effect_size_MB: Calculates HPS effect size

View source: R/HPS-ES-functions.R

effect_size_MBR Documentation

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
)

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

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

Note

If phi or rho is left unspecified (or both), estimates for the nuisance parameters will be calculated.

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. \Sexpr[results=rd]{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)


scdhlm documentation built on May 29, 2024, 12:11 p.m.