I am trying to identify scales in a query material consisting of 60000 respondents and 47 ordinal variables. For this purpose my idea is to perform a maximum likelihood exploratory factor analysis to identify underlying factors. These factors are then seen as constructs/indices for which I want to calculate relevant measures of internal consistency.

I have tried to read up on alpha and omega measures, but my math is rusty, so I want to understand them more from a functional perspective and would appreciate help. One resulting problem from my limited understanding of omega is that I don't know which is the correct procedure to calculate them and which one to use, since the psych package will give me several values.

I start by making the exploratory factor analysis:

### Clean slate and load data
load(file='ad6.rda')  # 60000 respondents (rows) and 47 query items (columns)

### Perform factor analysis
pc <- hetcor(ad6, ML=TRUE)         # polychoric correlation matrix 
vss(pc$correlations,n.obs=nrow(ad6),fm='ml',rotate='oblimin') # -> 5 factors
faResults=fa(r=pc$correlations,nfactors=5,n.obs=nrow(ad6),fm='ml',rotate='oblimin') # Make the factor analysis

And then I get this figure. enter image description here In-field expertise assessment is used to check for content/construct validity. As a consequence, 2 variables are removed from the last factor.

I then wish to check for internal consistency and here's where I get into trouble. What I do is this:

# Extract measures of internal consistency
o5=omega(ad6[,c(37,38,39)],nfactors = 2,fm='ml') # Two other variables excluded after assessment by in-field expertise
inc=c(22,30,8,14,24,3,36,35,33,34,1,42,12,29,11,26,19,20,17,16,18,37,38,39) # All included variables
oAll=omega(ad6[,inc],fm='ml',nfactors = 5)

The output I get from the oAll looks like this

enter image description here

I extract the different measures of internal consistency:


And I get the results:

> data.frame(indexNames,alpha,omegaT,omegaL,omegaH)
  indexNames alpha omegaT omegaL omegaH
1        all 0.746  0.829  0.422  0.350
2          1 0.863  0.914  0.818  0.748
3          2 0.778  0.848  0.729  0.619
4          3 0.624  0.774  0.509  0.393
5          4 0.654  0.786  0.641  0.504
6          5 0.638  0.714  0.854  0.610

> oAll$omega.group
        total    general     group
g   0.8293826 0.34961575 0.3974302
F1* 0.8524242 0.33961699 0.5128072
F2* 0.7360407 0.04618383 0.6898569
F3* 0.6242855 0.17356816 0.4507174
F4* 0.6776528 0.02239907 0.6552537
F5* 0.6547722 0.03545072 0.6193215

But I am confused by the different outputs of the omega() of the sub-indices and oAll. Unfortunately my poor reputation (<10) doesn't allow me to post more than 2 figures...

The Cronbach's alpha per index looks like it's calculated correctly, but why should the index be sub-divided into 3 (default) underlying factors for omega? Or is the omega() function not applicable to single constructs? If it isn't - Do I use the omega group output from the oAll? If the omega() function is applicable to single constructs, then why 3 factors per construct?

For the 5th construct, 3 factors gives me the warning that:

Warning message:
In cov2cor(t(w) %*% r %*% w) :
  diag(.) had 0 or NA entries; non-finite result is doubtful

As well as really weird output (both omega_h and omega.lim are 0). But documentation warns against 2 factors. Is it OK to do it using 2 factors?

Moreover, there are a number of papers comparing alpha to omega - and unfortunately I have only a limited understanding of them at present. However, some seem to compare alpha to hierarchical omega and some to total omega. Which is the better measure? And under which conditions?

I have tried to read psych documentation, forum threads and some scientific papers, but so far haven't been able to detect where I get lost. I hope for kind souls with a firm grasp on consistency measures to help me out. I apologise if I offend someone by not having read or understood the subject matter sufficiently beforehand - but I guarantee you that I am at least sufficiently lost to know to ask for help.

Kind regards,

PS. I also realise there is a lack of consistency in using polychoric correlations in the initial factor analysis and pearson correlations in the omega(). However, robustness analysis showed that correlations were almost identical between polychoric and pearson, likely due to the large population size (n=60000). I will however switch to polychoric also for the omega() calculations once I know what I'm doing...

I followed the suggestions from William Revelle (author of the psych package; see his answer below) and calculated omega for the first factor:

keys <- factor2cluster(faResults,cut=0.35)
keys.list <- keys2list(keys)
select1 <- selectFromKeys(keys.list[1])
omf1 <- omega(ad6[select1],fm='ml')

I then compared the omega from my own previous attempt (o1) and plotted them side by side:

enter image description here

The figures are identical and so are the outputs:

Call: omega(m = ad6[select1], fm = "ml")
Alpha:                 0.86 
G.6:                   0.86 
Omega Hierarchical:    0.75 
Omega H asymptotic:    0.82 
Omega Total            0.91 

Schmid Leiman Factor loadings greater than  0.2 
                g   F1*   F2*   F3*   h2   u2   p2
variable 22  0.79  0.39             0.84 0.16 0.75
variable 24  0.56        0.31       0.41 0.59 0.75
variable 30  0.78  0.46             0.85 0.15 0.72
variable 8-  0.71        0.52       0.77 0.23 0.65
variable 14- 0.64        0.21 -0.54 0.75 0.25 0.55

With eigenvalues of:
   g  F1*  F2*  F3* 
2.46 0.38 0.43 0.35 

general/max  5.65   max/min =   1.26
mean percent general =  0.68    with sd =  0.09 and cv of  0.12 
Explained Common Variance of the general factor =  0.68 

