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 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 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 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 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.
This document discusses various sampling methods used in research studies. It begins with defining key terms like population, sampling, target population and sampling frame. It then describes the main types of sampling methods - probability sampling methods like simple random sampling, stratified random sampling and cluster sampling as well as non-probability sampling methods like convenience sampling and snowball sampling. The advantages and limitations of different sampling methods are provided. The document emphasizes that probability sampling allows generalization of results to the target population while non-probability sampling does not. It concludes by noting some sources of error in sampling.
The document provides an overview of different sampling techniques, including:
- Probability sampling techniques like simple random sampling, stratified random sampling, systematic sampling, and probability proportional to size sampling.
- Non-probability sampling techniques like judgmental sampling, convenience sampling, and quota sampling.
- It defines key sampling concepts like population, sample, sampling frame, and explains the stages of sampling such as defining the population, selecting a sampling frame, sample selection, data collection, and inference.
- For each sampling technique, it provides examples, methodology, merits and demerits to help understand how to apply them for research sampling.
This document discusses various types of sampling techniques used in research methodology. It describes nonprobability sampling techniques like convenience sampling, judgmental sampling, quota sampling, and snowball sampling. It also explains probability sampling techniques such as simple random sampling, systematic sampling, stratified sampling, cluster sampling, and other sampling techniques. The key points are that sampling techniques can be either nonprobability or probability based, and each has distinct characteristics for selecting samples from a population.
This document discusses different types of sampling methods used in statistics. It defines key terms like population, sample, and random sampling. It then explains different random sampling techniques like simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also discusses the differences between strata and clusters. Finally, it briefly introduces some non-random sampling methods like quota sampling and convenience sampling.
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 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 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 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.
This document discusses various sampling methods used in research studies. It begins with defining key terms like population, sampling, target population and sampling frame. It then describes the main types of sampling methods - probability sampling methods like simple random sampling, stratified random sampling and cluster sampling as well as non-probability sampling methods like convenience sampling and snowball sampling. The advantages and limitations of different sampling methods are provided. The document emphasizes that probability sampling allows generalization of results to the target population while non-probability sampling does not. It concludes by noting some sources of error in sampling.
The document provides an overview of different sampling techniques, including:
- Probability sampling techniques like simple random sampling, stratified random sampling, systematic sampling, and probability proportional to size sampling.
- Non-probability sampling techniques like judgmental sampling, convenience sampling, and quota sampling.
- It defines key sampling concepts like population, sample, sampling frame, and explains the stages of sampling such as defining the population, selecting a sampling frame, sample selection, data collection, and inference.
- For each sampling technique, it provides examples, methodology, merits and demerits to help understand how to apply them for research sampling.
This document discusses various types of sampling techniques used in research methodology. It describes nonprobability sampling techniques like convenience sampling, judgmental sampling, quota sampling, and snowball sampling. It also explains probability sampling techniques such as simple random sampling, systematic sampling, stratified sampling, cluster sampling, and other sampling techniques. The key points are that sampling techniques can be either nonprobability or probability based, and each has distinct characteristics for selecting samples from a population.
This document discusses different types of sampling methods used in statistics. It defines key terms like population, sample, and random sampling. It then explains different random sampling techniques like simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also discusses the differences between strata and clusters. Finally, it briefly introduces some non-random sampling methods like quota sampling and convenience sampling.
This chapter discusses sampling and sampling distributions. It defines key terms like population, parameter, sample, and statistic. It also differentiates between a population and a sample. The chapter covers different sampling methods like simple random sampling, stratified random sampling, and cluster sampling. It describes the properties of the sampling distribution of the sample mean, including its expected value and standard deviation. The chapter also explains the central limit theorem.
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
This document provides an overview of sampling methods and the central limit theorem. It defines key learning objectives around sampling, describes common probability sampling methods like simple random sampling and stratified random sampling. It introduces the concept of sampling distribution of the sample mean and explains how the central limit theorem states that the sampling distribution of the sample mean approximates a normal distribution, even if the population is not normally distributed.
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.
This document discusses different sampling methods used in research. It defines population and sample, and explains that sampling is used to select a subset of a population when the entire population is too large. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection and allows results to be generalized to the population, while non-probability sampling relies on the researcher's judgment and results cannot be generalized. Specific probability sampling methods described include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Non-probability sampling methods mentioned are convenience sampling, snowball sampling, quota sampling, and judgmental sampling.
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Sampling Techniques for population and Material
1) Simple Random Sampling
2) Stratified Sampling
3) Systematic Sampling
4) Cluster Sampling
5) Two stage sampling.
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.
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.
This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.
