This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key points are that sampling allows researchers to make inferences about a population while using fewer subjects, and the type of sampling method impacts the accuracy and potential for bias in the results.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting samples and how to select the most appropriate method for their research needs.
This document provides an overview of sampling methods for research. It begins by defining research and the key components of empirical research. It then defines sampling as selecting a subset of a population to make inferences about that population. The document discusses probability and non-probability sampling methods. It provides details on specific probability methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also discusses non-probability sampling and compares different sampling techniques. The document aims to help readers understand sampling frames, sample sizes, and how to select appropriate sampling methods for research studies.
This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key takeaway is that sampling allows researchers to make inferences about a population while reducing time and costs compared to a census. Probability methods are preferred when possible due to their ability to estimate sampling errors.
This document provides an overview of different sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods, like convenience sampling. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The document aims to help readers understand why sampling is necessary, different sampling techniques, and how to select the appropriate method for their research needs.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to drawing sample populations and how to select the most appropriate method for their research needs.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting representative samples and how to select the appropriate sampling method for their research needs.
This document discusses various sampling methods used in research. It begins by defining a sample and explaining why sampling is used instead of surveying entire populations. The document then distinguishes between probability sampling methods, which assign a known probability of selection to each unit, and non-probability sampling methods, which do not. Specific probability methods covered include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability methods discussed are convenience sampling and purposive sampling. Advantages and disadvantages of each approach are provided.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, and non-probability sampling methods. For each method, it provides details on how the sampling is conducted and advantages and disadvantages. Cluster sampling is also explained as a multi-stage process where clusters rather than individuals are selected.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting samples and how to select the most appropriate method for their research needs.
This document provides an overview of sampling methods for research. It begins by defining research and the key components of empirical research. It then defines sampling as selecting a subset of a population to make inferences about that population. The document discusses probability and non-probability sampling methods. It provides details on specific probability methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also discusses non-probability sampling and compares different sampling techniques. The document aims to help readers understand sampling frames, sample sizes, and how to select appropriate sampling methods for research studies.
This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key takeaway is that sampling allows researchers to make inferences about a population while reducing time and costs compared to a census. Probability methods are preferred when possible due to their ability to estimate sampling errors.
This document provides an overview of different sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods, like convenience sampling. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The document aims to help readers understand why sampling is necessary, different sampling techniques, and how to select the appropriate method for their research needs.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to drawing sample populations and how to select the most appropriate method for their research needs.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting representative samples and how to select the appropriate sampling method for their research needs.
This document discusses various sampling methods used in research. It begins by defining a sample and explaining why sampling is used instead of surveying entire populations. The document then distinguishes between probability sampling methods, which assign a known probability of selection to each unit, and non-probability sampling methods, which do not. Specific probability methods covered include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability methods discussed are convenience sampling and purposive sampling. Advantages and disadvantages of each approach are provided.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, and non-probability sampling methods. For each method, it provides details on how the sampling is conducted and advantages and disadvantages. Cluster sampling is also explained as a multi-stage process where clusters rather than individuals are selected.
This document discusses various sampling methods used in market research, including probability and non-probability sampling. Probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and quota sampling allow researchers to calculate sampling error and make statistical inferences about the overall population. Non-probability methods like convenience sampling and snowball sampling provide easily accessible samples but results cannot be generalized to the population due to potential biases. The best sampling method depends on the research goals, population characteristics, and available resources.
The document discusses various methods of sampling used in business statistics, including probability and non-probability sampling. Probability sampling methods ensure that every element in the population has a known chance of being selected in the sample. These include random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling, and probability proportional to size sampling. Non-probability sampling methods do not assign a measurable probability to being selected, and include quota sampling, accidental sampling, purposive sampling, voluntary sampling, and snowball sampling. The document also outlines advantages and disadvantages of different sampling techniques.
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.
Systematic sampling in probability sampling Sachin H
This is a systematic sample in probability sampling which is consider to be one of the technics of sampling . It is most useful in certain circumstances in Random 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.
1. This document discusses different probability sampling techniques: simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
2. It provides examples to illustrate how each technique is implemented in practice. Advantages and disadvantages of each technique are also outlined.
3. Key steps are described for each technique, such as numbering units, calculating sampling intervals, determining sample sizes for each stratum, and randomly selecting clusters.
This document provides an overview of sampling techniques. It defines key sampling terms like population, sample, sampling frame, and discusses the need for sampling due to constraints of time and money for a full census. The document outlines different sampling methods like simple random sampling, stratified sampling, cluster sampling and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling and snowball sampling. The document emphasizes the importance of representativeness, adequacy and independence for a good sample. It concludes by noting sources of error in sampling like sampling errors and non-sampling errors.
Systematic random sampling is a type of probability sampling where units from a larger population are selected according to a random starting point and a fixed periodic interval. This method is simple, convenient, and economical compared to simple random sampling. It involves randomly selecting a starting point between 1 and the sampling interval, then selecting every kth unit thereafter. While it provides unbiased estimates, it could lead to over or underrepresentation if the population has a hidden periodic pattern.
Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population
This document discusses various sampling methods used in research. It defines key terms like population and sample. It describes the need for sampling due to limited resources. Different probability sampling methods are covered like simple random sampling, systematic sampling with random start, and stratified sampling. Simple random sampling selects units with equal probability from a sampling frame. Systematic sampling selects units at regular intervals but can result in bias. Stratified sampling divides the population into homogeneous subgroups before sampling. Finally, multistage sampling is used for large, scattered populations and involves multiple stages of sampling.
