Sampling 090622104926-phpapp01
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This document discusses sampling methods for collecting data. It outlines two main methods: census and sampling. The census method collects data from every unit in the population but is costly and time-consuming. The sampling method collects data from a subset of the population and inferences can be made about the whole population. There are random sampling methods like simple random sampling and stratified sampling that allow statistical analysis, as well as non-random methods like judgment sampling. Sampling provides benefits like lower cost and time compared to a census, but must be carefully designed to avoid bias.
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Sampling
This document discusses sampling methods for collecting data compared to a complete census. It defines sampling as studying a part of the population to make conclusions about the whole, while a census collects data from every single unit. The key sampling methods described are random sampling, which allows statistical analysis, and non-random sampling, which does not. Random sampling techniques include simple random sampling, stratified sampling, systematic sampling, and multi-stage sampling. Non-random techniques include judgment sampling, quota sampling, and convenience sampling. The document outlines some advantages and limitations of using sampling versus a complete census.
Sample design
This document discusses different aspects of sampling design including probability and non-probability sampling methods, criteria for selecting sampling procedures such as inappropriate sampling frames and non-respondents, implications for sample design like sample size and reducing error, steps in the sampling design process, and characteristics of a good sample design such as being representative and minimizing bias and error.
Sampling
This document discusses different sampling methods and terms. It defines key concepts like population, sample, element, and provides characteristics of a good sample. It outlines advantages and limitations of sampling and categorizes sampling methods into probability and non-probability. Specific probability methods like simple random sampling, stratified sampling, and systematic sampling are described in detail along with their procedures and advantages/disadvantages. Non-probability methods like quota sampling, accidental sampling and judgmental sampling are also briefly introduced.
Characteristics of a good sample design & types of sample design
The document discusses different types of sample designs, including their key characteristics and differences. It covers non-probability sampling designs like purposive sampling which rely on researcher judgement, and probability sampling designs like simple random sampling where every item has an equal chance of selection. Probability sampling is preferred because it allows estimating sampling errors and significance of results.
Sample designing
This document summarizes a presentation on sample designing for research methodology. It defines sampling as selecting a subset of a population to study so results can be generalized to the population. Reasons for sampling include lower costs, less errors and fatigue, and less time needed compared to studying the entire population. The document discusses factors to consider for sample size like the population, elements, frame, sample, unit, and subject. It also outlines probability sampling methods like simple random, systematic, stratified, and cluster sampling and non-probability methods. Common sampling errors are identified as well.
Sampling
This document discusses key concepts related to sampling, including defining a population and sample. It notes that sampling allows for convenient and economical study of large populations, and can increase the speed, accuracy, and rapport of data collection if done properly. However, sampling also carries risks of bias and requires expertise. The document outlines criteria for good sampling, such as the sample being representative and an appropriate size. It also discusses sampling error and how sample size and population variability can affect statistical inference drawn from samples.
Importance of Sampling design & Sample size
The document discusses the importance and benefits of sampling over a census for research purposes. It notes that sampling saves time and money compared to a census. Additionally, a sample may be more accurate than a census due to limitations in resources and risks of introducing unpopular actions to an entire market. The document also emphasizes that both sampling design and sample size are important considerations, as an inappropriate design will not allow findings to be generalized even with a large sample, and an inadequate sample size cannot meet study objectives. It provides some general guidelines for appropriate sample sizes.
Sampling in Market Research
1) The document discusses sampling in marketing research, which involves selecting a subset of individuals from a population to estimate characteristics of the whole population. It describes key concepts like sampling elements, population, sampling frame, and different sampling techniques.
2) It explains the advantages of sampling such as being economical, faster to execute, and able to provide comprehensive information. The sampling design process involves defining the target population, determining the sampling frame, selecting a technique, determining sample size, and executing the process.
3) Different sampling techniques are described such as probability and non-probability sampling. Types of errors in sampling like sampling error and non-sampling error are also outlined. Examples of sampling methods for different contexts like telephone surveys
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Sampling
This document discusses sampling methods for collecting data compared to a complete census. It defines sampling as studying a part of the population to make conclusions about the whole, while a census collects data from every single unit. The key sampling methods described are random sampling, which allows statistical analysis, and non-random sampling, which does not. Random sampling techniques include simple random sampling, stratified sampling, systematic sampling, and multi-stage sampling. Non-random techniques include judgment sampling, quota sampling, and convenience sampling. The document outlines some advantages and limitations of using sampling versus a complete census.