This document discusses sampling methods used in statistical analysis. It defines key terms like population, sample, and sampling. It then describes different types of sampling methods including probability sampling techniques like simple random sampling, stratified sampling, cluster sampling and systematic cluster sampling. It also discusses non-probability sampling techniques such as convenience sampling, purposive sampling, quota sampling, and referral sampling. The document notes advantages of sampling like reduced time and cost. It also discusses sources of sampling error and how sampling accuracy can be improved.
1) Sampling is the process of selecting a subset of individuals from within a population to gather information about the whole population. It allows researchers to learn about a larger population without studying every member.
2) 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 members are selected in a non-random way.
3) Some common probability sampling methods include simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. Common non-probability methods include convenience sampling and purposive sampling.
This document discusses different sampling methods used in research studies. It describes probability sampling methods like simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. It explains that probability sampling allows for statistical measurement of random error. The document also covers non-probability sampling methods including convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling. These do not allow for statistical measurement of variability and bias. The key sampling methods and their advantages and disadvantages are summarized.
CABT SHS Statistics & Probability - Sampling Distribution of MeansGilbert Joseph Abueg
This document is a presentation on sampling distributions of means for a Grade 11 Statistics and Probability lecture. It begins by defining populations and samples, and explaining how inferential statistics makes conclusions about populations based on sample data. It then discusses different sampling techniques like simple random sampling, systematic random sampling, stratified random sampling and cluster sampling. The key concepts of parameters, statistics, and sampling distributions are also introduced. Examples are provided to illustrate how to construct sampling distributions of means.
This document discusses various sampling methods used in research. It defines key terms like population, sample, and element. It describes characteristics of a good sample and the steps involved in the sampling process. Several probability and non-probability sampling techniques are explained in detail, including simple random sampling, stratified random sampling, systematic sampling, cluster sampling, area sampling, and multistage sampling. Advantages and limitations of each method are provided.
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 chapter discusses sampling and sampling distributions. It defines key terms like population, parameter, sample, and statistic. It also differentiates between a population and a sample. The chapter covers different sampling methods like simple random sampling, stratified random sampling, and cluster sampling. It describes the properties of the sampling distribution of the sample mean, including its expected value and standard deviation. The chapter also explains the central limit theorem.
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
This document provides an overview of sampling methods and the central limit theorem. It defines key learning objectives around sampling, describes common probability sampling methods like simple random sampling and stratified random sampling. It introduces the concept of sampling distribution of the sample mean and explains how the central limit theorem states that the sampling distribution of the sample mean approximates a normal distribution, even if the population is not normally distributed.
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.
This document discusses different sampling methods used in research. It defines population and sample, and explains that sampling is used to select a subset of a population when the entire population is too large. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection and allows results to be generalized to the population, while non-probability sampling relies on the researcher's judgment and results cannot be generalized. Specific probability sampling methods described include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Non-probability sampling methods mentioned are convenience sampling, snowball sampling, quota sampling, and judgmental sampling.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Sampling Techniques for population and Material
1) Simple Random Sampling
2) Stratified Sampling
3) Systematic Sampling
4) Cluster Sampling
5) Two stage sampling.
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.
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.
This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.
This document discusses sampling methods used in statistical analysis. It defines key terms like population, sample, and sampling. It then describes different types of sampling methods including probability sampling techniques like simple random sampling, stratified sampling, cluster sampling and systematic cluster sampling. It also discusses non-probability sampling techniques such as convenience sampling, purposive sampling, quota sampling, and referral sampling. The document notes advantages of sampling like reduced time and cost. It also discusses sources of sampling error and how sampling accuracy can be improved.
1) Sampling is the process of selecting a subset of individuals from within a population to gather information about the whole population. It allows researchers to learn about a larger population without studying every member.
2) 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 members are selected in a non-random way.
3) Some common probability sampling methods include simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. Common non-probability methods include convenience sampling and purposive sampling.
This document discusses different sampling methods used in research studies. It describes probability sampling methods like simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. It explains that probability sampling allows for statistical measurement of random error. The document also covers non-probability sampling methods including convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling. These do not allow for statistical measurement of variability and bias. The key sampling methods and their advantages and disadvantages are summarized.
CABT SHS Statistics & Probability - Sampling Distribution of MeansGilbert Joseph Abueg
This document is a presentation on sampling distributions of means for a Grade 11 Statistics and Probability lecture. It begins by defining populations and samples, and explaining how inferential statistics makes conclusions about populations based on sample data. It then discusses different sampling techniques like simple random sampling, systematic random sampling, stratified random sampling and cluster sampling. The key concepts of parameters, statistics, and sampling distributions are also introduced. Examples are provided to illustrate how to construct sampling distributions of means.
