scdhlm (version 0.3)

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, phi, rho)

Arguments

outcome

Vector of outcome data. May not contain any missing values.

treatment

Vector of treatment indicators. Must be the same length as outcome.

id

factor vector indicating unique cases. Must be the same length as outcome.

time

vector of measurement occasion times. Must be the same length as outcome.

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

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. doi:10.1002/jrsm.1086

Examples

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

data(Laski)
with(Laski, effect_size_MB(outcome, treatment, case, time))