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Statistical testing for a non-zero heterogeneity variance is often done based on Cochran's Q [13] or related test procedures. This common procedure however is questionable for several reasons, namely, the low power of such tests [14] especially in the very common case of only few estimates being combined in the analysis, [15] [7] as well as the specification of homogeneity as the null ...
They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part. In meta-analysis, which combines the data from several studies, homogeneity measures the differences or similarities between the several studies (see also Study heterogeneity).
The terms random-effect meta-regression and mixed-effect meta-regression are equivalent. Although calling one a random-effect model signals the absence of fixed effects, which would technically disqualify it from being a regression model, one could argue that the modifier random-effect only adds to, not takes away from, what any regression model should include: fixed effects.
The null hypothesis of this chi-squared test is homoscedasticity, and the alternative hypothesis would indicate heteroscedasticity. Since the Breusch–Pagan test is sensitive to departures from normality or small sample sizes, the Koenker–Bassett or 'generalized Breusch–Pagan' test is commonly used instead.
Cochran's test is a non-parametric statistical test to verify whether k treatments have identical effects in the analysis of two-way randomized block designs where the response variable is binary. [ 1 ] [ 2 ] [ 3 ] It is named after William Gemmell Cochran .
There is some work on detecting heterogeneous treatment effects using random forests [4] [5] as well as detecting heterogeneous subpopulations using cluster analysis. [6] [7] Recently, metalearning approaches have been developed that use arbitrary regression frameworks as base learners to infer the CATE.
In statistics, the Cochran–Mantel–Haenszel test (CMH) is a test used in the analysis of stratified or matched categorical data. It allows an investigator to test the association between a binary predictor or treatment and a binary outcome such as case or control status while taking into account the stratification. [ 1 ]
Employment testing is the practice of administering written, oral, or other tests as a means of determining the suitability or desirability of a job applicant. The premise is that if scores on a test correlate with job performance , then it is economically useful for the employer to select employees based on scores from that test.