This document discusses various sampling methods used in research. It defines key terms like population, sample, and element. It describes characteristics of a good sample and the steps involved in the sampling process. Several probability and non-probability sampling techniques are explained in detail, including simple random sampling, stratified random sampling, systematic sampling, cluster sampling, area sampling, and multistage sampling. Advantages and limitations of each method are provided.
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.
A monitoring and evaluation system is needed to assess both structural and health sector components of the response to HIV in key populations. It is critical that these systems are practical, not overly complicated, and that they collect information that is current, useful and readily used
The document discusses monitoring and evaluation frameworks for EMTCT (Elimination of Mother to Child Transmission) programmes. It notes that M&E systems need to be practical and collect useful, current information. Validation indicators and targets are used to monitor achieving EMTCT goals over time. Data is collected through various tools at the individual, facility, local, national and global levels for reporting and decision making purposes. Accurate record keeping and data management are important for monitoring implementation and evaluation of EMTCT programmes.
The document discusses meat hygiene and inspection, outlining the origins and objectives of quality control measures for meat intended for human consumption. It defines important terms, describes ante-mortem and post-mortem inspection procedures, and outlines principles for the humane slaughter of food animals according to various religious traditions or scientific methods involving pre-slaughter stunning.
Updated copy (introductio to environmental epidemiology & bio statistics)Nasiru Ibrahim Barda
This document provides an overview of environmental epidemiology and bio-statistics. It defines epidemiology as dealing with the incidence, distribution, and possible control of diseases and other factors relating to health in populations. Environmental epidemiology specifically studies how environmental exposures impact human health. The objectives of epidemiology are described as public health surveillance, field investigation, analytic studies, evaluation, and policy development. Environmental epidemiology aims to identify environmental hazards, populations exposed, exposure levels, health impacts, and approaches to reduce exposures.
The document provides information about food hygiene and inspection. It begins with definitions of key terms like food, food safety hazards, food hygiene, and food poisoning. It describes the objectives of food hygiene which include ensuring food is safe and handled safely. It discusses the WHO's 10 rules for safe food practices. It also covers determining whether a food item is wholesome or unwholesome, food analysis and quality control, reasons for and importance of food hygiene, methods for food inspection, and how to interpret laboratory results.
This document discusses environmental impact assessments (EIAs). It provides an overview of the EIA process, including its objectives, key principles, steps, and definitions. The main points are:
1) The EIA process identifies and assesses potential environmental impacts of projects to inform decision making and encourage environmentally positive outcomes.
2) Key steps include screening, scoping, impact prediction and mitigation planning, management and monitoring, and auditing.
3) The overall goals of EIA are to assist with project planning, ensure sound environmental implementation, and provide management information for monitoring impacts.
Polio is a highly infectious disease caused by a virus that spreads from person to person and can cause paralysis or death. It primarily affects children under 5 years old and is transmitted through contact with the feces of an infected person or through contaminated food or water. While vaccines were developed in the 1950s-60s that largely eliminated polio in industrialized nations, it remains endemic in only 4 countries as of 2006 - Afghanistan, India, Nigeria, and Pakistan. Controlling and preventing polio requires widespread vaccination efforts, especially of children under 1, as well as active disease surveillance.
As Mumbai's premier kidney transplant and donation center, L H Hiranandani Hospital Powai is not just a medical facility; it's a beacon of hope where cutting-edge science meets compassionate care, transforming lives and redefining the standards of kidney health in India.
Sectional dentures for microstomia patients.pptxSatvikaPrasad
Microstomia, characterized by an abnormally small oral aperture, presents significant challenges in prosthodontic treatment, including limited access for examination, difficulties in impression making, and challenges with prosthesis insertion and removal. To manage these issues, customized impression techniques using sectional trays and elastomeric materials are employed. Prostheses may be designed in segments or with flexible materials to facilitate handling. Minimally invasive procedures and the use of digital technologies can enhance patient comfort. Education and training for patients on prosthesis care and maintenance are crucial for compliance. Regular follow-up and a multidisciplinary approach, involving collaboration with other specialists, ensure comprehensive care and improved quality of life for microstomia patients.
End-tidal carbon dioxide (ETCO2) is the level of carbon dioxide that is released at the end of an exhaled breath. ETCO2 levels reflect the adequacy with which carbon dioxide (CO2) is carried in the blood back to the lungs and exhaled.
Non-invasive methods for ETCO2 measurement include capnometry and capnography. Capnometry provides a numerical value for ETCO2. In contrast, capnography delivers a more comprehensive measurement that is displayed in both graphical (waveform) and numerical form.