The document discusses six types of sampling methods: simple random sampling, systematic sampling, stratified random sampling, cluster sampling, convenience sampling, and errors that can occur in sampling. Simple random sampling involves randomly selecting subsets from a population so that every subset has an equal chance of being selected. Stratified random sampling divides the population into groups and then randomly selects subgroups from each group. Cluster sampling divides the population into clusters and randomly selects clusters to survey all members of that cluster. The document also outlines potential errors in sampling, including non-response errors, coverage errors, and observation errors due to issues with interviewers, respondents, or question wording.
This document discusses key concepts and terminology related to probability sampling. It defines sampling as selecting observations from a population to make inferences about the entire population without bias. There are two main types of sampling: probability and non-probability. Probability sampling involves giving every member of the population an equal chance of being selected, which allows for more accurate inferences. Key concepts discussed include population, sample, parameter, estimate, sampling error, and standard error. The document also covers different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It explains how and when each method should be used.
This document discusses key concepts related to sampling in research. It defines important terms like population, element, sample, and sampling unit. It explains the difference between sampling and a census. Some advantages of sampling over a census are that it saves time and costs, and sometimes produces more reliable results. There are two main types of errors in sampling - sampling error, which occurs when the sample is not representative, and non-sampling error from other issues. The document outlines different probability and non-probability sampling methods like simple random sampling, stratified sampling, cluster sampling, systematic sampling, convenience sampling, and quota sampling. It provides formulas for determining sample size based on factors like population variability, desired confidence level, and acceptable margin of error.
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 Methods in Qualitative and Quantitative ResearchSam Ladner
This document discusses different types of sampling methods used in qualitative and quantitative research. It outlines the different assumptions researchers make regarding sampling in qualitative versus quantitative studies. A variety of sampling techniques are described for different research contexts such as ethnographic fieldwork, interviews, and content analysis.
Sampling involves selecting a subset of a population to make inferences about the whole population. Common sampling techniques include probability sampling, where every unit has a known chance of selection, and non-probability sampling, where the probability of selection cannot be determined. Some specific sampling methods are systematic sampling, stratified sampling, cluster sampling, simple random sampling, convenience sampling, judgement sampling, snowball sampling, and quota sampling. Sampling error, the difference between the sample and the true population, can be reduced by using a large, randomly selected sample.
This presentation discusses various sampling methods that can be used in research in social and natural sciences. It introduces key concepts in sampling like population, sampling frame, sample size determination. It covers probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling and non-probability sampling methods like convenience sampling, purposive sampling and quota sampling. Examples of how these methods are applied in biological and sociological data collection are provided.
Sample and sampling design (Research method)niazali15CRP92
This document discusses different sampling methods and designs used in research. It defines a census as collecting data from the entire population, while a sample collects data from a subset of the population. Probability sampling aims for each unit to have an equal chance of selection, like simple random sampling. Non-probability sampling does not allow for determining selection probabilities. The document also describes different sampling designs such as stratified sampling, cluster sampling, and multistage sampling, as well as non-probability sampling techniques. The goal of sampling is to obtain a representative subset that can be generalized to the overall population.
There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves methods where the probability of selection of each individual is known, such as simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Simple random sampling involves selecting a sample that gives each individual an equal chance of being selected by identifying the population, determining sample size, listing all population members, assigning them numbers, selecting numbers at random from a table, and including individuals in the sample if their number is selected. The advantages are it is easy to conduct and requires minimum population knowledge, while disadvantages include needing all population member names and potential over or under representation.
This document discusses different sampling methods used in research. It defines key terms like population, sample, and sampling frame. It explains the difference between probability and non-probability sampling. Some common probability sampling methods described include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability sampling methods mentioned are convenience sampling and purposive sampling. The document provides details on how each sampling method is implemented and their relative advantages and disadvantages.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It also explains different types of sampling techniques including probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability sampling methods like convenience sampling are also discussed. The document compares advantages and disadvantages of different sampling approaches. It provides examples of how to implement certain sampling designs in practice.
This document discusses various sampling methods used in market research, including probability and non-probability sampling. Probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and quota sampling allow researchers to calculate sampling error and make statistical inferences about the overall population. Non-probability methods like convenience sampling and snowball sampling provide easily accessible samples but results cannot be generalized to the population due to potential biases. The best sampling method depends on the research goals, population characteristics, and available resources.
The document discusses various methods of sampling used in business statistics, including probability and non-probability sampling. Probability sampling methods ensure that every element in the population has a known chance of being selected in the sample. These include random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling, and probability proportional to size sampling. Non-probability sampling methods do not assign a measurable probability to being selected, and include quota sampling, accidental sampling, purposive sampling, voluntary sampling, and snowball sampling. The document also outlines advantages and disadvantages of different sampling techniques.
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.
Systematic sampling in probability sampling Sachin H
This is a systematic sample in probability sampling which is consider to be one of the technics of sampling . It is most useful in certain circumstances in Random 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.
1. This document discusses different probability sampling techniques: simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
2. It provides examples to illustrate how each technique is implemented in practice. Advantages and disadvantages of each technique are also outlined.
3. Key steps are described for each technique, such as numbering units, calculating sampling intervals, determining sample sizes for each stratum, and randomly selecting clusters.