Sample design
This document discusses different aspects of sampling design including probability and non-probability sampling methods, criteria for selecting sampling procedures such as inappropriate sampling frames and non-respondents, implications for sample design like sample size and reducing error, steps in the sampling design process, and characteristics of a good sample design such as being representative and minimizing bias and error.
Sampling
This document discusses different sampling methods and terms. It defines key concepts like population, sample, element, and provides characteristics of a good sample. It outlines advantages and limitations of sampling and categorizes sampling methods into probability and non-probability. Specific probability methods like simple random sampling, stratified sampling, and systematic sampling are described in detail along with their procedures and advantages/disadvantages. Non-probability methods like quota sampling, accidental sampling and judgmental sampling are also briefly introduced.
Characteristics of a good sample design & types of sample design
The document discusses different types of sample designs, including their key characteristics and differences. It covers non-probability sampling designs like purposive sampling which rely on researcher judgement, and probability sampling designs like simple random sampling where every item has an equal chance of selection. Probability sampling is preferred because it allows estimating sampling errors and significance of results.
Sample designing
This document summarizes a presentation on sample designing for research methodology. It defines sampling as selecting a subset of a population to study so results can be generalized to the population. Reasons for sampling include lower costs, less errors and fatigue, and less time needed compared to studying the entire population. The document discusses factors to consider for sample size like the population, elements, frame, sample, unit, and subject. It also outlines probability sampling methods like simple random, systematic, stratified, and cluster sampling and non-probability methods. Common sampling errors are identified as well.
Sampling
This document discusses key concepts related to sampling, including defining a population and sample. It notes that sampling allows for convenient and economical study of large populations, and can increase the speed, accuracy, and rapport of data collection if done properly. However, sampling also carries risks of bias and requires expertise. The document outlines criteria for good sampling, such as the sample being representative and an appropriate size. It also discusses sampling error and how sample size and population variability can affect statistical inference drawn from samples.
Importance of Sampling design & Sample size
The document discusses the importance and benefits of sampling over a census for research purposes. It notes that sampling saves time and money compared to a census. Additionally, a sample may be more accurate than a census due to limitations in resources and risks of introducing unpopular actions to an entire market. The document also emphasizes that both sampling design and sample size are important considerations, as an inappropriate design will not allow findings to be generalized even with a large sample, and an inadequate sample size cannot meet study objectives. It provides some general guidelines for appropriate sample sizes.
Sampling in Market Research
1) The document discusses sampling in marketing research, which involves selecting a subset of individuals from a population to estimate characteristics of the whole population. It describes key concepts like sampling elements, population, sampling frame, and different sampling techniques.
2) It explains the advantages of sampling such as being economical, faster to execute, and able to provide comprehensive information. The sampling design process involves defining the target population, determining the sampling frame, selecting a technique, determining sample size, and executing the process.
3) Different sampling techniques are described such as probability and non-probability sampling. Types of errors in sampling like sampling error and non-sampling error are also outlined. Examples of sampling methods for different contexts like telephone surveys
Problems of sampling
Sampling errors occur when a sample is not truly representative of the overall population being studied. Sampling errors are more likely when samples are small, heterogeneous populations are studied without accounting for differences, or the sampling process is not well planned or executed. Factors that can contribute to sampling errors include a lack of knowledge about appropriate sampling methods, insufficient resources, lack of participation from subjects, and not having existing sampling frames for large populations. Researchers should take care to select samples that are representative of populations and employ sampling techniques appropriately based on the phenomenon being studied.
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This document discusses sample design and the steps involved in determining an appropriate sample. It defines key terms like population, sample, sampling frame, and outlines different sampling techniques. It emphasizes the importance of sample size and how to calculate it using confidence intervals in order to achieve the desired level of accuracy and confidence in results. Sources of error like sampling error and non-sampling error are also explained.
Non sampling error
Non-sampling errors occur in surveys and censuses due to factors other than sampling and can happen at various stages:
- Specification errors occur during planning due to issues like incomplete population coverage or ambiguous questions.
- Ascertainment errors happen during data collection due to inaccurate recording or ambiguous instructions.
- Tabulation errors take place during analysis through mistakes in coding, analysis or presentation of results.
