampling, and two-stage random sampling. The steps for each random method are provided. Non-random methods like systematic sampling, convenience sampling, and purposive sampling are also briefly discussed along with their advantages and disadvantages.
Stratified sampling
The document discusses stratified random sampling, which is a statistical sampling technique where the population is first divided into homogeneous subgroups or strata, then a random sample is drawn from each stratum. The key steps are to 1) identify and define the population, 2) determine sample size, 3) identify variables and subgroups for representation, 4) classify population members into subgroups, and 5) randomly select an appropriate number of individuals from each subgroup. Stratified random sampling can reduce bias and variability compared to simple random sampling. However, it requires knowing the names of all population members and may be difficult if some selected cannot be reached.
Multistage random sampling
Multistage sampling is a complex form of cluster sampling that uses multiple sampling methods together in stages. It first divides the population into primary sampling units and randomly selects some of these units. The selected units are then divided into secondary sampling units where another random sample is selected. This process can continue for third and fourth stages if needed. Multistage sampling is commonly used in large surveys to efficiently select samples across geographical areas in multiple stages.
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This document discusses various sampling techniques used in epidemiological studies. It defines key terms like population, sample, and parameters. It covers both probability and non-probability sampling methods. Probability methods discussed include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Non-probability methods include convenience sampling, purposive sampling, snowball sampling, and quota sampling. The document provides details on how each sampling technique is implemented and their relative advantages and disadvantages.
Sampling techniques
Sampling is a process used in statistical analysis where a subset of a population, or sample, is used to estimate characteristics of the whole population. There are two main types of sampling: probability sampling, where every member of the population has a known chance of being selected; and non-probability sampling, where not every member has an equal chance of selection. Some common sampling techniques include simple random sampling, systematic random sampling, stratified random sampling, multi-stage random sampling, convenience sampling, quota sampling, and snowball sampling. The goal of sampling is to select a group that accurately represents the larger population to allow researchers to make inferences about the population.
Sampling techniques in Research
Sampling is a process used in statistical analysis where a subset of a population, called a sample, is used to estimate characteristics of the whole population. There are two main types of sampling: probability sampling, where every member of the population has a known chance of being selected; and non-probability sampling, where not every member has an equal chance of selection. Some common sampling techniques include simple random sampling, systematic random sampling, stratified random sampling, multi-stage random sampling, convenience sampling, quota sampling, and snowball sampling. The goal of sampling is to select a group that accurately represents the larger population to allow researchers to make generalizations about characteristics, attributes, and behaviors of the whole population.
Sampling
This document discusses sampling methods for research. It defines key terms like population, sample, and sampling. It covers the main types of sampling:
1. Probability sampling methods like simple random sampling, stratified sampling, and systematic sampling which give all units an equal chance of selection.
2. Non-probability sampling methods like judgement, quota, and convenience sampling which do not give all units an equal chance and can be biased.
3. Factors to consider for good sampling include accuracy, precision, defining the target population, determining the sampling method and size. The document provides details on each sampling technique and their advantages and disadvantages.
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Sampling ,types ,advantages and disadvantages of the sampling
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Sample
- 1. Assignment Subject: Inferential Statistics Topic: Sampling Prepared By: Khawaja Danish Farooq Id: BB-26119 Submitted To: Ibadullah Choudary
- 2. Sample A simple randomsample isasubsetof a statistical populationinwhicheachmemberof the subsethas an equal probabilityof beingchosen. Sampling Samplingisa processusedinstatistical analysisinwhichapredeterminednumberof observationsare takenfroma largerpopulation.The methodologyusedtosample fromalargerpopulation dependson the type of analysisbeingperformed,butmayinclude simple randomsamplingorsystematicsampling. Samplingisthe processof collectingthe dataare knownas sampling. There are twotypesof sampling. 1. ProbabilisticSampling 2. Non-ProbabilisticSampling ProbabilisticSampling Probability sampling is a sampling technique, in which the subjects of the population get an equal opportunity to be selected as a representative sample. It isuse whenthe numberof populationisknown;there are five typesof probabilisticsampling. I. Simple Random Sampling II. StratifiedSampling III. Systematic Random Sampling IV. Cluster Sampling V. Multistage Sampling Simple Random Sampling A simple randomsample isasubsetof a statistical populationinwhicheachmemberof the subsethas an equal probabilityof beingchosen.Anexampleof asimple randomsample wouldbe the namesof 25 employeesbeingchosenoutof a hat froma companyof 250 employees.
- 3. Stratified Sampling Stratifiedsamplingreferstoa type of sampling method.Withstratifiedsampling,the researcherdivides the populationintoseparate groups,calledstrata.Then,aprobabilitysample(oftenasimple random sample ) isdrawn fromeach group.Stratifiedsamplinghasseveral advantagesoversimple random sampling. . Systematic Random Sampling Systematicsamplingisatype of probabilitysamplingmethodinwhichsample membersfromalarger populationare selectedaccordingtoa randomstartingpointand a fixedperiodicinterval.Thisinterval, calledthe strata,is calculatedbydividingthe populationsize bythe desiredsamplesize.. N/n= No. of unit in population No. of unit in sample
- 4. Cluster Sampling It is related with geographical boundaries. Suppose if we are collecting data in different areas ao the target population is known as cluster. Withclustersampling,the researcherdividesthe populationintoseparate groups,calledclusters.Then,asimple randomsampleof clustersisselected fromthe population. Multistage Sampling Multistage samplingcanbe a complex formof clustersamplingbecauseitisa type of samplingwhich involvesdividingthe populationintogroups(orclusters).Then,one ormore clustersare chosenat randomand everyone withinthe chosenclusterissampled. Non-ProbabilisticSampling Non probabilistic sampling is a method of sampling wherein, it is not known that which individual from the population will be selected as a sample. There are four types of non probabilistic sampling. I. Convenience Sampling II. Quota Sampling III. Judgment or Purposive Sampling IV. Snowball Sampling
- 5. Convenience Sampling A convenience sample is one of the main types of non-probability sampling methods. A convenience sample is made up of people who are easy to reach. Quota Sampling A samplingmethodof gatheringrepresentative datafromagroup. Asopposedtorandomsampling, quotasamplingrequiresthatrepresentative individualsare chosenoutof a specificsubgroup.For example,aresearchermightaskfora sample of 100 females,or100 individualsbetweenthe agesof 20- 30.
- 6. Judgment or PurposiveSampling Purposive sampling.Purposive sampling(alsoknownasjudgment,selectiveorsubjective sampling)isa samplingtechnique inwhichresearcherreliesonhisorher ownjudgmentwhenchoosingmembersof populationtoparticipate inthe study. Snowball Sampling In sociologyandstatisticsresearch,snowballsampling(orchainsampling,chain-referralsampling, referral sampling)isanonprobability samplingtechniquewhere existingstudysubjectsrecruitfuture subjectsfromamongtheiracquaintances.