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Learn how to choose the number of observations or replicates to include in a statistical sample. Find out the factors that affect sample size, such as confidence level, margin of error, and variability, and see examples of sample size formulas for different scenarios.
Cross-sectional data is a type of data collected by observing many subjects at a single point or period of time. It differs from time series data, which observes the same subjects at various points in time, and panel data, which combines both aspects.
A cross-sectional study is an observational study that analyzes data from a population or a subset at a specific point in time. It is used to describe the prevalence of a condition, but not to establish causality or temporal order.
Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. Learn about its uses, limitations, and statistical analysis methods such as rANOVA.
Jackknife resampling is a statistical technique for bias and variance estimation based on omitting one observation at a time. Learn how to use the jackknife for mean estimation, bias correction, and variance estimation with examples and references.
A nested case–control (NCC) study is a variation of a case–control study in which cases and controls are drawn from the population in a fully enumerated cohort. [1] Usually, the exposure of interest is only measured among the cases and the selected controls. Thus the nested case–control study is more efficient than the full cohort design.
Design effect is a measure of how well a sample represents a larger group for a specific parameter. It is the ratio of the variance of an estimator based on a complex sampling design to the variance of an estimator based on simple random sampling.
Learn how to use instrumental variables (IVs) to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Find out the requirements, examples, history and theory of IV methods in statistics, econometrics and related disciplines.