cronbach                 package:psy                 R Documentation

_C_r_o_n_b_a_c_h'_s _c_o_e_f_f_i_c_i_e_n_t _a_l_p_h_a

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the Cronbach's reliability coefficient alpha. This
     coefficient may be applied to a series of items destinated to be
     aggregated in a single score. It estimates reliability in the
     framework of the domain sampling model.

_U_s_a_g_e:

     cronbach(v1)

_A_r_g_u_m_e_n_t_s:

      v1: n*p matrix or dataframe, n subjects and p items

_D_e_t_a_i_l_s:

     Missing value are omitted in a "listwise" way (all items are
     removed even if only one of them is missing).

_V_a_l_u_e:

     A list with : 

$sample.size: Number of subjects under study

$number.of.items: Number of items of the scale or questionnaire

  $alpha: alpha

_A_u_t_h_o_r(_s):

     Bruno Falissard

_R_e_f_e_r_e_n_c_e_s:

     Nunnaly, J.C., Bernstein, I.H. (1994), Psychometric Theory, 3rd
     edition, McGraw-Hill Series in Psychology.

_E_x_a_m_p_l_e_s:

     data(expsy)
     cronbach(expsy[,1:10])  ## not good because item 2 is reversed (1 is high and 4 is low)
     cronbach(cbind(expsy[,c(1,3:10)],-1*expsy[,2]))  ## better

     datafile <- cbind(expsy[,c(1,3:10)],-1*expsy[,2])
     library(boot)
     cronbach.boot <- function(data,x) {cronbach(data[x,])[[3]]}
     res <- boot(datafile,cronbach.boot,1000)
     quantile(res$t,c(0.025,0.975))  ## two-sided bootstrapped confidence interval of Cronbach's alpha

