I am HAFIZ M WASEEM FROM mailsi vehari
BSc in science college Multan Pakistan
MSC university of education Lahore Pakistan
i love Pakistan and my teachers
This document discusses the importance of sample size in primary studies. It notes that an adequate sample size is needed to achieve valid and significant results, and the goal is to recruit just enough participants for a specified level of certainty. Larger sample sizes are generally better than smaller ones as they yield results closer to the true population values. The document recommends using a sample size calculator to estimate the appropriate minimum sample size based on characteristics of the sample population. It also discusses estimating statistical power to check if a sample size provides adequate power for the study design.
Sampling - everything you need to know in the basics of sampling!!!!Anju George
This presentation deals with basic terminologies, characteristics, purposes, sampling process, factors influencing, non probability, probability sampling, sample size determination, For more PPTs in nursing research visit https://www.slideshare.net/AnjuJijo
The document discusses research methods for population and sampling. It defines population as the entire group about which information is desired, while a sample is a subset of the population. There are two main methods for collecting data: census, which collects data from every member of the population, and sampling, which collects data from a subset of the population. Sampling provides advantages such as saving time and money compared to census, but also has limitations such as potential for biased results.
This document discusses different types of research populations that must be considered when designing a study. There are four main types: the target population the results should apply to, the source population the sample is drawn from, the sample population asked to participate, and the study population that actually does participate. Probability and non-probability sampling techniques are used to select the sample from the source population. The study population must be representative of the target population to generalize results. Vulnerable populations require extra protections and community involvement can aid some studies.
This document outlines different types of sampling methods used in quantitative and qualitative research. It discusses probabilistic sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling that are used in quantitative studies to select representative samples from a population. It also covers non-probabilistic sampling methods like convenience sampling and snowball sampling. For qualitative research, it describes purposeful sampling techniques such as maximal variation sampling, typical case sampling, theory-based sampling, and opportunistic sampling that target information-rich cases for in-depth study.
This document discusses sampling techniques in research methodology. It defines a population as the overall group being studied and sampling as selecting a subset of individuals from the population to collect data from. There are two main types of sampling: probability sampling, which uses random selection to choose participants so that all members of the population have an equal chance of being selected; and non-probability sampling, which does not rely on random selection and can result in more bias. Several examples of probability and non-probability sampling techniques are provided.
This document discusses various sampling methods and terminology used in research. It defines key terms like population, sample, sampling frame, and probability and nonprobability sampling. It describes different specific sampling techniques like simple random sampling, stratified sampling, cluster sampling, and internet sampling. It explains how to plan a sampling strategy and consider factors like desired accuracy, resources, and knowledge of the target population.
This document discusses the importance of sample size in primary studies. It notes that an adequate sample size is needed to achieve valid and significant results, and the goal is to recruit just enough participants for a specified level of certainty. Larger sample sizes are generally better than smaller ones as they yield results closer to the true population values. The document recommends using a sample size calculator to estimate the appropriate minimum sample size based on characteristics of the sample population. It also discusses estimating statistical power to check if a sample size provides adequate power for the study design.
Sampling - everything you need to know in the basics of sampling!!!!Anju George
This presentation deals with basic terminologies, characteristics, purposes, sampling process, factors influencing, non probability, probability sampling, sample size determination, For more PPTs in nursing research visit https://www.slideshare.net/AnjuJijo
The document discusses research methods for population and sampling. It defines population as the entire group about which information is desired, while a sample is a subset of the population. There are two main methods for collecting data: census, which collects data from every member of the population, and sampling, which collects data from a subset of the population. Sampling provides advantages such as saving time and money compared to census, but also has limitations such as potential for biased results.
This document discusses different types of research populations that must be considered when designing a study. There are four main types: the target population the results should apply to, the source population the sample is drawn from, the sample population asked to participate, and the study population that actually does participate. Probability and non-probability sampling techniques are used to select the sample from the source population. The study population must be representative of the target population to generalize results. Vulnerable populations require extra protections and community involvement can aid some studies.
This document outlines different types of sampling methods used in quantitative and qualitative research. It discusses probabilistic sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling that are used in quantitative studies to select representative samples from a population. It also covers non-probabilistic sampling methods like convenience sampling and snowball sampling. For qualitative research, it describes purposeful sampling techniques such as maximal variation sampling, typical case sampling, theory-based sampling, and opportunistic sampling that target information-rich cases for in-depth study.
