`SMD.Rd`

Calculates the within-case standardized mean difference effect size index

```
SMD(
A_data,
B_data,
condition,
outcome,
baseline_phase = NULL,
intervention_phase = NULL,
improvement = "increase",
std_dev = "baseline",
bias_correct = TRUE,
confidence = 0.95
)
```

- A_data
vector of numeric data for A phase. Missing values are dropped.

- B_data
vector of numeric data for B phase. Missing values are dropped.

- condition
vector identifying the treatment condition for each observation in the series.

- outcome
vector of outcome data for the entire series.

- baseline_phase
character string specifying which value of

`condition`

corresponds to the baseline phase. Defaults to first observed value of`condition`

.- intervention_phase
character string specifying which value of

`condition`

corresponds to the intervention phase. Defaults to second unique value of`condition`

.- improvement
character string indicating direction of improvement. Default is "increase".

- std_dev
character string controlling how to calculate the standard deviation in the denominator of the effect size. Set to

`"baseline"`

(the default) to use the baseline standard deviation. Set to`"pool"`

to use the pooled standard deviation.- bias_correct
logical value indicating whether to use bias-correction (i.e., Hedges' g). Default is

`TRUE`

- confidence
confidence level for the reported interval estimate. Set to

`NULL`

to omit confidence interval calculations.

A list containing the estimate, standard error, and confidence interval.

The standardized mean difference parameter is defined as the difference between the mean level of the outcome in phase B and the mean level of the outcome in phase A, scaled by the within-case standard deviation of the outcome in phase A. The parameter is estimated using sample means and sample standard deviations and (optionally) making a small-sample correction.

By default, the scaling factor is estimated using the sample standard
deviation in phase A (the baseline phase) only. Set `std_dev = "pool"`

to use the sample standard deviation pooled across both phases. Hedges'
(1981) small-sample bias correction is applied by default.

```
A <- c(20, 20, 26, 25, 22, 23)
B <- c(28, 25, 24, 27, 30, 30, 29)
SMD(A_data = A, B_data = B, bias_correct = FALSE)
#> ES Est SE CI_lower CI_upper baseline_SD
#> 1 SMD 1.959294 0.8237984 0.3446789 3.573909 2.503331
SMD(A_data = A, B_data = B)
#> ES Est SE CI_lower CI_upper baseline_SD
#> 1 SMD 1.649932 0.6340935 0.4071314 2.892732 2.503331
SMD(A_data = A, B_data = B, std_dev = "pool")
#> ES Est SE CI_lower CI_upper pooled_SD
#> 1 SMD 1.876247 0.6374216 0.6269241 3.125571 2.431752
```