This document provides an overview of sampling techniques. It defines key sampling terms like population, sample, sampling frame, and discusses the need for sampling due to constraints of time and money for a full census. The document outlines different sampling methods like simple random sampling, stratified sampling, cluster sampling and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling and snowball sampling. The document emphasizes the importance of representativeness, adequacy and independence for a good sample. It concludes by noting sources of error in sampling like sampling errors and non-sampling errors.
Systematic random sampling is a type of probability sampling where units from a larger population are selected according to a random starting point and a fixed periodic interval. This method is simple, convenient, and economical compared to simple random sampling. It involves randomly selecting a starting point between 1 and the sampling interval, then selecting every kth unit thereafter. While it provides unbiased estimates, it could lead to over or underrepresentation if the population has a hidden periodic pattern.
Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population
This document discusses various sampling methods used in research. It defines key terms like population and sample. It describes the need for sampling due to limited resources. Different probability sampling methods are covered like simple random sampling, systematic sampling with random start, and stratified sampling. Simple random sampling selects units with equal probability from a sampling frame. Systematic sampling selects units at regular intervals but can result in bias. Stratified sampling divides the population into homogeneous subgroups before sampling. Finally, multistage sampling is used for large, scattered populations and involves multiple stages of sampling.
The document discusses six types of sampling methods: simple random sampling, systematic sampling, stratified random sampling, cluster sampling, convenience sampling, and errors that can occur in sampling. Simple random sampling involves randomly selecting subsets from a population so that every subset has an equal chance of being selected. Stratified random sampling divides the population into groups and then randomly selects subgroups from each group. Cluster sampling divides the population into clusters and randomly selects clusters to survey all members of that cluster. The document also outlines potential errors in sampling, including non-response errors, coverage errors, and observation errors due to issues with interviewers, respondents, or question wording.
This document discusses key concepts and terminology related to probability sampling. It defines sampling as selecting observations from a population to make inferences about the entire population without bias. There are two main types of sampling: probability and non-probability. Probability sampling involves giving every member of the population an equal chance of being selected, which allows for more accurate inferences. Key concepts discussed include population, sample, parameter, estimate, sampling error, and standard error. The document also covers different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It explains how and when each method should be used.
This document discusses key concepts related to sampling in research. It defines important terms like population, element, sample, and sampling unit. It explains the difference between sampling and a census. Some advantages of sampling over a census are that it saves time and costs, and sometimes produces more reliable results. There are two main types of errors in sampling - sampling error, which occurs when the sample is not representative, and non-sampling error from other issues. The document outlines different probability and non-probability sampling methods like simple random sampling, stratified sampling, cluster sampling, systematic sampling, convenience sampling, and quota sampling. It provides formulas for determining sample size based on factors like population variability, desired confidence level, and acceptable margin of error.
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 Methods in Qualitative and Quantitative ResearchSam Ladner
This document discusses different types of sampling methods used in qualitative and quantitative research. It outlines the different assumptions researchers make regarding sampling in qualitative versus quantitative studies. A variety of sampling techniques are described for different research contexts such as ethnographic fieldwork, interviews, and content analysis.
Sampling involves selecting a subset of a population to make inferences about the whole population. Common sampling techniques include probability sampling, where every unit has a known chance of selection, and non-probability sampling, where the probability of selection cannot be determined. Some specific sampling methods are systematic sampling, stratified sampling, cluster sampling, simple random sampling, convenience sampling, judgement sampling, snowball sampling, and quota sampling. Sampling error, the difference between the sample and the true population, can be reduced by using a large, randomly selected sample.
This presentation discusses various sampling methods that can be used in research in social and natural sciences. It introduces key concepts in sampling like population, sampling frame, sample size determination. It covers probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling and non-probability sampling methods like convenience sampling, purposive sampling and quota sampling. Examples of how these methods are applied in biological and sociological data collection are provided.
Sample and sampling design (Research method)niazali15CRP92
This document discusses different sampling methods and designs used in research. It defines a census as collecting data from the entire population, while a sample collects data from a subset of the population. Probability sampling aims for each unit to have an equal chance of selection, like simple random sampling. Non-probability sampling does not allow for determining selection probabilities. The document also describes different sampling designs such as stratified sampling, cluster sampling, and multistage sampling, as well as non-probability sampling techniques. The goal of sampling is to obtain a representative subset that can be generalized to the overall population.
There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves methods where the probability of selection of each individual is known, such as simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Simple random sampling involves selecting a sample that gives each individual an equal chance of being selected by identifying the population, determining sample size, listing all population members, assigning them numbers, selecting numbers at random from a table, and including individuals in the sample if their number is selected. The advantages are it is easy to conduct and requires minimum population knowledge, while disadvantages include needing all population member names and potential over or under representation.
