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Computes a slope and its standard error

Source: R/meta_se.R
se.slope.Rd

Computes a slope and its standard error for a simple linear regression model (random-x model) using the estimated Pearson correlation and the estimated standard deviations of the response variable and predictor variable. This function is useful in a meta-analysis of slopes of a simple linear regression model where some studies report the Pearson correlation but not the slope.

For more details, see Chapter 1 of Bonett (2021, Volume 5)

Usage

se.slope(cor, sdy, sdx, n)

Arguments

cor

estimated Pearson correlation

sdy

estimated standard deviation of the response variable

sdx

estimated standard deviation of the predictor variable

n

sample size

Value

Returns a one-row matrix:

  • Estimate - estimated slope

  • SE - standard error

References

Bonett DG (2021). Statistical Methods for Psychologists, Vol 1-5, https://dgbonett.sites.ucsc.edu/.

Examples

se.slope(.392, 4.54, 2.89, 60)
#>          Estimate        SE
#> Slope:  0.6158062 0.1897647

# Should return: 
#          Estimate        SE
# Slope:  0.6158062 0.1897647