This document discusses sampling techniques in research methodology. It defines a population as the overall group being studied and sampling as selecting a subset of individuals from the population to collect data from. There are two main types of sampling: probability sampling, which uses random selection to choose participants so that all members of the population have an equal chance of being selected; and non-probability sampling, which does not rely on random selection and can result in more bias. Several examples of probability and non-probability sampling techniques are provided.
This document discusses various sampling methods and terminology used in research. It defines key terms like population, sample, sampling frame, and probability and nonprobability sampling. It describes different specific sampling techniques like simple random sampling, stratified sampling, cluster sampling, and internet sampling. It explains how to plan a sampling strategy and consider factors like desired accuracy, resources, and knowledge of the target population.
This document discusses different types of sampling techniques, including non-probability and probability sampling. Non-probability sampling techniques include convenience, judgmental, snowball sampling, while probability sampling includes simple random sampling, systematic sampling, stratified sampling, and cluster sampling. The document provides details on each technique, including their objectives, processes, and uses.
This document discusses sampling fundamentals and different sampling methods. It begins by defining key terms like population, sample, census, and parameter/statistic. It then explains that the goal of sampling is to select a sample that is as representative of the population as possible in order to make accurate inferences. The document discusses probability and non-probability sampling methods. It provides details on specific probability methods like simple random sampling, stratified sampling, cluster sampling, and systematic sampling. It also briefly outlines non-probability sampling techniques and their uses and benefits.
The document discusses various sampling methods and concepts in research methodology. It defines key terms like population, sample, sampling frame, probability sampling, and non-probability sampling. It then explains different probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also discusses non-probability sampling methods and compares the advantages and disadvantages of different approaches. The document emphasizes the importance of representative sampling.
The document discusses sampling in educational research. It explains that sampling involves studying a representative subset of a population rather than the entire population due to limitations of time, resources and potential for greater accuracy. The key aspects covered are: defining the population and sampling frame; the advantages of sampling over a complete census; and the basic types of probability and non-probability sampling.
This document discusses various sampling strategies used in qualitative research including:
1) Purposeful sampling is used to select information-rich cases to answer research questions, such as extreme or typical cases.
2) Specific purposeful sampling strategies are described like maximum variation which selects cases from different conditions, and homogeneous groups which provides an in-depth look at a subgroup.
3) Other strategies discussed include snowball sampling which asks participants who else to interview, criterion sampling which uses predetermined criteria, and opportunistic sampling which makes on-the-spot decisions in the field.
This document discusses sampling methods used in research. It defines key terms like population, sample, element, and describes different sampling techniques. The main sampling methods covered are probability sampling techniques like simple random sampling, systematic sampling and stratified sampling which select samples randomly from the population. It also discusses non-probability sampling techniques like convenience sampling and purposive sampling which do not select samples randomly.
Sampling refers to selecting a subset of a population to make inferences about the whole population. There are two main types of sampling: probability sampling, which aims to be representative, and non-probability sampling. Probability sampling includes random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Non-probability sampling includes convenience sampling and snowball sampling. Sample size, standard error, and confidence levels allow researchers to assess how representative their sample is of the overall population.
The document discusses key concepts related to sampling and statistical inference in political science research. It defines populations and samples, and explains that samples are used to make inferences about populations that are too large to study in their entirety. It covers different sampling methods like simple random sampling, stratified sampling, and cluster sampling. It also discusses concepts like expected values, standard errors, sampling distributions, and how confidence intervals are used to assess the accuracy of sample estimates.
This document discusses non-probability sampling techniques. Non-probability sampling does not rely on random selection and samples may be biased. There are several types of non-probability sampling including convenience sampling, purposive sampling, quota sampling, and referral/snowball sampling. Convenience sampling selects samples based on availability, purposive sampling selects samples based on meeting specific criteria for the research purpose, quota sampling aims to select a representative sample with the same proportions as the overall population, and referral/snowball sampling starts with an initial sample and asks them to refer additional samples.
