Convenience sampling, the Glossary
Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand.[1]
Table of Contents
9 relations: Bias, Data collection, Hypothesis, Nonprobability sampling, Population, Power (statistics), Questionnaire, Sampling (statistics), Sampling error.
- Sampling techniques
Bias
* Bias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is inaccurate, closed-minded, prejudicial, or unfair.
See Convenience sampling and Bias
Data collection
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.
See Convenience sampling and Data collection
Hypothesis
A hypothesis (hypotheses) is a proposed explanation for a phenomenon.
See Convenience sampling and Hypothesis
Nonprobability sampling
Nonprobability sampling is a form of sampling that does not utilise random sampling techniques where the probability of getting any particular sample may be calculated. Convenience sampling and Nonprobability sampling are sampling techniques.
See Convenience sampling and Nonprobability sampling
Population
Population is the term typically used to refer to the number of people in a single area.
See Convenience sampling and Population
Power (statistics)
In frequentist statistics, power is a measure of the ability of an experimental design and hypothesis testing setup to detect a particular effect if it is truly present.
See Convenience sampling and Power (statistics)
Questionnaire
A questionnaire is a research instrument that consists of a set of questions (or other types of prompts) for the purpose of gathering information from respondents through survey or statistical study.
See Convenience sampling and Questionnaire
Sampling (statistics)
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population.
See Convenience sampling and Sampling (statistics)
Sampling error
In statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population.
See Convenience sampling and Sampling error
See also
Sampling techniques
- Belt transect
- Bernoulli sampling
- Cluster sampling
- Consecutive sampling
- Convenience sampling
- Demon algorithm
- Distance sampling
- Event sampling methodology
- Experience sampling method
- Exponential tilting
- Gradsect
- Infrastructure bias
- Kish grid
- Latin hypercube sampling
- Line-intercept sampling
- Matching (statistics)
- Mean-field particle methods
- Metropolis-adjusted Langevin algorithm
- Monte Carlo method
- Multilevel Monte Carlo method
- Multistage sampling
- Nonprobability sampling
- Particle filter
- Passive sampling
- Poisson sampling
- Preconditioned Crank–Nicolson algorithm
- Probability-proportional-to-size sampling
- Quota sampling
- Random-sampling mechanism
- Simple random sample
- Snowball sampling
- Social polling
- Square root biased sampling
- Stratified randomization
- Stratified sampling
- Survey sampling
- Systematic sampling
References
[1] https://en.wikipedia.org/wiki/Convenience_sampling
Also known as Accidental sample, Accidental sampling, Convenience Sampling (Statistics), Convenience sample, Grab sample, Grab sampling, Opportunity sample, Opportunity sampling.