Random effects model
You don't need to be Editor-In-Chief to add or edit content to WikiDoc. You can begin to add to or edit text on this WikiDoc page by clicking on the edit button at the top of this page. Next enter or edit the information that you would like to appear here. Once you are done editing, scroll down and click the Save page button at the bottom of the page.
Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1] Phone:617-525-6884
Please Take Over This Page and Apply to be Editor-In-Chief for this topic: There can be one or more than one Editor-In-Chief. You may also apply to be an Associate Editor-In-Chief of one of the subtopics below. Please mail us [2] to indicate your interest in serving either as an Editor-In-Chief of the entire topic or as an Associate Editor-In-Chief for a subtopic. Please be sure to attach your CV and or biographical sketch.
In statistics, a random effect(s) model, also called a variance components model is a kind of hierarchical linear model. It assumes that the data describe a hierarchy of different populations whose differences are constrained by the hierarchy. The fixed effects model is a special case.
Simple example
Suppose m elementary large schools are chosen randomly from among millions in a large country. Then n pupils are chosen randomly from among those at each such school. Their scores on a standard aptitude test are ascertained. Let Yij be the score of the jth pupil at the ith school. Then
where μ is the average of all scores in the whole population, Ui is the deviation of the average of all scores at the ith school from the average in the whole population, and Wij is the deviation of the jth pupil's score from the average score at the ith school.
Variance components
The variance of Yij is the sum of the variances τ2 and σ2 of Ui and Wij respectively.
Let
be the average, not of all scores at the ith school, but of those at the ith school that are included in the random sample. Let
be the "grand average".
Let
be respectively the sum of squares due to differences within groups and the sum of squares due to difference between groups. Then it can be shown that
and
These "expected mean squares" can be used as the basis for estimation of the "variance components" σ2 and τ2.
References
- Random effect model at Bandolier (Oxford EBM website)
- Fixed and random effects models
- Distinguishing Between Random and Fixed: Variables, Effects, and Coefficients
- How to Conduct a Meta-Analysis: Fixed and Random Effect Models
See also
Acknowledgement and Attribution Regarding Sources of Content
Some of the initial content on this page may be incorporated in part from copyleft sources in the public domain including wikis such as Wikipedia and AskDrWiki. Drug information for patients came from the The National Library of Medicine. Infectious disease information may have come from the Centers for Disease Control (CDC). Differential Diagnoses are drawn from clinicians as well as an amalgamation of 3 sources: 1.The Disease Database; 2. Kahan, Scott, Smith, Ellen G. In A Page: Signs and Symptoms. Malden, Massachusetts: Blackwell Publishing, 2004:3; 3. Sailer, Christian, Wasner, Susanne. Differential Diagnosis Pocket. Hermosa Beach, CA: Borm Bruckmeir Publishing LLC, 2002:7 .