This document discusses various sampling techniques used in research. It defines key terms like population, sample, sampling, and sampling techniques. Some common non-probability sampling techniques discussed include convenience sampling, purposive sampling, and snowball sampling. Probability sampling techniques discussed include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. The document also discusses important considerations in sampling like sample size calculation and sampling distribution.
This document outlines different sampling methods used in quantitative and qualitative research. It discusses the purpose of sampling, stages in selecting a sample, and types of probability and non-probability sampling. For quantitative research, it describes random sampling, stratified random sampling, cluster sampling, and systematic sampling. For qualitative research, it discusses purposive sampling techniques like maximal variation sampling, typical case sampling, and theory or concept sampling. The document stresses the importance of representation and generalization in samples and notes some ethical considerations in data collection like maintaining trust and informing participants.
This document provides an overview of key concepts in sampling and statistics. It defines key terms like population, parameter, sample, and sampling error. It discusses different sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. It also covers non-probability sampling methods. The document explains how to calculate sampling error and discusses other sources of bias. Overall, it serves as an introduction to important statistical concepts for sample surveys and studies.
This document defines key population and sampling concepts for research. It discusses target and accessible populations, and how samples are selected using probability and non-probability sampling methods. Probability methods like simple random, stratified random, cluster random, and systematic sampling aim to select a representative sample where every member has an equal chance of being selected. Non-probability methods like convenience, purposive, and snowball sampling do not aim for representativeness. Sample size is important to reduce error and increase power to detect relationships.
Dynamic drivers of disease in Africa: Integration of participatory researchILRI
Presented by Peter Atkinson, Gianni Lo Iacono, Catherine Grant, Bernard Bett, Vupenyu Dzingirai, Tom Winnebah and other members of the Dynamic Drivers of Disease in Africa Consortium at the EcoHealth 2014 conference, Montreal, Canada, 11-15 August 2014.
The document discusses need analysis for students. It explains that need analysis includes collecting information about students' learning needs, wants, desires, expectations, motivations, lacks and requirements. It is an ongoing process that also considers the expectations of teachers, administrators and other stakeholders. The target population refers to the clearly defined group experiencing the problem or need. Effective need analysis identifies the target population, their characteristics, geographic distribution and barriers. Information can be collected through interviews, conversations, questionnaires, observations and tests. The analysis considers personal, economic and learning needs to develop an appropriate student profile and strategies.
This document discusses comparative research methods. It defines comparative research as comparing two or more treatments over time to classify social phenomena and explain differences. There are two main factors in comparative research: time and space. Cross-national comparisons are commonly used. Examples include a study comparing cancer patient experiences across Sweden, Denmark and England. Challenges include maintaining funding for multi-country studies and having sufficient cultural knowledge of the countries studied. Proper case selection and avoiding assumptions are important to avoid mistakes. Comparative research has potentials for analyzing shortcomings but can be time-consuming.
This document discusses various sampling methods used in research. It describes probability sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It also covers non-probability sampling techniques including convenience sampling, judgmental sampling, quota sampling, and snowball sampling. The document provides details on how to design sample plans and minimize errors in sampling.
Sampling technique in quantitative and qualitative researchIreneGabor
The document discusses various sampling techniques used in quantitative and qualitative research methods. It describes probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It explains that probability samples allow for generalizing results to larger populations. The document also covers non-probability sampling techniques commonly used in qualitative research, such as purposive sampling, quota sampling, snowball sampling, and convenience sampling. The goal of these techniques is in-depth understanding rather than generalizability.
This document discusses different types of sampling techniques, including non-probability and probability sampling. Non-probability sampling techniques include convenience, judgmental, snowball sampling, while probability sampling includes simple random sampling, systematic sampling, stratified sampling, and cluster sampling. The document provides details on each technique, including their objectives, processes, and uses.
This document discusses sampling fundamentals and different sampling methods. It begins by defining key terms like population, sample, census, and parameter/statistic. It then explains that the goal of sampling is to select a sample that is as representative of the population as possible in order to make accurate inferences. The document discusses probability and non-probability sampling methods. It provides details on specific probability methods like simple random sampling, stratified sampling, cluster sampling, and systematic sampling. It also briefly outlines non-probability sampling techniques and their uses and benefits.
The document discusses various sampling methods and concepts in research methodology. It defines key terms like population, sample, sampling frame, probability sampling, and non-probability sampling. It then explains different probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also discusses non-probability sampling methods and compares the advantages and disadvantages of different approaches. The document emphasizes the importance of representative sampling.