This document discusses different sampling methods used in research. It defines key terms like population, sample, and sampling frame. It explains the difference between probability and non-probability sampling. Some common probability sampling methods described include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability sampling methods mentioned are convenience sampling and purposive sampling. The document provides details on how each sampling method is implemented and their relative advantages and disadvantages.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It also explains different types of sampling techniques including probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability sampling methods like convenience sampling are also discussed. The document compares advantages and disadvantages of different sampling approaches. It provides examples of how to implement certain sampling designs in practice.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, and non-probability sampling methods. For each method, it provides details on how the sampling is conducted and notes advantages and disadvantages. The goal is to help readers understand different approaches to collecting representative samples from a population in a way that allows results to be generalized.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, which assign a known probability of selection to units, and non-probability sampling methods, which do not. The document provides details on how to implement different probability sampling techniques and discusses their relative advantages and disadvantages. It emphasizes that the goal of sampling is to select a subset of a population that is representative of the whole.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, and non-probability sampling methods. For each method, it provides details on how the sampling is conducted and advantages and disadvantages. Cluster sampling is also explained as a multi-stage process where clusters rather than individuals are selected.
This document discusses different sampling methods used in research. It begins by defining sampling as selecting a subset of units from a larger population. The document then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes how to implement the technique and highlights advantages and disadvantages. The key goal is to help readers understand how to appropriately select samples to gather data about a target population.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then covers different types of sampling, distinguishing between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods like convenience sampling. For each method, it provides details on how to implement it and notes advantages and disadvantages. The document aims to help readers understand different sampling techniques and how to select the appropriate method for their research needs.
This document discusses different sampling methods used in research. It begins by defining sampling as selecting a subset of a population to make inferences about the whole population. The document then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. Key advantages and disadvantages of each method are described.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then covers different types of sampling, distinguishing between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods like convenience sampling. For each method, it provides details on how to implement it and notes advantages and disadvantages. The document aims to help readers understand different sampling techniques and how to select the appropriate method for their research needs.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then covers different types of sampling, distinguishing between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods like convenience sampling. For each method, it provides details on how to implement it and notes advantages and disadvantages. The document aims to help readers understand sampling techniques and how to select the appropriate method for their research needs.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then explains the difference between probability sampling methods, like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods, like convenience sampling. For each method, it provides details on how to implement the method and discusses their relative advantages and disadvantages. The goal is to help readers understand different approaches to drawing sample populations from a target population in a way that limits bias.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then covers different types of sampling, distinguishing between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods like convenience sampling. For each method, it provides details on how to implement it and notes advantages and disadvantages. The document aims to help readers understand different sampling techniques and how to select the appropriate method for their research needs.
This document discusses different sampling methods used in research. It begins by defining sampling as selecting a subset of a population to make inferences about the whole population. The document then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key goal is to help readers understand how to select representative samples for research studies.
Methods of Sampling used in dentistry. pptswarnimakhichi
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then describes different types of sampling, including probability sampling methods like simple random sampling, systematic sampling, and stratified sampling, as well as non-probability sampling methods. For each method, it provides details on the process and discusses their relative advantages and disadvantages. The document aims to help readers understand sampling and how to select the appropriate technique for their research.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then covers different types of sampling, distinguishing between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods like convenience sampling. For each method, it provides brief explanations of the process and notes advantages and disadvantages. The document aims to help readers understand different sampling techniques and how to select the appropriate method for their research needs.
Phyton class by Pavan - Study notes inclPavan Babu .G
This document discusses different sampling methods used in research. It begins by defining sampling as selecting a subset of a population to make inferences about the whole population. The document then covers various probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also discusses non-probability sampling and compares the advantages and disadvantages of different sampling techniques. Key factors that influence sample representativeness like sampling procedure, sample size, and response rate are also highlighted.
The document discusses various sampling methods used in research. It describes sampling as selecting a subset of a population to make inferences about the whole population. Probability sampling methods like simple random sampling, systematic sampling, and stratified sampling aim to give all population members an equal chance of selection. Non-probability methods do not allow for estimating sampling errors. The key factors discussed include defining the target population, developing a sampling frame, determining sample size and method, and ensuring a representative sample.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
2. LEARNING OBJECTIVES
2
Learn the reasons for sampling
Develop an understanding about different
sampling methods
Distinguish between probability & non probability
sampling
Discuss the relative advantages & disadvantages
of each sampling methods
3. What is research?
3
• “Scientific research is systematic, controlled,
empirical, and critical investigation of natural
phenomena guided by theory and hypotheses
about the presumed relations among such
phenomena.”
– Kerlinger, 1986
• Research is an organized and systematic way of
finding answers to questions
4. Important Components of Empirical Research
4
Problem statement, research questions, purposes,
benefits
Theory, assumptions, background literature
Variables and hypotheses
Operational definitions and measurement
Research design and methodology
Instrumentation, sampling
Data analysis
Conclusions, interpretations, recommendations
5. SAMPLING
5
A sample is “a smaller (but hopefully
representative) collection of units from a
population used to determine truths about that
population” (Field, 2005)
Why sample?
Resources (time, money) and workload
Gives results with known accuracy that can be
calculated mathematically
The sampling frame is the list from which the
potential respondents are drawn
Registrar’s office
Class rosters
Must assess sampling frame errors
6. SAMPLING……
6
What is your population of interest?
To whom do you want to generalize your
results?
All doctors
School children
Indians
Women aged 15-45 years
Other
Can you sample the entire population?
7. SAMPLING…….
7
3 factors that influence sample representative-
ness
Sampling procedure
Sample size
Participation (response)
When might you sample the entire population?
