# Chapter 16 Omega

## 16.1 Intro

Omega is considered a measure of internal consistency and can, if a number of assumptions are met, estimate the reliability of a set of items.

### 16.1.1 Example dataset

This example uses the Rosetta Stats example dataset “pp15” (see Chapter 1 for information about the datasets and Chapter 3 for an explanation of how to load datasets).

### 16.1.2 Variable(s)

From this dataset, this example uses variables highDose_AttGeneral_good, highDose_AttGeneral_prettig, highDose_AttGeneral_slim, highDose_AttGeneral_gezond & highDose_AttGeneral_spannend.

## 16.2 Input: jamovi

In the “Analyses” tab, click the “Factor” button and from the menu that appear, select “Reliability Analysis” as shown in Figure 16.1. Figure 16.1: Opening the reliability analysis menu in jamovi

In the box at the left, select all variables you want to include in this analysis and move them to the box labelled “Items” using the button labelled with the rightward-pointing arrow as shown in Figure 16.2. Figure 16.2: Adding the items to analyse in jamovi

Select the checkbox labelled “Omega” in the left-most column at the bottom labelled “Scale Statistics”, as shown in Figure 16.3. Figure 16.3: Ordering "Selecting Omega in jamovi

You can now also order additional statistics, such as the value Omega would have if you were to omit each item, as shown in Figure 16.4. Figure 16.4: Ordering “Omega if item dropped” in jamovi

## 16.3 Input: R

rosetta::reliability(
data = dat,
items = c(
"highDose_AttGeneral_good",
"highDose_AttGeneral_prettig",
"highDose_AttGeneral_slim",
"highDose_AttGeneral_gezond",
"highDose_AttGeneral_spannend"
)
);

## 16.4 Input: SPSS

SPSS is unable to compute Omega.

## 16.5 Output: jamovi Figure 16.5: The output of a reliability analysis where Omega is computed in jamovi

## 16.6 Output: R

### 16.6.1 R: rosetta

#### 16.6.1.1 Reliability analysis

##### 16.6.1.1.1 Scale structure
###### 16.6.1.1.1.1 Scale structure

Dataframe: res\$data
Items: highDose_AttGeneral_good, highDose_AttGeneral_prettig, highDose_AttGeneral_slim, highDose_AttGeneral_gezond & highDose_AttGeneral_spannend
Observations: 303
Positive correlations: 10
Number of correlations: 10
Percentage positive correlations: 100

16.6.1.1.1.1.2 Estimates assuming interval level

Omega (total): 0.79
Omega (hierarchical): 0.80
Revelle’s Omega (total): 0.79
Greatest Lower Bound (GLB): 0.87
Coefficient H: 0.85
Coefficient Alpha: 0.78

Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help (‘?ufs::scaleStructure’) for more information.

## 16.7 Output: SPSS

SPSS is unable to compute Omega.