scdhlm (version 0.7.3)

effect_size_MB: Calculates HPS effect size

Description

Calculates the HPS effect size estimator based on data from a multiple baseline design, as described in Hedges, Pustejovsky, & Shadish (2013). Note that the data must contain one row per measurement occasion per subject.

Usage

effect_size_MB(
  outcome,
  treatment,
  id,
  time,
  data = NULL,
  phi = NULL,
  rho = NULL
)

Value

A list with the following components

g_dotdot	total number of non-missing observations
K	number of time-by-treatment groups containing at least one observation
D_bar	numerator of effect size estimate
S_sq	sample variance, pooled across time points and treatment groups
delta_hat_unadj	unadjusted effect size estimate
phi	corrected estimate of first-order auto-correlation
sigma_sq_w	corrected estimate of within-case variance
rho	estimated intra-class correlation
theta	estimated scalar constant
nu	estimated degrees of freedom
delta_hat	corrected effect size estimate
V_delta_hat	estimated variance of delta_hat

Arguments

outcome

vector of outcome data or name of variable within data. May not contain any missing values.

treatment

vector of treatment indicators or name of variable within data. Must be the same length as outcome.

id

factor vector indicating unique cases or name of variable within data. Must be the same length as outcome.

time

vector of measurement occasion times or name of variable within data. Must be the same length as outcome.

data

(Optional) dataset to use for analysis. Must be data.frame.

phi

(Optional) value of the auto-correlation nuisance parameter, to be used in calculating the small-sample adjusted effect size

rho

(Optional) value of the intra-class correlation nuisance parameter, to be used in calculating the small-sample adjusted effect size

References

Hedges, L. V., Pustejovsky, J. E., & Shadish, W. R. (2013). A standardized mean difference effect size for multiple baseline designs across individuals. Research Synthesis Methods, 4(4), 324-341. tools:::Rd_expr_doi("10.1002/jrsm.1086")

Examples

data(Saddler)
effect_size_MB(outcome = outcome, treatment = treatment, id = case, 
               time = time, data = subset(Saddler, measure=="writing quality"))

data(Laski)
effect_size_MB(outcome = outcome, treatment = treatment, id = case, 
               time = time, data = Laski)