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Simple mediation model. The independent variable causes the mediator variable; the mediator variable causes the dependent variable. In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator ...
These data sources include interview transcripts, videos of social interactions, notes, verbal reports [8] and artifacts such as books or works of art. The case study method exemplifies qualitative researchers' preference for depth, detail, and context. [11] [12] Data triangulation is also a strategy used in qualitative research. [13]
Designing Social Inquiry: Scientific Inference in Qualitative Research (or KKV) is an influential 1994 book written by Gary King, Robert Keohane, and Sidney Verba that lays out guidelines for conducting qualitative research. [1]
In mathematics, a function is a rule for taking an input (in the simplest case, a number or set of numbers) [5] and providing an output (which may also be a number). [5] A symbol that stands for an arbitrary input is called an independent variable, while a symbol that stands for an arbitrary output is called a dependent variable. [6]
Since U is unobserved, the requirement that Z be independent of U cannot be inferred from data and must instead be determined from the model structure, i.e., the data-generating process. Causal graphs are a representation of this structure, and the graphical definition given above can be used to quickly determine whether a variable Z qualifies ...
In statistics there is a synonym for intervening variable - "mediator variable". See Mediation (statistics). It seems that some crossreferences or merging needed. —Preceding unsigned comment added by 90.189.181.244 17:38, 5 March 2011 (UTC)
Other latent variables correspond to abstract concepts, like categories, behavioral or mental states, or data structures. The terms hypothetical variables or hypothetical constructs may be used in these situations. The use of latent variables can serve to reduce the dimensionality of data. Many observable variables can be aggregated in a model ...
In causal models, controlling for a variable means binning data according to measured values of the variable. This is typically done so that the variable can no longer act as a confounder in, for example, an observational study or experiment.