Sidestream devices can monitor both intubated and non-intubated patients, while mainstream devices are most often limited to intubated patients.
This particular slides consist of- what is hypotension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is the summary of hypotension:
Hypotension, or low blood pressure, is when the pressure of blood circulating in the body is lower than normal or expected. It's only a problem if it negatively impacts the body and causes symptoms. Normal blood pressure is usually between 90/60 mmHg and 120/80 mmHg, but pressures below 90/60 are generally considered hypotensive.
Hypertension and it's role of physiotherapy in it.Vishal kr Thakur
This particular slides consist of- what is hypertension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is summary of hypertension -
Hypertension, also known as high blood pressure, is a serious medical condition that occurs when blood pressure in the body's arteries is consistently too high. Blood pressure is the force of blood pushing against the walls of blood vessels as the heart pumps it. Hypertension can increase the risk of heart disease, brain disease, kidney disease, and premature death.
CHAPTER 1 SEMESTER V COMMUNICATION TECHNIQUES FOR CHILDREN.pdfSachin Sharma
Here are some key objectives of communication with children:
Build Trust and Security:
Establish a safe and supportive environment where children feel comfortable expressing themselves.
Encourage Expression:
Enable children to articulate their thoughts, feelings, and experiences.
Promote Emotional Understanding:
Help children identify and understand their own emotions and the emotions of others.
Enhance Listening Skills:
Develop children’s ability to listen attentively and respond appropriately.
Foster Positive Relationships:
Strengthen the bond between children and caregivers, peers, and other adults.
Support Learning and Development:
Aid cognitive and language development through engaging and meaningful conversations.
Teach Social Skills:
Encourage polite, respectful, and empathetic interactions with others.
Resolve Conflicts:
Provide tools and guidance for children to handle disagreements constructively.
Encourage Independence:
Support children in making decisions and solving problems on their own.
Provide Reassurance and Comfort:
Offer comfort and understanding during times of distress or uncertainty.
Reinforce Positive Behavior:
Acknowledge and encourage positive actions and behaviors.
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Offer clear instructions and explanations to help children understand expectations and learn new concepts.
By focusing on these objectives, communication with children can be both effective and nurturing, supporting their overall growth and well-being.
TEST BANK FOR Health Assessment in Nursing 7th Edition by Weber Chapters 1 - ...rightmanforbloodline
TEST BANK FOR Health Assessment in Nursing 7th Edition by Weber Chapters 1 - 34.
TEST BANK FOR Health Assessment in Nursing 7th Edition by Weber Chapters 1 - 34.
TEST BANK FOR Health Assessment in Nursing 7th Edition by Weber Chapters 1 - 34.
NURSING MANAGEMENT OF PATIENT WITH EMPHYSEMA .PPTblessyjannu21
Prepared by Prof. BLESSY THOMAS, VICE PRINCIPAL, FNCON, SPN.
Emphysema is a disease condition of respiratory system.
Emphysema is an abnormal permanent enlargement of the air spaces distal to terminal bronchioles, accompanied by destruction of their walls and without obvious fibrosis.
Emphysema of lung is defined as hyper inflation of the lung ais spaces due to obstruction of non respiratory bronchioles as due to loss of elasticity of alveoli.
It is a type of chronic obstructive
pulmonary disease.
It is a progressive disease of lungs.
Fit to Fly PCR Covid Testing at our Clinic Near YouNX Healthcare
A Fit-to-Fly PCR Test is a crucial service for travelers needing to meet the entry requirements of various countries or airlines. This test involves a polymerase chain reaction (PCR) test for COVID-19, which is considered the gold standard for detecting active infections. At our travel clinic in Leeds, we offer fast and reliable Fit to Fly PCR testing, providing you with an official certificate verifying your negative COVID-19 status. Our process is designed for convenience and accuracy, with quick turnaround times to ensure you receive your results and certificate in time for your departure. Trust our professional and experienced medical team to help you travel safely and compliantly, giving you peace of mind for your journey.www.nxhealthcare.co.uk
This particular slides consist of- what is Pneumothorax,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is a summary of Pneumothorax:
Pneumothorax, also known as a collapsed lung, is a condition that occurs when air leaks into the space between the lung and chest wall. This air buildup puts pressure on the lung, preventing it from expanding fully when you breathe. A pneumothorax can cause a complete or partial collapse of the lung.
The facial nerve, also known as cranial nerve VII, is one of the 12 cranial nerves originating from the brain. It's a mixed nerve, meaning it contains both sensory and motor fibres, and it plays a crucial role in controlling various facial muscles, as well as conveying sensory information from the taste buds on the anterior two-thirds of the tongue.
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