Some common sources of non-sampling errors include lack of proper planning, incomplete or inaccurate responses, ambiguous definitions, and errors in data processing. Measures like pre-testing questionnaires, hiring experienced staff, and cross-checking data can help reduce non-sampling errors.
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This document discusses different sampling techniques that can be used to analyze large datasets. It defines key sampling concepts like the target population, sampling frame, and sampling unit. Probability sampling techniques described include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and probability proportional to size sampling. Non-probability sampling techniques include convenience sampling and purposive sampling. The document also covers how to calculate sample sizes needed to estimate proportions and means within a desired level of accuracy. Stratified sampling can help reduce variability and improve efficiency by dividing the population into relevant subgroups.
Non Probability Sampling
Non-probability sampling techniques are sampling methods where the probability of selecting members from a population cannot be calculated. In non-probability sampling, selection is non-random and relies on the researcher's judgment. While being cheaper and easier than probability sampling, it cannot accurately represent a population or calculate errors. Common non-probability sampling types include convenience, purposive, expert, and snowball sampling, which select samples based on accessibility, researcher knowledge, or recruitment within social networks.
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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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The document defines sampling and the different types of sampling methods. It states that sampling is the process of selecting a subset of individuals from a population to estimate characteristics of the whole population. There are two main types of sampling: probability sampling, where every individual has an equal chance of being selected, and non-probability sampling, where some individuals may have no chance of selection. Some examples of probability sampling methods provided are random sampling, stratified sampling, and systematic sampling. Examples of non-probability sampling include convenience sampling, purposive sampling, quota sampling, and snowball sampling.
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This document provides an introduction to simulation. It defines simulation as modeling a real system and experimenting with that model to understand the system's behavior or evaluate different operational strategies. The document discusses how simulation allows modeling complex systems in detail and making predictions. It provides examples of simulation applications in computer systems, manufacturing, business, and government for purposes like hardware/software testing, production planning, financial analysis, and military planning. Advantages of simulation are that it can study existing systems without disruption and test proposed systems beforehand. Drawbacks include difficulty interpreting results and high time/costs compared to analytical methods.
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This document discusses sampling and provides definitions of key sampling terms. It describes the difference between probability and non-probability sampling. Some key sampling designs are simple random sampling, stratified sampling, cluster sampling, systematic sampling, convenience sampling, and quota sampling. The document also outlines the sampling process and discusses advantages and disadvantages of sampling.
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This document discusses sampling and different sampling methods. It defines key concepts like census, sampling, population, sampling frame, and sampling unit. It explains the sampling process which includes defining the population, identifying the sampling frame, specifying the sampling unit, selecting a sampling method, determining sample size, and selecting the sample. It also discusses different types of sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and non-probability sampling methods. Finally, it covers potential sources of error in sampling and key statistical concepts.
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This document discusses key factors to consider when determining sample size for research. It explains that sample size depends on population size, required confidence level, expected response rate, and other variables. Larger samples are needed when variables are numerous, differences are expected to be small, or subgroups will be analyzed. The standard error of the sample is used to calculate sampling error, which decreases as sample size increases. Both probability and non-probability sampling strategies are outlined. The document provides guidance on planning a sampling strategy that considers research questions, population characteristics, and feasibility of access to samples.
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This document discusses key concepts related to sampling plans, including different sampling methods and terminology. It begins by outlining the sampling design process and factors that influence sample selection. Probability and non-probability sampling methods are then classified and described, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling, convenience sampling, purposive sampling, quota sampling, and snowball sampling. Considerations like sample size calculation and sources of error in sampling are also covered. The document provides an overview of essential information for developing a sampling plan.
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This document discusses determining appropriate sample sizes in survey research. It provides formulas and procedures for calculating sample sizes for continuous and categorical variables. For continuous variables, the Cochran formula is presented which considers the acceptable margin of error, alpha level, estimated variance, and population size. For categorical variables, similar considerations are made but typically result in larger sample sizes. The document emphasizes accurately estimating variables like variance and anticipated response rates to determine an appropriate oversampling size to achieve the target minimum sample.
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This document discusses non-sampling error in surveys. It notes that non-sampling error occurs for reasons other than the sampling technique and can happen at every stage of a survey. There are two main types of non-sampling error: non-observation error, which includes non-coverage and non-response; and measurement error, which is when a respondent's answer differs from the true value. The document outlines various causes of non-sampling error and methods to reduce errors, such as improved training and monitoring of interviewers.
