Launch Omega with the Psych library in R

Question

I have five elements in the design, when I run alphaon it, I get the following results without errors

 psych::alpha(construct,
         na.rm = TRUE,
         title = 'myscale', 
         n.iter = 1000)

Reliability analysis  myscale  
Call: psych::alpha(x = construct, title = "myscale", na.rm = TRUE, 
n.iter = 1000)

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd
  0.81      0.81    0.78      0.46 4.3 0.013  2.6 0.89

 lower alpha upper     95% confidence boundaries
0.78 0.81 0.84 

 lower median upper bootstrapped confidence intervals
 0.77 0.81 0.84

I read the article From Alpha to omega: A practical solution to the pervasive problem of internal consistency estimation link

He recommends using the code below

MBESS::ci.reliability(construct, interval.type="bca", B=1000, type = "omega") 

$est
[1] 0.8107376

$se
[1] 0.01651936

$ci.lower
[1] 0.7764029

$ci.upper
[1] 0.839944

$conf.level
[1] 0.95

$type
[1] "omega"

$interval.type
[1] "bca bootstrap"

I'm trying to run omega on my sample set using the psych package to keep things in my analysis

psych::omega(m = construct, 
      nfactors = 1, fm = "pa", n.iter = 1000, p = 0.05, 
      title = "Omega", plot = FALSE, n.obs = 506)

I get two error messages

In factor.scores, the correlation matrix is ​​singular, using the Omega_h approximation for 1 factor does not make sense, just omega_t

This warning occurs because the number of columns for Omega_h is two. A previous question about SO answers this somewhat McDonalds omega: warnings in R

Im error having below

(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate,:    : (NA) . . : 50 ( (), 50)

, ,

    Q1                  Q2          Q3    
 Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.:2.000   1st Qu.:2.000   1st Qu.:2.000  
 Median :3.000   Median :2.000   Median :3.000  
 Mean   :2.597   Mean   :2.393   Mean   :3.227  
 3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:4.000  
 Max.   :6.000   Max.   :6.000   Max.   :6.000  

Q4              Q5   
 Min.   :0.00   Min.   :0.000  
 1st Qu.:1.00   1st Qu.:2.000  
 Median :2.00   Median :2.000  
 Mean   :2.17   Mean   :2.445  
 3rd Qu.:3.00   3rd Qu.:3.000  
 Max.   :6.00   Max.   :6.000  

