random effects model


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random effects model

A statistical model that may be used in meta-analysis, in which both within-study sampling error (variance) and between-studies variation are included in assessing the uncertainty or confidence interval of the results of the meta-analysis.
References in periodicals archive ?
For the random effects model, the [[alpha].sub.i]'s, [[beta].sub.j]'s, [[gamma].sub.ij]'s, and [[member of].sub.ijk]'s are mutually independent, the [[alpha].sub.i]'s are iid N(0,[[sigma].sup.2.sub.[alpha]]), the [[beta].sub.j]'s are iid N(0, [[sigma].sup.2.sub.[beta]]),the [[gamma].sub.ij]'s are iid N(0, [[sigma].sup.2.sub.[gamma]]), and the [[member of].sub.ijk]'s are iid N(0, [[sigma].sup.2.sub.[member of]]).
However, most previous studies only used a random effects model which does not control such bias.
Table 6: Odds Ratio, by Sample Size (Random Effects Model) N with Event / Total N (%) Study Intervention Control Suarez et al.
In order to further investigate about whether fixed effects model or random effects model is more useful, so called Hausman test is used.
As a multilevel modeling technique, the mixed random effects model offers a number of advantages over standard fixed effects specifications and is particularly attractive for the objectives of this analysis.
(1993, 1996) and Cornelius and Crossa (1995, 1999), herein named CCC method, were constructed by analogy to shrinkage factors involved in empirical BLUPs in a two-way random effects model with interaction.
As it is necessary to control for firm-specific variation in the profit rate (Greene, 1999; Hsiao, 1986), two options are available: using a fixed effects or a random effects model. Both models allow the researcher to account for unobservable factors (such as corporate culture, the nature of the industry, market structure and demand for product) which contribute to differences in the profit rate between firms.
Three different panel data estimating techniques are applied, including a random effects model that is distinctive in allowing for correlation between hospital effects and observable regressors, circumventing inconsistency problems following from standard generalized least-squares estimations.
The choice of the fixed effects versus random effects model depends on the context of the data.
The random effects model includes the same set of independent variables as the weighted least squares model but in addition accounts for state-specific effects.
Results using the random effects model are presented in Section IV, followed by the conclusion.
The selection correction utilizes the methodology of Lee (1978); we follow Mundlak's (1978) random effects model in correcting for city-level error components.