PerformativityPerformativity is the concept that language can function as a form of social action and have the effect of change. The concept has multiple applications in diverse fields such as anthropology, social and cultural geography, economics, gender studies (social construction of gender), law, linguistics, performance studies, history, management studies and philosophy. The concept is first described by philosopher of language John L. Austin when he referred to a specific capacity: the capacity of speech and communication to act or to consummate an action.
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. 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, and thus, it can provide insights in cases where it is infeasible to measure an entire population.
Strict scrutinyIn U.S. constitutional law, when a law infringes upon a fundamental constitutional right, the court may apply the strict scrutiny standard. Strict scrutiny holds the challenged law as presumptively invalid unless the government can demonstrate that the law or regulation is necessary to achieve a "compelling state interest". The government must also demonstrate that the law is "narrowly tailored" to achieve that compelling purpose, and that it uses the "least restrictive means" to achieve that purpose.
Intermediate scrutinyIntermediate scrutiny, in U.S. constitutional law, is the second level of deciding issues using judicial review. The other levels are typically referred to as rational basis review (least rigorous) and strict scrutiny (most rigorous). In order to overcome the intermediate scrutiny test, it must be shown that the law or policy being challenged furthers an important government interest by means that are substantially related to that interest.
Stratified samplingIn statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population.
Convenience samplingConvenience 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. This type of sampling is most useful for pilot testing. Convenience sampling is not often recommended for research due to the possibility of sampling error and lack of representation of the population. But it can be handy depending on the situation. In some situations, convenience sampling is the only possible option.
Sampling errorIn statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. It can produced biased results. Since the sample does not include all members of the population, statistics of the sample (often known as estimators), such as means and quartiles, generally differ from the statistics of the entire population (known as parameters). The difference between the sample statistic and population parameter is considered the sampling error.
Nonprobability samplingSampling is the use of a subset of the population to represent the whole population or to inform about (social) processes that are meaningful beyond the particular cases, individuals or sites studied. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. In cases where external validity is not of critical importance to the study's goals or purpose, researchers might prefer to use nonprobability sampling.
Cluster samplingIn statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected. The elements in each cluster are then sampled. If all elements in each sampled cluster are sampled, then this is referred to as a "one-stage" cluster sampling plan.
