MOTE (version 1.2.2)

omega_full_ss: omega^2 for One-Way and Multi-Way ANOVA from F

Description

This function displays \(\omega^2\) from ANOVA analyses and its non-central confidence interval based on the \(F\) distribution. This formula works for one way and multi way designs with careful focus on which error term you are using for the calculation.

Usage

omega_full_ss(dfm, dfe, msm, mse, sst, a = 0.05)
omega.full.SS(dfm, dfe, msm, mse, sst, a = 0.05)

Value

omega

\(\omega^2\) effect size (legacy name; see also `omega_value`)

omegalow

lower-level confidence interval of \(\omega^2\) (legacy name; see also `omega_lower_limit`)

omegahigh

upper-level confidence interval of \(\omega^2\) (legacy name; see also `omega_upper_limit`)

dfm

degrees of freedom for the model/IV/between (legacy name; see also `df_model`)

dfe

degrees of freedom for the error/residual/within (legacy name; see also `df_error`)

F

\(F\)-statistic (legacy name; see also `f_value`)

p

p-value (legacy name; see also `p_value`)

estimate

the \(\omega^2\) statistic and confidence interval in APA style for markdown printing

statistic

the \(F\)-statistic in APA style for markdown printing

omega_value

\(\omega^2\) effect size (snake_case alias of `omega`)

omega_lower_limit

lower-level confidence interval of \(\omega^2\) (alias of `omegalow`)

omega_upper_limit

upper-level confidence interval of \(\omega^2\) (alias of `omegahigh`)

df_model

degrees of freedom for the model/IV/between (alias of `dfm`)

df_error

degrees of freedom for the error/residual/within (alias of `dfe`)

f_value

\(F\)-statistic (alias of `F`)

p_value

p-value (alias of `p`)

Arguments

dfm

degrees of freedom for the model/IV/between

dfe

degrees of freedom for the error/residual/within

msm

mean square for the model/IV/between

mse

mean square for the error/residual/within

sst

sum of squares total

a

significance level

Details

Omega squared is calculated by deducting the mean square of the error from the mean square of the model and multiplying by the degrees of freedom for the model. This is divided by the sum of the sum of squares total and the mean square of the error.

$$\omega^2 = \frac{df_m (ms_m - ms_e)}{SS_T + ms_e}$$

Learn more on our example page.

**Note on function and output names:** This effect size is now implemented with the snake_case function name `omega_full_ss()` to follow modern R style guidelines. The original dotted version `omega.full.SS()` is still available as a wrapper for backward compatibility, and both functions return the same list. The returned object includes both the original element names (e.g., `omega`, `omegalow`, `omegahigh`, `dfm`, `dfe`, `F`, `p`, `estimate`, `statistic`) and newer snake_case aliases (e.g., `omega_value`, `omega_lower_limit`, `omega_upper_limit`, `df_model`, `df_error`, `f_value`, `p_value`). New code should prefer `omega_full_ss()` and the snake_case output names, but existing code using the older names will continue to work.

Examples

# The following example is derived from the "bn1_data"
# dataset, included in the MOTE library.

# A health psychologist recorded the number of close inter-personal
# attachments of 45-year-olds who were in excellent, fair, or poor
# health. People in the Excellent Health group had 4, 3, 2, and 3
# close attachments; people in the Fair Health group had 3, 5,
# and 8 close attachments; and people in the Poor Health group
# had 3, 1, 0, and 2 close attachments.

anova_model <- lm(formula = friends ~ group, data = bn1_data)
summary.aov(anova_model)

omega_full_ss(dfm = 2, dfe = 8,
              msm = 12.621, mse = 2.548,
              sst = (25.54 + 19.67), a = .05)

# Backwards-compatible dotted name (deprecated)
omega.full.SS(dfm = 2, dfe = 8,
              msm = 12.621, mse = 2.548,
              sst = (25.54 + 19.67), a = .05)