When your population is very small
When you have extensive resources
When you don’t expect a very high response
10. Types of Samples
10
Probability (Random) Samples
Simple random sample
Systematic random sample
Stratified random sample
Multistage sample
Multiphase sample
Cluster sample
Non-Probability Samples
Convenience sample
Purposive sample
Quota
11. Process
11
The sampling process comprises several stages:
Defining the population of concern
Specifying a sampling frame, a set of items or
events possible to measure
Specifying a sampling method for selecting
items or events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collecting
Reviewing the sampling process
12. Population definition
12
A population can be defined as including all
people or items with the characteristic one
wishes to understand.
Because there is very rarely enough time or
money to gather information from everyone
or everything in a population, the goal
becomes finding a representative sample (or
subset) of that population.
13. Population definition…….
13
Note also that the population from which the sample
is drawn may not be the same as the population about
which we actually want information. Often there is
large but not complete overlap between these two
groups due to frame issues etc .
Sometimes they may be entirely separate - for
instance, we might study rats in order to get a
better understanding of human health, or we might
study records from people born in 2008 in order to
make predictions about people born in 2009.
14. SAMPLING FRAME
14
In the most straightforward case, such as the
sentencing of a batch of material from production
(acceptance sampling by lots), it is possible to
identify and measure every single item in the
population and to include any one of them in our
sample. However, in the more general case this is not
possible. There is no way to identify all rats in the
set of all rats. Where voting is not compulsory, there
is no way to identify which people will actually vote at
a forthcoming election (in advance of the election)
As a remedy, we seek a sampling frame which has the
property that we can identify every single element
and include any in our sample .
The sampling frame must be representative of the
population
15. PROBABILITY SAMPLING
15
A probability sampling scheme is one in which every
unit in the population has a chance (greater than zero)
of being selected in the sample, and this probability
can be accurately determined.
. When every element in the population does have the
same probability of selection, this is known as an
'equal probability of selection' (EPS) design. Such
designs are also referred to as 'self-weighting'
because all sampled units are given the same weight.
16. PROBABILITY SAMPLING…….
16
Probability sampling includes:
Simple Random Sampling,
Systematic Sampling,
Stratified Random Sampling,
Cluster Sampling
Multistage Sampling.
Multiphase sampling
17. NON PROBABILITY SAMPLING
17
Any sampling method where some elements of population
have no chance of selection (these are sometimes
referred to as 'out of coverage'/'undercovered'), or
where the probability of selection can't be accurately
determined. It involves the selection of elements based
on assumptions regarding the population of interest, which
forms the criteria for selection. Hence, because the
selection of elements is nonrandom, nonprobability
sampling not allows the estimation of sampling errors..
Example: We visit every household in a given street, and
interview the first person to answer the door. In any
household with more than one occupant, this is a
nonprobability sample, because some people are more
likely to answer the door (e.g. an unemployed person who
spends most of their time at home is more likely to
answer than an employed housemate who might be at work
when the interviewer calls) and it's not practical to
calculate these probabilities.
18. NONPROBABILITY SAMPLING…….
18
• Nonprobability Sampling includes:
Accidental Sampling, Quota Sampling and
Purposive Sampling. In addition, nonresponse
effects may turn any probability design into a
nonprobability design if the characteristics of
nonresponse are not well understood, since
nonresponse effectively modifies each
element's probability of being sampled.
19. SIMPLE RANDOM SAMPLING
19
• Applicable when population is small, homogeneous &
readily available
• All subsets of the frame are given an equal
probability. Each element of the frame thus has an
equal probability of selection.
• It provides for greatest number of possible samples.
This is done by assigning a number to each unit in the
sampling frame.
• A table of random number or lottery system is used
to determine which units are to be selected.
20. SIMPLE RANDOM SAMPLING……..
20
Estimates are easy to calculate.
Simple random sampling is always an EPS design, but not all
EPS designs are simple random sampling.
Disadvantages
If sampling frame large, this method impracticable.
Minority subgroups of interest in population may not be
present in sample in sufficient numbers for study.
21. REPLACEMENT OF SELECTED UNITS
21
Sampling schemes may be without replacement
('WOR' - no element can be selected more than once
in the same sample) or with replacement ('WR' - an
element may appear multiple times in the one
sample).
For example, if we catch fish, measure them, and
immediately return them to the water before
continuing with the sample, this is a WR design,
because we might end up catching and measuring the
same fish more than once. However, if we do not
return the fish to the water (e.g. if we eat the fish),
this becomes a WOR design.
22. SYSTEMATIC SAMPLING
22
Systematic sampling relies on arranging the target
population according to some ordering scheme and then
selecting elements at regular intervals through that
ordered list.
Systematic sampling involves a random start and then
proceeds with the selection of every kth element from
then onwards. In this case, k=(population size/sample
size).
It is important that the starting point is not
automatically the first in the list, but is instead
randomly chosen from within the first to the kth
element in the list.
A simple example would be to select every 10th name
from the telephone directory (an 'every 10th' sample,
also referred to as 'sampling with a skip of 10').
23. SYSTEMATIC SAMPLING……
23
As described above, systematic sampling is an EPS method, because all
elements have the same probability of selection (in the example
given, one in ten). It is not 'simple random sampling' because
different subsets of the same size have different selection
probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten
probability of selection, but the set {4,13,24,34,...} has zero
probability of selection.
24. SYSTEMATIC SAMPLING……
24
ADVANTAGES:
Sample easy to select
Suitable sampling frame can be identified easily
Sample evenly spread over entire reference population
DISADVANTAGES:
Sample may be biased if hidden periodicity in population
coincides with that of selection.
Difficult to assess precision of estimate from one survey.