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This document discusses various types of errors that can occur in sampling techniques, including sampling errors and non-sampling errors. It defines sampling errors as errors that arise from using a sample rather than the entire population. Non-sampling errors occur due to issues in data collection, processing, and analysis. Some key points made are that sampling error decreases with larger sample sizes, while non-sampling error does not necessarily decrease, and that important surveys conducted in Bangladesh include demographic, health, and nutrition surveys.
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This document summarizes probability and non-probability sampling methods. Probability sampling assigns all population members an equal chance of selection, allowing for random selection techniques like simple random sampling. Non-probability sampling does not give all members an equal chance, relying instead on subjective judgment in techniques like convenience sampling. The key differences are that probability sampling is random, unbiased, and allows for statistical inferences, while non-probability sampling is non-random, potentially biased, and only permits analytical conclusions.
Sampling errors 8-12-2014
Sampling errors occur when using a sample to make inferences about a population. There are two main types of sampling errors - random sampling error and bias sampling error. Random sampling error is caused by chance fluctuations in who is selected in the sample and usually balances out, while bias sampling error results from flaws in the sampling design or implementation and does not balance out. Some factors that influence the size of sampling errors are the sample size, with larger samples having smaller errors, and the heterogeneity of the population. Non-sampling errors also exist, such as errors in defining the population, sampling methodology, non-responses, and measurement errors.
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This document discusses different sampling methods used in research. It begins with defining sampling as selecting a representative part of the population to determine characteristics of the whole. The sampling process involves defining the population, selecting a sampling method, and determining sample size. Probability sampling methods like random, stratified, cluster and systematic sampling aim to give all units an equal chance of being selected. Non-probability methods like convenience, judgmental, snowball and quota sampling do not use chance and focus on easily available units. The document provides details on each sampling method and their advantages and disadvantages.
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Sampling errors occur when a sample is not truly representative of the overall population being studied. Sampling errors are more likely when samples are small, heterogeneous populations are studied without accounting for differences, or the sampling process is not well planned or executed. Factors that can contribute to sampling errors include a lack of knowledge about appropriate sampling methods, insufficient resources, lack of participation from subjects, and not having existing sampling frames for large populations. Researchers should take care to select samples that are representative of populations and employ sampling techniques appropriately based on the phenomenon being studied.
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This document discusses sample design and the steps involved in determining an appropriate sample. It defines key terms like population, sample, sampling frame, and outlines different sampling techniques. It emphasizes the importance of sample size and how to calculate it using confidence intervals in order to achieve the desired level of accuracy and confidence in results. Sources of error like sampling error and non-sampling error are also explained.
Non sampling error
Non-sampling errors occur in surveys and censuses due to factors other than sampling and can happen at various stages:
- Specification errors occur during planning due to issues like incomplete population coverage or ambiguous questions.
- Ascertainment errors happen during data collection due to inaccurate recording or ambiguous instructions.
- Tabulation errors take place during analysis through mistakes in coding, analysis or presentation of results.
Some common sources of non-sampling errors include lack of proper planning, incomplete or inaccurate responses, ambiguous definitions, and errors in data processing. Measures like pre-testing questionnaires, hiring experienced staff, and cross-checking data can help reduce non-sampling errors.
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This document discusses different sampling techniques that can be used to analyze large datasets. It defines key sampling concepts like the target population, sampling frame, and sampling unit. Probability sampling techniques described include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and probability proportional to size sampling. Non-probability sampling techniques include convenience sampling and purposive sampling. The document also covers how to calculate sample sizes needed to estimate proportions and means within a desired level of accuracy. Stratified sampling can help reduce variability and improve efficiency by dividing the population into relevant subgroups.
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Non-probability sampling techniques are sampling methods where the probability of selecting members from a population cannot be calculated. In non-probability sampling, selection is non-random and relies on the researcher's judgment. While being cheaper and easier than probability sampling, it cannot accurately represent a population or calculate errors. Common non-probability sampling types include convenience, purposive, expert, and snowball sampling, which select samples based on accessibility, researcher knowledge, or recruitment within social networks.
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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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The document defines sampling and the different types of sampling methods. It states that sampling is the process of selecting a subset of individuals from a population to estimate characteristics of the whole population. There are two main types of sampling: probability sampling, where every individual has an equal chance of being selected, and non-probability sampling, where some individuals may have no chance of selection. Some examples of probability sampling methods provided are random sampling, stratified sampling, and systematic sampling. Examples of non-probability sampling include convenience sampling, purposive sampling, quota sampling, and snowball sampling.
