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Bradford Hill criteria. The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship between a presumed cause and an observed effect and have been widely used in public health research.
Retrospective cohort study. Case–control study versus cohort on a timeline. "OR" stands for "odds ratio" and "RR" stands for "relative risk". A retrospective cohort study, also called a historic cohort study, is a longitudinal cohort study used in medical and psychological research. A cohort of individuals that share a common exposure factor ...
e. A quasi-experiment is an empirical interventional study used to estimate the causal impact of an intervention on target population without random assignment. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to ...
Validity (statistics) Validity is the main extent to which a concept, conclusion, or measurement is well-founded and likely corresponds accurately to the real world. [1][2] The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool ...
Kendall rank correlation coefficient. In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient (after the Greek letter τ, tau), is a statistic used to measure the ordinal association between two measured quantities. A τ test is a non-parametric hypothesis test for statistical dependence based ...
Exploratory Factor Analysis Model. In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables. [1]
Mill's Methods. Mill's Methods are five methods of induction described by philosopher John Stuart Mill in his 1843 book A System of Logic. [1][2] They are intended to establish a causal relationship between two or more groups of data, analyzing their respective differences and similarities.
The correlation reflects the noisiness and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case, the correlation coefficient is undefined because the variance of Y is zero.