- 100 (Alpha 0,56), omega

structure(list(Q1 = c(4, 5, 3, 5, 4, 5, 3, 5, 5, 5, 6, 
3, 5, 4, 6, 5, 5, 6, 7, 4, 5, 5, 3, 4, 4, 5, 4, 3, 5, 4, 5, 5, 
6, 6, 3, 6, 3, 4, 4, 4, 6, 5, 3, 2, 6, 6, 4, 5, 4, 3, 6, 4, 4, 
5, 6, 2, 4, 3, 4, 6, 4, 6, 4, 5, 5, 6, 4, 6, 5, 5, 4, 5, 6, 6, 
2, 5, 4, 3, 4, 4, 4, 6, 3, 3, 5, 4, 4, 4, 5, 5, 5, 3, 6, 6, 6, 
6, 5, 4, 3, 5), Q2 = c(7, 4, 4, 4, 4, 6, 6, 6, 7, 6, 5, 
6, 5, 4, 5, 6, 6, 6, 7, 5, 4, 4, 6, 6, 4, 4, 6, 2, 6, 5, 4, 6, 
4, 6, 6, 6, 5, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 6, 2, 6, 6, 5, 4, 
6, 6, 4, 4, 7, 6, 5, 5, 5, 5, 6, 5, 5, 4, 5, 5, 5, 4, 6, 7, 5, 
5, 5, 6, 5, 6, 5, 6, 7, 2, 6, 5, 7, 3, 5, 5, 3, 3, 3, 7, 4, 5, 
6, 6, 6, 5, 7), Q3 = c(5, 4, 5, 6, 4, 4, 5, 4, 2, 6, 5, 
5, 5, 5, 7, 5, 5, 6, 7, 6, 3, 6, 6, 6, 5, 6, 6, 5, 5, 4, 5, 5, 
6, 6, 5, 6, 5, 5, 4, 4, 6, 4, 4, 4, 4, 4, 4, 5, 5, 4, 5, 5, 4, 
3, 5, 4, 5, 6, 6, 6, 4, 5, 5, 5, 6, 4, 5, 5, 7, 4, 5, 6, 6, 5, 
5, 3, 3, 5, 4, 6, 5, 5, 1, 3, 5, 3, 2, 5, 4, 6, 6, 6, 6, 4, 6, 
3, 6, 6, 6, 5), Q4 = c(6, 6, 4, 7, 4, 6, 7, 6, 7, 6, 6, 
6, 5, 7, 7, 6, 6, 5, 7, 7, 6, 6, 7, 7, 6, 6, 6, 5, 6, 7, 5, 6, 
7, 5, 4, 6, 4, 3, 6, 4, 6, 6, 6, 3, 5, 7, 5, 6, 4, 6, 7, 6, 7, 
4, 6, 3, 5, 7, 5, 4, 6, 6, 4, 6, 5, 5, 5, 5, 7, 7, 7, 6, 6, 6, 
5, 6, 6, 4, 5, 7, 6, 7, 3, 5, 6, 5, 6, 5, 5, 7, 7, 6, 6, 2, 7, 
6, 6, 7, 7, 5)), .Names = c("Q1", "Q2", "Q3", 
"Q4"), row.names = c(NA, 100L), class = "data.frame")

- , ?

Answer 1

:

psych::omega(m = construct)

:

Omega 
Call: psych::omega(m = construct)
Alpha:                 0.56 
G.6:                   0.49 
Omega Hierarchical:    0.53 
Omega H asymptotic:    0.89 
Omega Total            0.6 

Schmid Leiman Factor loadings greater than  0.2 
     g   F1*   F2*   F3*   h2   u2   p2
Q1 0.41  0.30             0.26 0.74 0.65
Q2 0.37  0.25             0.20 0.80 0.67
Q3 0.50        0.25       0.31 0.69 0.80
Q4 0.64              0.23 0.46 0.54 0.89

With eigenvalues of:
    g  F1*  F2*  F3* 
 0.95 0.15 0.06 0.05 

 general/max  6.35   max/min =   2.83
mean percent general =  0.75    with sd =  0.11 and cv of  0.15 
Explained Common Variance of the general factor =  0.78 

The degrees of freedom are -3  and the fit is  0 
The number of observations was  100  with Chi Square =  0  with prob <  NA
The root mean square of the residuals is  0 
The df corrected root mean square of the residuals is  NA

Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 2  and the fit is  0.01 
The number of observations was  100  with Chi Square =  0.62  with prob <  0.73
The root mean square of the residuals is  0.03 
The df corrected root mean square of the residuals is  0.05 

RMSEA index =  0  and the 90 % confidence intervals are  NA 0.14
BIC =  -8.59 

Measures of factor score adequacy             
                                                 g   F1*   F2*   F3*
Correlation of scores with factors            0.75  0.37  0.27  0.24
Multiple R square of scores with factors      0.57  0.14  0.07  0.06
Minimum correlation of factor score estimates 0.14 -0.72 -0.86 -0.88

 Total, General and Subset omega for each subset
                                                 g  F1*  F2*  F3*
Omega total for total scores and subscales    0.60 0.37 0.31 0.46
Omega general for total scores and subscales  0.53 0.25 0.25 0.41
Omega group for total scores and subscales    0.06 0.12 0.06 0.05

nfactors = 3 n.iter = 1. n.iter n., n.iter = 7 nfactors 3

psych::omega(m = construct, n.iter = 7, p = 0.05, nfactors = 3)

n.iter