The document discusses sampling in educational research. It explains that sampling involves studying a representative subset of a population rather than the entire population due to limitations of time, resources and potential for greater accuracy. The key aspects covered are: defining the population and sampling frame; the advantages of sampling over a complete census; and the basic types of probability and non-probability sampling.
This document discusses various sampling strategies used in qualitative research including:
1) Purposeful sampling is used to select information-rich cases to answer research questions, such as extreme or typical cases.
2) Specific purposeful sampling strategies are described like maximum variation which selects cases from different conditions, and homogeneous groups which provides an in-depth look at a subgroup.
3) Other strategies discussed include snowball sampling which asks participants who else to interview, criterion sampling which uses predetermined criteria, and opportunistic sampling which makes on-the-spot decisions in the field.
This document discusses sampling methods used in research. It defines key terms like population, sample, element, and describes different sampling techniques. The main sampling methods covered are probability sampling techniques like simple random sampling, systematic sampling and stratified sampling which select samples randomly from the population. It also discusses non-probability sampling techniques like convenience sampling and purposive sampling which do not select samples randomly.
Sampling refers to selecting a subset of a population to make inferences about the whole population. There are two main types of sampling: probability sampling, which aims to be representative, and non-probability sampling. Probability sampling includes random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Non-probability sampling includes convenience sampling and snowball sampling. Sample size, standard error, and confidence levels allow researchers to assess how representative their sample is of the overall population.
The document discusses key concepts related to sampling and statistical inference in political science research. It defines populations and samples, and explains that samples are used to make inferences about populations that are too large to study in their entirety. It covers different sampling methods like simple random sampling, stratified sampling, and cluster sampling. It also discusses concepts like expected values, standard errors, sampling distributions, and how confidence intervals are used to assess the accuracy of sample estimates.
This document discusses non-probability sampling techniques. Non-probability sampling does not rely on random selection and samples may be biased. There are several types of non-probability sampling including convenience sampling, purposive sampling, quota sampling, and referral/snowball sampling. Convenience sampling selects samples based on availability, purposive sampling selects samples based on meeting specific criteria for the research purpose, quota sampling aims to select a representative sample with the same proportions as the overall population, and referral/snowball sampling starts with an initial sample and asks them to refer additional samples.
This document discusses various sampling techniques used in research. It defines key terms like population, sample, sampling, and sampling techniques. Some common non-probability sampling techniques discussed include convenience sampling, purposive sampling, and snowball sampling. Probability sampling techniques discussed include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. The document also discusses important considerations in sampling like sample size calculation and sampling distribution.
This document outlines different sampling methods used in quantitative and qualitative research. It discusses the purpose of sampling, stages in selecting a sample, and types of probability and non-probability sampling. For quantitative research, it describes random sampling, stratified random sampling, cluster sampling, and systematic sampling. For qualitative research, it discusses purposive sampling techniques like maximal variation sampling, typical case sampling, and theory or concept sampling. The document stresses the importance of representation and generalization in samples and notes some ethical considerations in data collection like maintaining trust and informing participants.
This document provides an overview of key concepts in sampling and statistics. It defines key terms like population, parameter, sample, and sampling error. It discusses different sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. It also covers non-probability sampling methods. The document explains how to calculate sampling error and discusses other sources of bias. Overall, it serves as an introduction to important statistical concepts for sample surveys and studies.
This document defines key population and sampling concepts for research. It discusses target and accessible populations, and how samples are selected using probability and non-probability sampling methods. Probability methods like simple random, stratified random, cluster random, and systematic sampling aim to select a representative sample where every member has an equal chance of being selected. Non-probability methods like convenience, purposive, and snowball sampling do not aim for representativeness. Sample size is important to reduce error and increase power to detect relationships.
Dynamic drivers of disease in Africa: Integration of participatory researchILRI
Presented by Peter Atkinson, Gianni Lo Iacono, Catherine Grant, Bernard Bett, Vupenyu Dzingirai, Tom Winnebah and other members of the Dynamic Drivers of Disease in Africa Consortium at the EcoHealth 2014 conference, Montreal, Canada, 11-15 August 2014.
