metastudy_X_sigma_cors: Computes correlations to test the independece assumption...

In skranz/MetaStudies: Shiny app and function for Meta studies following Andrews and Kasy (2019).

Description

A crucial assumption of Andrews and Kasy (2019) is that in the unobserved latent distribution without publication error the estimate and its standard error are statistically independent from each other.

Usage

metastudy_X_sigma_cors(ms)

Arguments

ms	An object returned from the function metastudies_estimation

Details

While the latent distribution cannot be observed, this function computes some correlations that may indicate problems with respect to this assumption. We use an inverse probability weighting approach. More precisely, it weights inversely with the estimated publication probabilities to recover the correlation in the unobserved latent distribution. (This approach was suggested in an email by Isaiah Andrews and implemented by Sebastian Kranz).

To compute standard errrors via bootstrap (very time consuming), call the function bootstrap_specification_tests.

Value

a data frame with correlations, confidence intervals and also relevent results from a linear regression of standard errors on estimates.

skranz/MetaStudies documentation built on Dec. 23, 2021, 3:23 a.m.