"Sampling is the selection of a subset or a statistical sample of individuals from within a statistical population to estimate characteristics of the whole population."
The process of selecting participants or data points for a research study from a larger population or dataset.
Population and Sample: This topic discusses the differences between a population and a sample and how to select a representative sample from a population.
Sampling Techniques: This involves learning different methods of sampling, including random sampling, stratified sampling, and systematic sampling, and understanding when and how to use each method.
Sampling Error: This topic looks at the various types of sampling error, including selection bias, measurement error, and non-response error, and their potential impact on research findings.
Sample Size: Learning about how to determine an appropriate sample size, considering factors such as population size, variability, and confidence level, is another important topic when starting to learn about sampling.
Data Collection: This topic involves understanding the different methods of collecting data, including surveys, questionnaires, and interviews, and how to ensure the sample is representative of the population being studied.
Sampling in Qualitative Research: Sampling for qualitative research is different from quantitative research, and this topic covers the various techniques for sampling in qualitative research, such as purposeful sampling, snowball sampling, and theoretical sampling.
Data Analysis: Once data is collected, it is important to understand how to analyze it properly, which includes understanding sampling weights, sampling units, and weighting data.
Probability Sampling: This involves understanding the principles of probability sampling, such as randomization, and how to apply these principles in research studies.
Sampling Frames: Understanding sampling frames is important when selecting a sample, as it involves identifying the population being studied and the elements that make up the population.
Non-probability Sampling: Non-probability sampling is useful in situations where probability sampling is not feasible, and this topic covers various methods of non-probability sampling, such as quota sampling, purposive sampling, and convenience sampling.
Simple random sampling: Each member of the population has an equal chance of being selected.
Systematic sampling: The sample is selected by choosing every nth person from the population. For example, every 10th student in a list could be chosen.
Stratified sampling: The population is divided into homogeneous groups or strata, and then random samples are taken from each stratum proportionally. This technique is useful for ensuring that all subgroups are represented proportionally.
Cluster sampling: The population is divided into non-homogeneous clusters, and then some clusters are randomly selected for inclusion in the sample.
Convenience sampling: A sample is selected based on convenience and availability rather than any predefined criteria.
Quota sampling: A sample is selected with a specific number of participants from each subgroup to ensure representativeness.
Snowball sampling: Participants are asked to refer others to the study, which can be useful for sampling from populations that are difficult to reach otherwise.
Purposive sampling: Participants are selected based on specific criteria to obtain a sample that is representative of a specific group in the population.
Judgemental sampling: Participants are selected based on the expertise or judgement of the researcher to obtain a sample of highly relevant participants.
Multi-stage sampling: A combination of different sampling techniques is used to obtain a sample that represents the population.
"Statisticians attempt to collect samples that are representative of the population."
"Sampling has lower costs and faster data collection compared to recording data from the entire population."
"It can provide insights in cases where it is infeasible to measure an entire population."
"Each observation measures one or more properties (such as weight, location, colour, or mass) of independent objects or individuals."
"In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling."
"In business and medical research, sampling is widely used for gathering information about a population."
"Acceptance sampling is used to determine if a production lot of material meets the governing specifications."
"In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample of individuals from within a statistical population."
"The selection of a subset or a statistical sample of individuals from within a statistical population to estimate characteristics of the whole population."
"Sampling in statistics, quality assurance, and survey methodology is the selection of a subset or a statistical sample of individuals."
"Statisticians attempt to collect samples that are representative of the population."
"Sampling has lower costs and faster data collection compared to recording data from the entire population."
"Weights can be applied to the data to adjust for the sample design, particularly in stratified sampling."
"Business and medical research widely use sampling for gathering information about a population."
"Acceptance sampling is used to determine if a production lot of material meets the governing specifications."
"Results from probability theory and statistical theory are employed to guide the practice."
"Sampling is the selection of a subset or a statistical sample of individuals from within a statistical population."
"Results from probability theory and statistical theory are employed to guide the practice."
"Weights can be applied to the data to adjust for the sample design, particularly in stratified sampling."