The document discusses need analysis for students. It explains that need analysis includes collecting information about students' learning needs, wants, desires, expectations, motivations, lacks and requirements. It is an ongoing process that also considers the expectations of teachers, administrators and other stakeholders. The target population refers to the clearly defined group experiencing the problem or need. Effective need analysis identifies the target population, their characteristics, geographic distribution and barriers. Information can be collected through interviews, conversations, questionnaires, observations and tests. The analysis considers personal, economic and learning needs to develop an appropriate student profile and strategies.
This document discusses comparative research methods. It defines comparative research as comparing two or more treatments over time to classify social phenomena and explain differences. There are two main factors in comparative research: time and space. Cross-national comparisons are commonly used. Examples include a study comparing cancer patient experiences across Sweden, Denmark and England. Challenges include maintaining funding for multi-country studies and having sufficient cultural knowledge of the countries studied. Proper case selection and avoiding assumptions are important to avoid mistakes. Comparative research has potentials for analyzing shortcomings but can be time-consuming.
This document discusses various sampling methods used in research. It describes probability sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It also covers non-probability sampling techniques including convenience sampling, judgmental sampling, quota sampling, and snowball sampling. The document provides details on how to design sample plans and minimize errors in sampling.
Sampling technique in quantitative and qualitative researchIreneGabor
The document discusses various sampling techniques used in quantitative and qualitative research methods. It describes probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It explains that probability samples allow for generalizing results to larger populations. The document also covers non-probability sampling techniques commonly used in qualitative research, such as purposive sampling, quota sampling, snowball sampling, and convenience sampling. The goal of these techniques is in-depth understanding rather than generalizability.
Sampling by Dr. Rangappa AshiAssociate ProfessorSDM Institute of Nursing Sc...rangappa
In research studies it’s not
always possible to study an
entire population, therefore the
researcher draws a
representative part of a
population through sampling
process.
Sampling is the process of selecting a portion of a population to represent the entire population. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves random selection and allows researchers to generalize results to the population, while non-probability sampling does not allow for generalization due to potential biases in sample selection. Some common sampling methods include simple random sampling, stratified random sampling, cluster sampling, systematic sampling, convenience sampling, and purposive sampling. The goal of sampling is to obtain a group that is representative of the target population in order to make inferences about the population.
The document discusses different sampling methods used in business research. It defines sampling as selecting a smaller group from a larger population to make inferences about the whole population. There are two main types of sampling: probability sampling, which uses random selection so each unit has an equal chance of being chosen; and non-probability sampling, which relies on the researcher's judgement. Some key probability sampling methods described are simple random sampling, stratified random sampling, systematic sampling, and cluster random sampling. The main non-probability sampling techniques discussed are convenience sampling, judgmental sampling, quota sampling, and snowball sampling.
This document discusses sampling techniques used in educational research. It begins by defining key terms like population, sample, and sampling techniques. It then describes probability sampling methods like systematic sampling and non-probability sampling methods like purposive sampling. For systematic sampling, every kth unit is selected from an ordered population. Purposive sampling involves selecting units that are relevant to the research objectives. The document outlines the advantages and limitations of these sampling methods.
Sampling Techniques literture-Dr. Yasser Mohammed Hassanain Elsayed.pptxYasserMohammedHassan1
The document provides definitions and explanations of key concepts related to sampling techniques used in research. It discusses the differences between a population and a sample, and describes several probability and non-probability sampling methods, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and non-probability sampling. The document emphasizes the importance of selecting the appropriate sampling technique based on the research question and of clearly explaining the sampling method used in research studies.
Sampling refers to selecting a subset of a population for study. There are two main types of sampling: probability sampling, where every member of the population has a known, non-zero chance of being selected; and non-probability sampling, where some members are more likely to be selected than others. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Common non-probability sampling methods include convenience sampling, quota sampling, snowball sampling, and purposive sampling. Sample size and sampling method depend on factors like the study objectives, resources available, and characteristics of the target population.
Sampling is used when it is not feasible to study the entire population due to constraints of time, money, and resources. There are two main types of sampling - probability sampling and non-probability sampling. Some key sampling techniques include simple random sampling, stratified sampling, cluster sampling, systematic sampling, convenience sampling, and snowball sampling. It is important to select a sampling technique based on the characteristics of the population and research objectives to obtain a representative sample and minimize bias. Sample size depends on required confidence level, acceptable margin of error, and intended analyses.
