smd                  package:MBESS                  R Documentation

_S_t_a_n_d_a_r_d_i_z_e_d _m_e_a_n _d_i_f_f_e_r_e_n_c_e

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

     Function to calculate the standardized mean difference (regular or
     unbiased) using either raw data or summary measures.

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

     smd(Group.1 = NULL, Group.2 = NULL, Mean.1 = NULL, Mean.2 = NULL, 
     s.1 = NULL, s.2 = NULL, s = NULL, n.1 = NULL, n.2 = NULL,
     Unbiased=FALSE)

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

 Group.1: Raw data for group 1.

 Group.2: Raw data for group 2.

  Mean.1: The mean of group 1.

  Mean.2: The mean of group 2.

     s.1: The standard deviation of group 1 (i.e., the square root of
          the unbiased estimator of the population variance).

     s.2: The standard deviation of group 2 (i.e., the square root of
          the unbiased estimator of the population variance).

       s: The pooled group standard deviation (i.e., the square root of
          the unbiased estimator of the population variance).

     n.1: The sample size within group 1.

     n.2: The sample size within group 2.

Unbiased: Returns the unbiased estimate of the standardized mean
          difference.

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

     When 'Unbiased=TRUE', the unbiased estimate of the standardized
     mean difference is returned (Hedges, 1981).

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

     Returns the estimated standardized mean difference.

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

     Ken Kelley (Indiana University; KKIII@Indiana.Edu)

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

     Cohen, J. (1988) Statistical power analysis for the behavioral
     sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

     Cumming, G. & Finch, S. (2001) A primer on the understanding, use,
     and calculation of confidence intervals that are based on central
     and noncentral distributions, _Educational and Psychological
     Measurement, 61_, 532-574.

     Hedges, L. V. (1981) Distribution theory for Glass's Estimator of
     effect size and related estimators. _Journal of Educational
     Statistics, 2_, 107-128.

     Kelley, K. (2005) The effects of nonnormal distributions on
     confidence intervals around the standardized mean difference:
     Bootstrap and parametric confidence intervals, _Educational and
     Psychological Measurement, 65_, 51-69.

     Steiger, J. H., & Fouladi, R. T. (1997) Noncentrality interval
     estimation and the evaluation of statistical methods. In L. L.
     Harlow, S. A. Mulaik,&J.H. Steiger (Eds.), _What if there where no
     significance tests?_ (pp. 221-257). Mahwah, NJ: Lawrence Erlbaum.

_S_e_e _A_l_s_o:

     'smd.c', 'conf.limits.nct', 'ss.aipe'

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

     # Generate sample data.
     set.seed(113)
     g.1 <- rnorm(n=25, mean=.5, sd=1)
     g.2 <- rnorm(n=25, mean=0, sd=1)
     smd(Group.1=g.1, Group.2=g.2)

     M.x <- .66745
     M.y <- .24878
     sd <- 1.048
     smd(Mean.1=M.x, Mean.2=M.y, s=sd)

     M.x <- .66745
     M.y <- .24878
     n1 <- 25
     n2 <- 25
     sd.1 <- .95817
     sd.2 <- 1.1311
     smd(Mean.1=M.x, Mean.2=M.y, s.1=sd.1, s.2=sd.2, n.1=n1, n.2=n2)

     smd(Mean.1=M.x, Mean.2=M.y, s.1=sd.1, s.2=sd.2, n.1=n1, n.2=n2, 
     Unbiased=TRUE)