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This document discusses sampling and different sampling methods. It defines key concepts like census, sampling, population, sampling frame, and sampling unit. It explains the sampling process which includes defining the population, identifying the sampling frame, specifying the sampling unit, selecting a sampling method, determining sample size, and selecting the sample. It also discusses different types of sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and non-probability sampling methods. Finally, it covers potential sources of error in sampling and key statistical concepts.
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This document discusses key factors to consider when determining sample size for research. It explains that sample size depends on population size, required confidence level, expected response rate, and other variables. Larger samples are needed when variables are numerous, differences are expected to be small, or subgroups will be analyzed. The standard error of the sample is used to calculate sampling error, which decreases as sample size increases. Both probability and non-probability sampling strategies are outlined. The document provides guidance on planning a sampling strategy that considers research questions, population characteristics, and feasibility of access to samples.
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This document discusses key concepts related to sampling plans, including different sampling methods and terminology. It begins by outlining the sampling design process and factors that influence sample selection. Probability and non-probability sampling methods are then classified and described, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling, convenience sampling, purposive sampling, quota sampling, and snowball sampling. Considerations like sample size calculation and sources of error in sampling are also covered. The document provides an overview of essential information for developing a sampling plan.
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This document discusses determining appropriate sample sizes in survey research. It provides formulas and procedures for calculating sample sizes for continuous and categorical variables. For continuous variables, the Cochran formula is presented which considers the acceptable margin of error, alpha level, estimated variance, and population size. For categorical variables, similar considerations are made but typically result in larger sample sizes. The document emphasizes accurately estimating variables like variance and anticipated response rates to determine an appropriate oversampling size to achieve the target minimum sample.
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This document discusses non-sampling error in surveys. It notes that non-sampling error occurs for reasons other than the sampling technique and can happen at every stage of a survey. There are two main types of non-sampling error: non-observation error, which includes non-coverage and non-response; and measurement error, which is when a respondent's answer differs from the true value. The document outlines various causes of non-sampling error and methods to reduce errors, such as improved training and monitoring of interviewers.
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This document discusses various types of errors that can occur in sampling techniques, including sampling errors and non-sampling errors. It defines sampling errors as errors that arise from using a sample rather than the entire population. Non-sampling errors occur due to issues in data collection, processing, and analysis. Some key points made are that sampling error decreases with larger sample sizes, while non-sampling error does not necessarily decrease, and that important surveys conducted in Bangladesh include demographic, health, and nutrition surveys.
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The document discusses different sampling methods for surveys and research. It describes census as a complete enumeration of all items in a population, while sample survey involves selecting a representative sample. Probability sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select samples in a way that each item has an equal chance of selection and sampling errors can be estimated. Non-probability methods like purposive sampling involve deliberately choosing items without randomization. The key factors in sampling design are representation, sample size, and selection technique to balance accuracy and costs.
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This document summarizes probability and non-probability sampling methods. Probability sampling assigns all population members an equal chance of selection, allowing for random selection techniques like simple random sampling. Non-probability sampling does not give all members an equal chance, relying instead on subjective judgment in techniques like convenience sampling. The key differences are that probability sampling is random, unbiased, and allows for statistical inferences, while non-probability sampling is non-random, potentially biased, and only permits analytical conclusions.
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Sampling errors occur when using a sample to make inferences about a population. There are two main types of sampling errors - random sampling error and bias sampling error. Random sampling error is caused by chance fluctuations in who is selected in the sample and usually balances out, while bias sampling error results from flaws in the sampling design or implementation and does not balance out. Some factors that influence the size of sampling errors are the sample size, with larger samples having smaller errors, and the heterogeneity of the population. Non-sampling errors also exist, such as errors in defining the population, sampling methodology, non-responses, and measurement errors.
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This document provides an overview and summaries of chapters from a study guide on sampling and survey research methods. It discusses key concepts in probability sampling, including simple random sampling, stratified sampling, and cluster sampling. It also covers survey research methods, describing how surveys can be administered through questionnaires, interviews, or computers. Additional chapters summarize qualitative interviewing techniques, field observation methods, and other approaches to collecting data through questions. The document aims to help students understand different research designs and data collection strategies.