25. STRATIFIED SAMPLING
25
Where population embraces a number of distinct
categories, the frame can be organized into separate
"strata." Each stratum is then sampled as an
independent sub-population, out of which individual
elements can be randomly selected.
Every unit in a stratum has same chance of being
selected.
Using same sampling fraction for all strata ensures
proportionate representation in the sample.
Adequate representation of minority subgroups of
interest can be ensured by stratification & varying
sampling fraction between strata as required.
26. STRATIFIED SAMPLING……
26
Finally, since each stratum is treated as an
independent population, different sampling
approaches can be applied to different strata.
Drawbacks to using stratified sampling.
First, sampling frame of entire population has to
be prepared separately for each stratum
Second, when examining multiple criteria,
stratifying variables may be related to some, but
not to others, further complicating the design,
and potentially reducing the utility of the strata.
Finally, in some cases (such as designs with a
large number of strata, or those with a specified
minimum sample size per group), stratified
sampling can potentially require a larger sample
than would other methods
28. POSTSTRATIFICATION
28
Stratification is sometimes introduced after the
sampling phase in a process called "poststratification“.
This approach is typically implemented due to a lack of
prior knowledge of an appropriate stratifying variable
or when the experimenter lacks the necessary
information to create a stratifying variable during the
sampling phase. Although the method is susceptible to
the pitfalls of post hoc approaches, it can provide
several benefits in the right situation. Implementation
usually follows a simple random sample. In addition to
allowing for stratification on an ancillary variable,
poststratification can be used to implement weighting,
which can improve the precision of a sample's
estimates.
29. OVERSAMPLING
29
Choice-based sampling is one of the stratified
sampling strategies. In this, data are
stratified on the target and a sample is taken
from each strata so that the rare target class
will be more represented in the sample. The
model is then built on this biased sample. The
effects of the input variables on the target
are often estimated with more precision with
the choice-based sample even when a smaller
overall sample size is taken, compared to a
random sample. The results usually must be
adjusted to correct for the oversampling.
30. CLUSTER SAMPLING
30
Cluster sampling is an example of 'two-stage
sampling' .
First stage a sample of areas is chosen;
Second stage a sample of respondents within
those areas is selected.
Population divided into clusters of homogeneous
units, usually based on geographical contiguity.
Sampling units are groups rather than individuals.
A sample of such clusters is then selected.
All units from the selected clusters are studied.
31. CLUSTER SAMPLING…….
31
Advantages :
Cuts down on the cost of preparing a sampling
frame.
This can reduce travel and other
administrative costs.
Disadvantages: sampling error is higher for a
simple random sample of same size.
Often used to evaluate vaccination coverage in
EPI
32. CLUSTER SAMPLING…….
32
• Identification of clusters
– List all cities, towns, villages & wards of cities with
their population falling in target area under study.
– Calculate cumulative population & divide by 30, this
gives sampling interval.
– Select a random no. less than or equal to sampling
interval having same no. of digits. This forms 1st
cluster.
– Random no.+ sampling interval = population of 2nd
cluster.
– Second cluster + sampling interval = 4th
cluster.
– Last or 30th
cluster = 29th
cluster + sampling interval
33. CLUSTER SAMPLING…….
33
Two types of cluster sampling methods.
One-stage sampling. All of the elements within
selected clusters are included in the sample.
Two-stage sampling. A subset of elements
within selected clusters are randomly selected
for inclusion in the sample.
34. CLUSTER SAMPLING…….
34
• Freq c f cluster
• I 2000 2000 1
• II 3000 5000 2
• III 1500 6500
• IV 4000 10500 3
• V 5000 15500 4, 5
• VI 2500 18000 6
• VII 2000 20000 7
• VIII 3000 23000 8
• IX 3500 26500 9
• X 4500 31000 10
• XI 4000 35000 11, 12
• XII 4000 39000 13
• XIII 3500 44000 14,15
• XIV 2000 46000
• XV 3000 49000 16
• XVI 3500 52500 17
• XVII 4000 56500 18,19
• XVIII 4500 61000 20
• XIX 4000 65000 21,22
• XX 4000 69000 23
• XXI 2000 71000 24
• XXII 2000 73000
• XXIII 3000 76000 25
• XXIV 3000 79000 26
• XXV 5000 84000 27,28
• XXVI 2000 86000 29
• XXVII 1000 87000
• XXVIII 1000 88000
• XXIX 1000 89000 30
• XXX 1000 90000
• 90000/30 = 3000 sampling interval
35. Difference Between Strata and Clusters
35
Although strata and clusters are both non-
overlapping subsets of the population, they
differ in several ways.
All strata are represented in the sample; but
only a subset of clusters are in the sample.
With stratified sampling, the best survey
results occur when elements within strata are
internally homogeneous. However, with cluster
sampling, the best results occur when elements
within clusters are internally heterogeneous
36. MULTISTAGE SAMPLING
36
Complex form of cluster sampling in which two or more levels of
units are embedded one in the other.
First stage, random number of districts chosen in all
states.
Followed by random number of talukas, villages.
Then third stage units will be houses.
All ultimate units (houses, for instance) selected at last step are
surveyed.
37. MULTISTAGE SAMPLING……..
37
This technique, is essentially the process of taking random
samples of preceding random samples.
Not as effective as true random sampling, but probably
solves more of the problems inherent to random sampling.
