Non probability sampling
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This presentation educates you about Non-Probability Sampling, Types of non-probability sampling, When to use non-probability sampling?, Advantages of non-probability sampling and difference. For topics stay tuned with Learnbay.
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This document discusses different types of sampling methods. It explains that sampling allows researchers to study large populations in a more economical and timely manner. There are two main types of sampling: probability sampling and non-probability sampling. Non-probability sampling methods include judgment sampling, convenience sampling, quota sampling, and snowball sampling. Judgment sampling relies on a researcher's knowledge and discretion to select samples, while convenience sampling selects easily accessible samples. Quota sampling determines quotas for different population categories in advance. Snowball sampling finds additional samples through referrals from initial respondents.
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Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
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This document discusses survey research design. It defines survey research as collecting information from subjects within a population using questionnaires or interviews. Surveys can study either a sample of the population or the entire population. The document outlines different types of surveys, including descriptive surveys that describe phenomena, exploratory surveys of unknown factors, correlational surveys that study relationships between variables, and comparative surveys that compare groups. It also discusses methods of survey data collection, such as written questionnaires, oral interviews, and electronic methods like email or mobile messages.
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Student Name: Sidera Saleem
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This document discusses different types of sampling methods. It explains that sampling allows researchers to study large populations in a more economical and timely manner. There are two main types of sampling: probability sampling and non-probability sampling. Non-probability sampling methods include judgment sampling, convenience sampling, quota sampling, and snowball sampling. Judgment sampling relies on a researcher's knowledge and discretion to select samples, while convenience sampling selects easily accessible samples. Quota sampling determines quotas for different population categories in advance. Snowball sampling finds additional samples through referrals from initial respondents.
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Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
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Probability sampling
This document defines probability sampling and describes four main types: simple random sampling, stratified random sampling, systematic random sampling, and cluster random sampling. Probability sampling involves selecting samples in a way that gives every member of the population an equal and known chance of being chosen. It aims to result in a sample that accurately represents the larger population. The document provides examples of how to select samples for each of the four probability sampling techniques.
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This document provides an overview of key concepts in sampling and statistics. It defines population as the entire set of items from which a sample can be drawn. It discusses different types of sampling methods including probability sampling (simple random, stratified, cluster, systematic) and non-probability sampling (convenience, judgmental, quota, snowball). It also defines key terms like bias, precision, randomization. The document discusses the sampling process and compares advantages and disadvantages of sampling. It provides examples of calculating standard error of mean and proportion. Finally, it distinguishes between standard deviation and standard error.
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- The target population is the group to which results will be generalized.
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- The sampling frame is the list from which potential respondents are drawn.
- Probability sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select a representative sample and allow estimates of sampling error. Non-probability methods do not involve random selection.
Nonprobability Sampling
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Sample design
This document discusses various sampling designs and their characteristics. It describes probability sampling designs like simple random sampling which gives every unit an equal chance of selection. It also describes non-probability sampling designs like purposive sampling which involves deliberately choosing units. Specific probability designs discussed include systematic sampling, stratified sampling, cluster sampling, area sampling, and multi-stage sampling.
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Non-probability sampling is a type of sampling where samples are gathered in a way that does not give all individuals in the population an equal chance of being selected. It is often used when random sampling is impossible due to large population sizes or limited resources. Some common types of non-probability sampling include convenience sampling, quota sampling, snowball sampling, and purposive sampling. While non-probability sampling is less costly and easier than probability sampling, the results cannot be generalized to the larger population due to potential sampling biases.
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This document provides an overview of key concepts in sampling and statistics. It defines population as the entire set of items from which a sample can be drawn. It discusses different types of sampling methods including probability sampling (simple random, stratified, cluster, systematic) and non-probability sampling (convenience, judgmental, quota, snowball). It also defines key terms like bias, precision, randomization. The document discusses the sampling process and compares advantages and disadvantages of sampling. It provides examples of calculating standard error of mean and proportion. Finally, it distinguishes between standard deviation and standard error.
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The document discusses different types of sampling designs used in research. It describes probability sampling methods like simple random sampling and systematic sampling which allow every unit in the population to have a chance of being selected. It also covers non-probability sampling which does not assure equal chance of selection. Key factors in sampling like sample size, target population, and parameters of interest are explained.
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This document discusses sampling methods and their key aspects. It defines sampling as selecting a subset of individuals from a population to make inferences about the whole population. Probability sampling methods aim to give all population elements an equal chance of selection, while non-probability methods do not. Some common probability methods described include simple random sampling, systematic sampling, and stratified sampling. The document also discusses sampling frames, statistics versus parameters, confidence levels, and evaluating different sampling techniques.
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The document discusses key concepts in sampling, including:
- The target population is the group to which results will be generalized.
- Sampling units are the smallest elements that can be selected from the sampling frame.
- The sampling frame is the list from which potential respondents are drawn.
- Probability sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select a representative sample and allow estimates of sampling error. Non-probability methods do not involve random selection.
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This document discusses non-probability sampling techniques. It defines key terms like population, sample, probability sampling, and non-probability sampling. It then describes several common non-probability sampling methods: convenience sampling, which uses readily available participants; snowball sampling, which uses referrals from initial participants to recruit more; purposive sampling, which selects participants based on predefined criteria; and quota sampling, which sets quotas for participant demographics to be filled. The document notes advantages and disadvantages of these non-probability methods.
Research tools & data collection method_vipin
data collection method-
it include following sub points-
1) definition of research tool
2) data
3) primary and secondary data
4) observation method
5) interview
6) questionnaire
7) physiological measure
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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.