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Data analysis involves inspecting, cleansing, transforming, and modeling data to enhance productivity and business growth. It refers to techniques used to analyze data to derive insights, generate reports, perform market analysis, and improve business strategies. Common data analysis tools include Tableau, Power BI, R, Python, and Apache Spark. Decision science uses quantitative techniques like decision analysis, risk analysis, and simulation modeling to inform decision-making. It is part of fields like operations research, microeconomics, and computer science.
Sampling methods in medical research
1) The document discusses various sampling methods used in medical research including simple random sampling, stratified sampling, and cluster sampling.
2) It explains the need for sampling over a complete census due to advantages like lower cost, ability to estimate errors, and feasibility for large populations.
3) Key concepts discussed include sampling units, frames, parameters, statistics, and different types of errors associated with sampling.
Sampling and types of techniques
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
26738157 sampling-design
The document discusses different sampling techniques used in research. It describes probability sampling methods like simple random sampling, systematic sampling, stratified random sampling, and multistage cluster sampling which ensure that each population element has a known chance of selection. It also covers non-probability sampling which uses arbitrary selection. Key advantages of probability sampling include controlling for bias and representing the population, while non-probability sampling has lower costs. Sample size is based on desired precision, population variability, and confidence level.
Understanding Sampling Methods, Simple Random Sampling & Judgment Sampling
This PowerPoint presentation provides a comprehensive overview of two prominent sampling techniques used in research: Simple Random Sampling (SRS) and Judgment Sampling. It aims to educate the audience about the principles, applications, advantages, and limitations of these sampling methods.
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This document discusses key concepts in sampling including:
- Probability and non-probability sampling designs such as simple random sampling, stratified random sampling, cluster sampling, and convenience sampling.
- Determining appropriate sample size based on desired precision, confidence level, and population variability.
- The differences between sampling error and non-sampling error, where sampling error is due to an unrepresentative sample and non-sampling error is from other issues like poor question design.
- Advantages of probability sampling include being cost-effective, simple, and non-technical compared to non-probability sampling which is faster and cheaper but provides less accuracy.
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Sampling techniques Systematic & Purposive sampling
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Sampling 090622104926-phpapp01
- 2. INTRODUTION The need for adequate and reliable data is ever increasing for taking wise decisions in different fields of human activity and business is no exception to it. Complete enumeration survey or census method Sampling method
- 3. CENSUS METHOD Data are collected for each and every unit belonging to the population or universe which is the complete set of items which are of interest in any particular situation . Advantages of census method are:- Information can be obtained for each and every unit. Greater Accuracy.
- 4. SAMPLING METHOD In the sampling method instead of every unit of the population only a part of the population is studied and the conclusions are drawn on that basis for the entire population. Purpose of sampling :-sampling is only a tool which help to know the characteristics of the universe or population by examining only a small part of it. The value obtain from the study of sample ,such as the average and variance ,are known as ‘statistic’. Such value for the population are called ‘parameters’.
- 5. PRINCIPLES OF SAMPLING 1. Principle of ‘Statistical Regularity’ ,and 2. Principle of ‘Inertia of Large Numbers’.
- 6. METHOD OF SAMPLING when a sample is required to be reflected from a population ,it is necessary to decide which method should be applied . The various method of sampling or sampling design can be grouped under the head as random sampling and non- sampling . Random sampling referred to as probability sampling since if the sampling process is random ,the law of probability can be applied ;thus the pattern of sampling distribution needed to interpret and evaluate a sample is provided. Non –random sampling is selected on basis other than probability considerations such as expert judgment ,convenience or some critria . The most important aspect of non –sampling worth nothing is that its subject to sampling variability but there is no way of knowing the pattern of variability in the process.
- 7. A) Random sampling method: (i)Simple Random Sampling (ii)Stratified Sampling (iii)Systematic Sampling (iv)Multi-stage Sampling B)Non-random sampling method: (i) Judgment Sampling (ii)Quota Sampling (iii)Convenience Sampling.
- 8. MERITS OF SAMPLING METHOD Less time Less cost More reliable results More detailed information The destructive nature of certain tests
- 9. LIMITATIONS OF SAMPLING Must be carefully planned and executed The service of experts At times the Sampling plan may be so complicated that requires more time, labour and money than a complete count. If the information is required for each and every unit in the domain of study, a complete enumeration survey is necessary.