An effective strategy because it banks on multiple
randomizations. As such, extremely useful.
Multistage sampling used frequently when a complete list
of all members of the population not exists and is
inappropriate.
Moreover, by avoiding the use of all sample units in all
selected clusters, multistage sampling avoids the large,
and perhaps unnecessary, costs associated with traditional
cluster sampling.
38. MULTI PHASE SAMPLING
38
Part of the information collected from whole sample & part from
subsample.
In Tb survey MT in all cases – Phase I
X –Ray chest in MT +ve cases – Phase II
Sputum examination in X – Ray +ve cases - Phase III
Survey by such procedure is less costly, less laborious & more
purposeful
39. MATCHED RANDOM SAMPLING
39
A method of assigning participants to groups in which
pairs of participants are first matched on some
characteristic and then individually assigned randomly to
groups.
The Procedure for Matched random sampling can be
briefed with the following contexts,
Two samples in which the members are clearly paired, or
are matched explicitly by the researcher. For example,
IQ measurements or pairs of identical twins.
Those samples in which the same attribute, or variable, is
measured twice on each subject, under different
circumstances. Commonly called repeated measures.
Examples include the times of a group of athletes for
1500m before and after a week of special training; the
milk yields of cows before and after being fed a
particular diet.
40. QUOTA SAMPLING
40
The population is first segmented into mutually exclusive
sub-groups, just as in stratified sampling.
Then judgment used to select subjects or units from
each segment based on a specified proportion.
For example, an interviewer may be told to sample 200
females and 300 males between the age of 45 and 60.
It is this second step which makes the technique one of
non-probability sampling.
In quota sampling the selection of the sample is non-
random.
For example interviewers might be tempted to interview
those who look most helpful. The problem is that these
samples may be biased because not everyone gets a
chance of selection. This random element is its greatest
weakness and quota versus probability has been a matter
of controversy for many years
41. CONVENIENCE SAMPLING
41
Sometimes known as grab or opportunity sampling or accidental
or haphazard sampling.
A type of nonprobability sampling which involves the sample being
drawn from that part of the population which is close to hand.
That is, readily available and convenient.
The researcher using such a sample cannot scientifically make
generalizations about the total population from this sample
because it would not be representative enough.
For example, if the interviewer was to conduct a survey at a
shopping center early in the morning on a given day, the people
that he/she could interview would be limited to those given there
at that given time, which would not represent the views of other
members of society in such an area, if the survey was to be
conducted at different times of day and several times per week.
This type of sampling is most useful for pilot testing.
In social science research, snowball sampling is a similar technique,
where existing study subjects are used to recruit more subjects
into the sample.
43. Judgmental sampling or Purposive
sampling
43
- The researcher chooses the sample based on
who they think would be appropriate for the
study. This is used primarily when there is a
limited number of people that have expertise
in the area being researched
44. PANEL SAMPLING
44
Method of first selecting a group of participants through a
random sampling method and then asking that group for the same
information again several times over a period of time.
Therefore, each participant is given same survey or interview at
two or more time points; each period of data collection called a
"wave".
This sampling methodology often chosen for large scale or nation-
wide studies in order to gauge changes in the population with
regard to any number of variables from chronic illness to job
stress to weekly food expenditures.
Panel sampling can also be used to inform researchers about
within-person health changes due to age or help explain changes in
continuous dependent variables such as spousal interaction.
There have been several proposed methods of analyzing panel
sample data, including growth curves.
46. What sampling method u recommend?
46
Determining proportion of undernourished five year
olds in a village.
Investigating nutritional status of preschool children.
Selecting maternity records for the study of previous
abortions or duration of postnatal stay.
In estimation of immunization coverage in a province,
data on seven children aged 12-23 months in 30
clusters are used to determine proportion of fully
immunized children in the province.
Give reasons why cluster sampling is used in this
survey.
47. Probability proportional to size
sampling
47
In some cases the sample designer has access to an "auxiliary variable"
or "size measure", believed to be correlated to the variable of
interest, for each element in the population. This data can be used to
improve accuracy in sample design. One option is to use the auxiliary
variable as a basis for stratification, as discussed above.
Another option is probability-proportional-to-size ('PPS') sampling,
in which the selection probability for each element is set to be
proportional to its size measure, up to a maximum of 1. In a simple
PPS design, these selection probabilities can then be used as the basis
for Poisson sampling. However, this has the drawbacks of variable
sample size, and different portions of the population may still be
over- or under-represented due to chance variation in selections. To
address this problem, PPS may be combined with a systematic
approach.
48. Contd.
48
Example: Suppose we have six schools with populations of 150,
180, 200, 220, 260, and 490 students respectively (total 1500
students), and we want to use student population as the basis for a
PPS sample of size three. To do this, we could allocate the first
school numbers 1 to 150, the second school 151 to
330 (= 150 + 180), the third school 331 to 530, and so on to the
last school (1011 to 1500). We then generate a random start
between 1 and 500 (equal to 1500/3) and count through the school
populations by multiples of 500. If our random start was 137, we
would select the schools which have been allocated numbers 137,
637, and 1137, i.e. the first, fourth, and sixth schools.
The PPS approach can improve accuracy for a given sample size by
concentrating sample on large elements that have the greatest
impact on population estimates. PPS sampling is commonly used
for surveys of businesses, where element size varies greatly and
auxiliary information is often available - for instance, a survey
attempting to measure the number of guest-nights spent in hotels
might use each hotel's number of rooms as an auxiliary variable. In
some cases, an older measurement of the variable of interest can be
used as an auxiliary variable when attempting to produce more
current estimates.
