# omega.F: Omega Squared for ANOVA from F In MOTE: Effect Size and Confidence Interval Calculator

## Description

This function displays omega squared from ANOVA analyses and its non-central confidence interval based on the F distribution. These values are calculated directly from F statistics and can be used for between subjects and repeated measures designs. Remember if you have two or more IVs, these values are partial omega squared.

## Usage

 `1` ```omega.F(dfm, dfe, Fvalue, n, a = 0.05) ```

## Arguments

 `dfm` degrees of freedom for the model/IV/between `dfe` degrees of freedom for the error/residual/within `Fvalue` F statistic `n` full sample size `a` significance level

## Details

Omega squared or partial omega squared is calculated by subtracting one from the F-statistic and multiplying it by degrees of freedom of the model. This is divided by the same value after adding the number of valid responses. This value will be omega squared for one-way ANOVA designs, and will be partial omega squared for multi-way ANOVA designs (i.e. with more than one IV).

omega^2 = (dfm * (Fvalue-1)) / ((dfm * (Fvalue-1)) + n)

## Value

The effect size (Cohen's d) with associated confidence intervals and relevant statistics.

 `omega` omega statistic `omegalow` lower level confidence interval d value `omegahigh` upper level confidence interval d value `dfm` degrees of freedom for the model/IV/between `dfe` degrees of freedom for the error/residual/within `F` F-statistic `p` p-value `estimate` the omega squared statistic and confidence interval in APA style for markdown printing `statistic` the F-statistic in APA style for markdown printing

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```#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.F(dfm = 2, dfe = 8, Fvalue = 5.134, n = 11, a = .05) ```

MOTE documentation built on May 2, 2019, 5:51 a.m.