This document discusses various sampling techniques used in research. It defines key terms like population, sample, and sampling frame. It describes probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also covers non-probability sampling techniques such as convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling. The document provides details on calculating sample size and discusses Cochran's sample size formula.
This document discusses sampling design and various sampling methods used in research. It defines key concepts like population, sampling frame, and sampling unit. It also describes different types of probability sampling designs including simple random sampling, systematic random sampling, and stratified random sampling. Non-probability sampling methods like convenience sampling are also briefly covered. The aims and advantages of sampling are to obtain representative results in a timely and cost-effective manner while minimizing bias.
This document discusses sampling and different sampling methods. It defines sampling as selecting a small group from a larger population to make conclusions about the whole group. There are probability and non-probability sampling methods. Probability methods like simple random, stratified, cluster, and systematic sampling give reliable representations by randomly selecting participants. Non-probability methods like convenience, judgmental, snowball, and quota sampling rely on researcher judgment and cannot generalize to the full population. The document provides examples and explanations of each sampling method.
This document discusses sampling and different sampling methods. It defines sampling as selecting a small group from a larger population to make conclusions about the whole group. There are probability and non-probability sampling methods. Probability methods like simple random, stratified, cluster, and systematic sampling give reliable representations if everyone has an equal chance of selection. Non-probability methods like convenience, judgmental, snowball, and quota sampling rely on the researcher's judgment and cannot generalize to the full population. The document provides examples and explanations of each sampling method.
This document discusses key concepts related to sampling in marketing research. It defines important terminology like population, element, sampling unit, and sample. It explains different sampling designs like probability sampling methods (e.g. random, stratified, cluster) and non-probability methods (e.g. convenience, judgment, quota, snowball). The document emphasizes that the sample size and sampling method should be chosen based on the characteristics of the target population and research objectives. Response rates and potential sources of error are also addressed.
this is an presentation regarding samples in research methodology in qualitative and quantitative approaches . this will be very useful basically this presentation most significant for university students those who are following and learning for the research methodology. in this i have discussed
what is sampling
why samples for research
sampling methods
size of sample
types of sample
advantages of sample
disadvantages of sample
process
sampling frame
time factor
sampling problems...
sampling in research methodology. qualitative and quantitative approach Samantha Jayasundara
This document discusses different sampling methods used in qualitative and quantitative research. It explains that qualitative research typically uses purposeful sampling to select information-rich cases, while quantitative research aims for random sampling to generalize to the population. Several specific sampling techniques are outlined for both qualitative and quantitative research, including their advantages and disadvantages. Key differences in the assumptions between qualitative and quantitative sampling are also highlighted.
Sampling Methods & Sampling Error PPT - For Seminar Amal G
This document discusses various sampling methods used in research including probability sampling techniques like simple random sampling, cluster sampling, systematic sampling, and stratified random sampling. It also covers non-probability sampling methods such as convenience sampling, judgmental sampling, quota sampling, and snowball sampling. The document explains how each method works with examples and concludes by defining sampling error and non-sampling error that can occur in research.
1) Sampling involves selecting a subset of a larger population to gather data from. It allows researchers to study large populations in a more efficient manner.
2) There are two main types of sampling methods - probability sampling and non-probability sampling. Probability sampling involves random selection to ensure representativeness, while non-probability sampling relies on convenience.
3) Common probability sampling methods include simple random sampling, stratified random sampling, systematic random sampling, and cluster sampling. Non-probability methods include quota sampling, convenience sampling, and purposive sampling. The document provides details on how each method is implemented.
This document defines key terms related to sampling design and methods. It discusses the differences between populations, target populations, and accessible populations. It also defines samples, sampling frames, strata, random selection, and representativeness. The document outlines several probability sampling methods, including simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. It also discusses non-probability sampling methods like convenience sampling, purposive sampling, and quota sampling.