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Stratified sampling is a technique where the population is divided into subgroups or strata, and then a random sample is selected proportionally from each strata. This ensures adequate representation of specific subgroups of interest. There are two main types: proportional, where each strata is sampled at the same rate relative to its population size, and disproportionate, where strata can be sampled at different rates. Stratified sampling provides benefits like more accurate estimates for different population strata and improved overall representativeness of the sample. However, it also has disadvantages like difficulty defining strata and more complex analysis compared to simple random sampling.
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QUASI EXPERIMENTAL RESEARCH DESIGN
Quasi-experimental research designs involve manipulating an independent variable to observe its effects on a dependent variable, unlike true experiments which require random assignment and control groups. Quasi-experiments lack one of these characteristics. There are two main types: non-randomized control group designs where experimental and control groups are not randomly assigned, and time series designs which measure the effects of a treatment over multiple time periods with an individual or small group. Quasi-experiments are more practical than true experiments but less reliable for establishing causation due to lack of control over extraneous variables.
Sampling techniques
The USFWS wanted to understand purchasers of duck stamps to improve marketing. The target population was households in the US. A sampling frame of working US telephone numbers was used. Simple random sampling with modifications to exclude non-households was conducted to generate a sample of 1,000 telephone numbers. Focus groups and a telephone survey were then administered to collect data to answer the research questions.
Sample design
This document discusses various sampling designs and their characteristics. It describes probability sampling designs like simple random sampling which gives every unit an equal chance of selection. It also describes non-probability sampling designs like purposive sampling which involves deliberately choosing units. Specific probability designs discussed include systematic sampling, stratified sampling, cluster sampling, area sampling, and multi-stage sampling.
Selecting a sample
This document discusses key concepts in quantitative research methods including research, samples, populations, random and non-random sampling techniques. It defines research as a careful investigation to discover new facts or interpret existing facts. A sample is a subset of a population used to gain insights about the whole. Random sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select representative samples, while non-random methods like convenience and purposive sampling are not generalizable. The document also discusses qualitative research and purposive sampling techniques.
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Non-probabilistic sampling is any sampling method where some population elements have no chance of selection or where the probability of selection cannot be accurately determined. It involves selecting elements based on assumptions about the population rather than randomly. Common non-probabilistic sampling methods include convenience sampling, snowball sampling, purposive sampling, and quota sampling. While cheaper and easier than probabilistic sampling, non-probabilistic sampling cannot estimate sampling errors and results may be biased since not all population elements have an equal chance of selection.
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This document discusses different sampling methods used in statistics. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection so every member of the population has a chance of being selected, including simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Non-probability sampling does not use random selection and includes convenience sampling, quota sampling, purposive sampling, and snowball sampling. The document provides details on each of these sampling techniques.
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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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Prof. Shriram Kargaonkar (Asst. Prof . & HOD Statistics Department) explained the concept of Population and Sample in the very simple way. (YouTube Video Link - https://youtu.be/r0ovqPw7624?t=18
Email- snkargaonkar@mitacsc.ac.in
LIKE SHARE & SUBSCRIBE YT Channel - https://www.youtube.com/channel/UC9QIMhC3OioapZDXgsPvnAw
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This document discusses key concepts in sampling including:
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- 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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The document discusses different sampling methods used in market research. It describes probability sampling as selecting members randomly where all have an equal chance of selection. This reduces bias. Non-probability sampling does not have a fixed selection process and some members may be less likely to be selected. Examples of probability methods include simple random sampling and cluster sampling. Non-probability methods include convenience sampling, snowball sampling, and quota sampling. Probability sampling aims to accurately represent a population while non-probability sampling is used in exploratory research when time or budget is limited.
Chapter 6 Selecting a Sample
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.
SAMPLING METHODS &TYPES, & TECHNIQUES & EXAMPLES.pptx
Sampling methods are divided into probability sampling and non-probability sampling. Probability sampling uses random selection so that every member of the population has an equal chance of being selected, allowing statistical inferences about the whole population. Common probability methods include simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Non-probability sampling does not use random selection and statistical inferences cannot be made, but it is used when random selection is not feasible. Common non-probability methods include quota sampling, snowball sampling, and discretionary sampling. The choice of sampling method depends on the characteristics of the population and the goals of the research.
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SAMPLING METHODS &TYPES, & TECHNIQUES & EXAMPLES.pptx
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Non probability sampling
- 2. Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research. Non-Probability sampling
- 3. Non-probability sampling is a sampling method in which not all members of the population have an equal chance of participating in the study, unlike probability sampling. Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations.
- 4. Convenience sampling Consecutive sampling Quota sampling Judgmental or Purposive sampling Snowball sampling Types of non-probability sampling
- 5. Use this type of sampling to indicate if a particular trait or characteristic exists in a population. Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research. Researchers use it when they have limited time to conduct research or have budget constraints. When the researcher needs to observe whether a particular issue needs in-depth analysis, he applies this method. Use it when you do not intend to generate results that will generalize the entire population. When to use non-probability sampling?
- 6. Non-probability sampling techniques are a more conducive and practical method for researchers deploying surveys in the real world. Although statisticians prefer probability sampling because it yields data in the form of numbers, however, if done correctly, it can produce similar if not the same quality of results. Advantages of non-probability sampling
- 7. Getting responses using non-probability sampling is faster and more cost-effective than probability sampling because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.
- 8. Probability sampling The sample is selected at random. Everyone in the population has an equal chance of getting selected. Used when sampling bias has to be reduced. Useful when the population is diverse. Used to create an accurate sample. Finding the right respondents is not easy. Difference
- 9. Non-probability sampling Sample selection based on the subjective judgment of the researcher. Not everyone has an equal chance to participate. The researcher does not consider sampling bias. Useful when the population has similar traits. The sample does not accurately represent the population. Finding respondents is easy. Difference
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