49. Event sampling
49
Event Sampling Methodology (ESM) is a new form of
sampling method that allows researchers to study ongoing
experiences and events that vary across and within days in its
naturally-occurring environment. Because of the frequent
sampling of events inherent in ESM, it enables researchers to
measure the typology of activity and detect the temporal and
dynamic fluctuations of work experiences. Popularity of ESM as a
new form of research design increased over the recent years
because it addresses the shortcomings of cross-sectional research,
where once unable to, researchers can now detect intra-individual
variances across time. In ESM, participants are asked to record
their experiences and perceptions in a paper or electronic diary.
There are three types of ESM:# Signal contingent – random
beeping notifies participants to record data. The advantage of this
type of ESM is minimization of recall bias.
Event contingent – records data when certain events occur
50. Contd.
50
Event contingent – records data when certain events occur
Interval contingent – records data according to the passing of a
certain period of time
ESM has several disadvantages. One of the disadvantages of ESM is
it can sometimes be perceived as invasive and intrusive by
participants. ESM also leads to possible self-selection bias. It may
be that only certain types of individuals are willing to participate
in this type of study creating a non-random sample. Another
concern is related to participant cooperation. Participants may not
be actually fill out their diaries at the specified times.
Furthermore, ESM may substantively change the phenomenon
being studied. Reactivity or priming effects may occur, such that
repeated measurement may cause changes in the participants'
experiences. This method of sampling data is also highly
vulnerable to common method variance.[6]
51. contd.
51
Further, it is important to think about whether or not an
appropriate dependent variable is being used in an ESM design.
For example, it might be logical to use ESM in order to answer
research questions which involve dependent variables with a great
deal of variation throughout the day. Thus, variables such as
change in mood, change in stress level, or the immediate impact
of particular events may be best studied using ESM methodology.
However, it is not likely that utilizing ESM will yield meaningful
predictions when measuring someone performing a repetitive task
throughout the day or when dependent variables are long-term in
nature (coronary heart problems).
Editor's Notes
PROBLEM STATEMENT, PURPOSES, BENEFITS
What exactly do I want to find out?
What is a researchable problem?
What are the obstacles in terms of knowledge, data availability, time, or resources?
Do the benefits outweigh the costs?
THEORY, ASSUMPTIONS, BACKGROUND LITERATURE
What does the relevant literature in the field indicate about this problem?
Which theory or conceptual framework does the work fit within?
What are the criticisms of this approach, or how does it constrain the research process?
What do I know for certain about this area?
What is the background to the problem that needs to be made available in reporting the work?
VARIABLES AND HYPOTHESES
What will I take as given in the environment ie what is the starting point?
Which are the independent and which are the dependent variables?
Are there control variables?
Is the hypothesis specific enough to be researchable yet still meaningful?
How certain am I of the relationship(s) between variables?
OPERATIONAL DEFINITIONS AND MEASUREMENT
Does the problem need scoping/simplifying to make it achievable?
What and how will the variables be measured?
What degree of error in the findings is tolerable?
Is the approach defendable?
RESEARCH DESIGN AND METHODOLOGY
What is my overall strategy for doing this research?
Will this design permit me to answer the research question?
What constraints will the approach place on the work?
INSTRUMENTATION/SAMPLING
How will I get the data I need to test my hypothesis?
What tools or devices will I use to make or record observations?
Are valid and reliable instruments available, or must I construct my own?
How will I choose the sample?
Am I interested in representativeness?
If so, of whom or what, and with what degree of accuracy or level of confidence?
DATA ANALYSIS
What combinations of analytical and statistical process will be applied to the data?
Which of these will allow me to accept or reject my hypotheses?
Do the findings show numerical differences, and are those differences important?
CONCLUSIONS, INTERPRETATIONS, RECOMMENDATIONS
Was my initial hypothesis supported?
What if my findings are negative?
What are the implications of my findings for the theory base, for the background assumptions, or relevant literature?
What recommendations result from the work?
What suggestions can I make for further research on this topic?
Sampling frame errors: university versus personal email addresses; changing class rosters; are all students in your population of interest represented?
How do we determine our population of interest?
Administrators can tell us
We notice anecdotally or through qualitative research that a particular subgroup of students is experiencing higher risk
We decide to do everyone and go from there
3 factors that influence sample representativeness
Sampling procedure
Sample size
Participation (response)
When might you sample the entire population?
When your population is very small
When you have extensive resources
When you don’t expect a very high response
Picture of sampling breakdown
Two general approaches to sampling are used in social science research. With probability sampling, all elements (e.g., persons, households) in the population have some opportunity of being included in the sample, and the mathematical probability that any one of them will be selected can be calculated. With nonprobability sampling, in contrast, population elements are selected on the basis of their availability (e.g., because they volunteered) or because of the researcher's personal judgment that they are representative. The consequence is that an unknown portion of the population is excluded (e.g., those who did not volunteer). One of the most common types of nonprobability sample is called a convenience sample – not because such samples are necessarily easy to recruit, but because the researcher uses whatever individuals are available rather than selecting from the entire population.
Because some members of the population have no chance of being sampled, the extent to which a convenience sample – regardless of its size – actually represents the entire population cannot be known