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I AM HAFIZ MUHAMMAD WASEEM from mailsi vehari
BSc from science college Multan
MSC university of education Lahore
i love Pakistan and my teachers and my parents
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MSC university of education Lahore
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I AM HAFIZ MUHAMMAD WASEEM from mailsi vehari
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MSC university of education Lahore
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I AM HAFIZ MUHAMMAD WASEEM from mailsi vehari
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MSC university of education Lahore
i love Pakistan and my teachers and my parents
I AM HAFIZ MUHAMMAD WASEEM from mailsi vehari
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MSC university of education Lahore
i love Pakistan and my teachers and my parents
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I AM HAFIZ MUHAMMAD WASEEM from mailsi vehari
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Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
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Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
3. 3
A population is a group of
organisms belonging to same
species which can interbreed
or generally a group of people
under study
There is rarely enough time or
money to gather information
from everyone or everything in
a population
The purpose is to find a
representative sample (or
subset) of that population for
the study
Population and sample
4. 4
A sample is a small
portion of a population
which is selected for the
study
It is costly and time
consuming to carry out
research on the whole
population
It must be representative
of the whole population
under study
It helps us to derive
estimates and inferences
Population and sample
5. 5
Larger sample sizes are more
accurate representations of
the whole population
The goal is to
make inferences/conclusions
about a population
The sample size chosen is a
balance between obtaining a
statistically valid
representation, and the
time, energy, money, labor,
equipments and access
available
Population and sample
6. 6
A sampling frame is
complete list of all people
or organisms or items
which form the
population
All members of the
population must be
present only once in the
frame
No elements from outside
the population should be
included in the frame
Population and sample
7.
8. 8
A shortcut method for
investigating a whole
population
A process in which a
predetermined number of
observations are taken from a
larger population to estimate
features of the whole
population
Data is gathered on a small
part of the whole parent
population or sampling frame,
and used to see how the
picture look like
Sampling and sampling criteria
9. 9
List of characteristics which
are decided before which are
essential for eligibility to form
part of the sample
The purpose is to look at what
we are studying for our
research project
It should include
◦ The minimum of biasness
◦ Normal distribution of
Parent population
◦ A 95% probability or
confidence level
Sampling and sampling criteria
10. 10
In reality there is simply not
enough sources like
◦ Time
◦ Energy
◦ Money
◦ Labor/manpower
◦ Equipments
◦ Access to suitable sites to
measure every single item
Therefore an appropriate
sampling strategy is adopted
to obtain a representative,
and statistically valid sample
of the whole population
Sampling and sampling criteria
11.
12. 12
Probability sampling:
◦ Sample has a known probability
of being selected
◦ Types include simple random,
systematic, stratified, cluster,
multistage sampling
Non-probability sampling:
◦ Sample does not have known
probability of being selected as
in convenience or voluntary
response surveys
◦ Types include convenience
sampling, quota
sampling and purposive
sampling
Types of sampling
13. 13
Least biased of all
sampling techniques
Each member of the total
population has an equal
chance of being selected
It provides greatest
number of possible
samples
Applicable when
population is
• Small
• Homogeneous
• Readily available
Types of sampling
14. 14
Advantages
◦ Estimates are easy to
calculate
◦ Simple random sampling is
always an EPS design, but
not all EPS designs are
simple random sampling
Disadvantages
◦ Impracticable with large
sampling frame
◦ Minority subgroups of
interest in population may
not be present in sample in
sufficient numbers for the
study
Types of sampling
15. 15
The samples are chosen in a
systematic or regular way
◦ They are evenly distributed
in a spatial context for
example every two meters
along a transect line
◦ They can be at
equal/regular intervals in a
temporal context for
example every half hour or
at set times of the day
◦ They can be regularly
numbered for
example every 10th house
or person
Systematic sampling
16. 16
Advantages:
◦ Easy to select
◦ Suitable sampling frame can
be identified easily
◦ Samples 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
from one survey.
Types of sampling techniques
18. 18
The defining characteristic is to
get representative data from a
group
This is generally done to ensure
the inclusion of a particular
segment of the population
The proportions may or may not
differ dramatically from the
actual proportion in the
population
The researcher sets a quota,
independent of population
characteristics
It is based on the proportion of
subclasses in the population
Nonprobability Sampling
19. 19
It is sampling which is more
accessible and proximal for the
researcher
It may not be even
representative of the entire
population
It is fast, expensive and easy
method
We get the basic data,
informations and trends
Volunteers constitute a
convenience sample.
It may be biased by volunteers
Nonprobability Sampling