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R code from Chris Evans for computing confidence intervals for Cronbach's alpha

You need a value of alpha (obs.a), sample size (n), the number of items (k), length of confidence interval (ci) and the value under the null hypothesis (usually 0).

feldt1.return <- function(obs.a, n, k, ci = 0.95, null.a = 0) { 

        if(obs.a > null.a)
                f <- (1 - obs.a)/(1 - null.a)
        else f <- (1 - null.a)/(1 - obs.a)      
              # allows for testing against a higher null
        n.den <- (n - 1) * (k - 1)
        n.num <- n - 1
        null.p <- pf(f, n.num, n.den)   
              # set the upper and lower p values for the desired C.I.
        p1 <- (1 - ci)/2
        p2 <- ci + p1   # corresponding F values
        f1 <- qf(p1, n.num, n.den)
        f2 <- qf(p2, n.num, n.den)      # confidence interval
        lwr <- 1 - (1 - obs.a) * f2
        upr <- 1 - (1 - obs.a) * f1
        cat(round(lwr,2), "to",round(upr,2),"\n")
        interval <- list(lwr,upr)
        return(interval)

} 

feldt1.return(0.913, 873, 20, 0.95, 0)

gives

0.9 to 0.92 
[[1]]
[1] 0.9044024

[[2]]
[1] 0.9211588

None: FAQ/cafun (last edited 2013-03-08 10:17:18 by localhost)