The degrees of freedom are -2  and the fit is  0 
The number of observations was  59978  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 5  and the fit is  0.35 
The number of observations was  59978  with Chi Square =  21065.47  with prob <  0
The root mean square of the residuals is  0.1 
The df corrected root mean square of the residuals is  0.14 

RMSEA index =  0.265  and the 10 % confidence intervals are  0.262 0.268
BIC =  21010.46 

Measures of factor score adequacy             
                                                 g   F1*   F2*  F3*
Correlation of scores with factors            0.88  0.59  0.66 0.75
Multiple R square of scores with factors      0.77  0.34  0.44 0.56
Minimum correlation of factor score estimates 0.55 -0.31 -0.12 0.12

 Total, General and Subset omega for each subset
                                                 g  F1*  F2*  F3*
Omega total for total scores and subscales    0.91 0.91 0.73 0.70
Omega general for total scores and subscales  0.75 0.70 0.51 0.41
Omega group for total scores and subscales    0.11 0.20 0.22 0.29

With the kind help from William Revelle, I have it "from the horse's mouth" (to the best of my understanding at least) that this is the correct way to calculate hierarchical omega.

What's really baking my noodle right now is why there should be 3 factors (F1*, F2* and F3*) to describe the data, when my assumption is that they correspond to only one construct (g)? Moreover, for one of the constructs, 3 factors gives me a warning message, whereas reducing to 2 gives me another type of warning:

> o5=omega(ad6[,c(37,38,39)],fm='ml') 
Warning message:
In cov2cor(t(w) %*% r %*% w) :
  diag(.) had 0 or NA entries; non-finite result is doubtful

> o5=omega(ad6[,c(37,38,39)],nfactors = 2,fm='ml')
Three factors are required for identification -- general factor loadings set to be equal. 
Proceed with caution. 
Think about redoing the analysis with alternative values of the 'option' setting.

enter image description here

What's the rationale for choosing number of factors for one construct?


The values of omega change with number of groups (nfactors parameter in omega() function). Alpha does not, which is what I would have expected also from omega if it is functionally a similar measure.

o2 <- omega(ad6[select2],nfactors = 3)
o3 <- omega(ad6[select2],nfactors = 4)


> summary(o2)
Alpha:                 0.78 
G.6:                   0.77 
Omega Hierarchical:    0.62 
Omega H asymptotic:    0.73 
Omega Total            0.85

> summary(o3)
Alpha:                 0.78 
G.6:                   0.77 
Omega Hierarchical:    0.65 
Omega H asymptotic:    0.76 
Omega Total            0.86 
  • 1
    $\begingroup$ Alpha is insensitive to the internal structure of a test. It just reflects the average correlation of the items and the number of items. Omega_h is sensitive to the structure. It is "model based" and will vary by your model. $\endgroup$ Commented Sep 11, 2017 at 20:37
  • $\begingroup$ I think that the discussion of the statistical concepts here is absolutely on-topic for this site, but I think the process of successive editing effectively in reply to answers may tend towards questions becoming too broad to fit neatly into our Q&A format. It may have been a better idea to ask follow-up questions (e.g. What's the rationale for choosing number of factors for one construct?) in a new post. You can always link back to the original post for additional context when you do this. $\endgroup$
    – Silverfish
    Commented Sep 11, 2017 at 20:56
  • 1
    $\begingroup$ And now another comment. The f1*, f2* and f3*, are for the lower level constructs, omega_total is for the general construct. What you need to specify is whether you are trying to find the general for all of your items, or just the subsets of your items. I took your original question to mean you were trying to find the omega_hierarchical for each subset. If you want 5 coefficient alphas, then getting 5 omegas makes sense. But what you will be doing (as I did in my examples) is divide each construct into a general (of which there will be 5) and lower level (at least 3 for each would be good $\endgroup$ Commented Sep 11, 2017 at 21:00
  • $\begingroup$ @Silverfish: Thx for your comment. I will keep this in mind for future posts. $\endgroup$ Commented Sep 12, 2017 at 5:59

3 Answers 3


I am giving a somewhat longer answer than fits in the comment section.

Omega_hierarchical is defined as the amount of item variance associated with the variance common to all of the items. We attempt to partition the variance of the items into general variance, group variance, and residual or specific variance. The group variance is associated with just some of the items, the general variance with all of the items.

This model is conceptually similar to what is known as a "bi-factor" model, with a general factor and multiple group factors. I say conceptually because the algorithm for finding exploratory omega is not technically a bifactor solution, although omega_sem will find the bifactor solution using either the lavaan or sem packages.

Omega finds 3 (or more, that is an option) lower level factors. These factors are transformed obliquely (that is to say, allowed to correlate) and then the correlations of these factors are themselves factored into one general factor. Using the Schmid-Leiman transformation, is is possible to then find the loadings of each item on this general factor. (These will be the loadings on the group factor x the group factor loading on the general factor.) This general factor variance is then removed from the groups and the resulting group factor loadings are reported.
This can also be done (see omegaSem) by doing a confirmatory factor model where every item loads on a general factor and on a group factor. Omega_hierachical (aka Omega_general) is the amount of the total test variance associated with the general factor.

Now, in your case, you have 5 separate domains, for each of which you are trying to find some measure of internal consistency. Focus on just one of those domains. From my previous example, try the items associated with the second domain (listed in select2).

You can find three lower level factors of this set of items by

f3 <- fa(spi[select2],3)

Note their correlation:

 With factor correlations of 
      MR1   MR3   MR2
MR1  1.00  0.46 -0.49
MR3  0.46  1.00 -0.34
MR2 -0.49 -0.34  1.00

This Phi matrix is an object in the omega output: om2$schmid$phi We can find the general factor of these lower level factors by factoring the Phi matrix. This the result of the factoring of this matrix (om2$schmid$gloading) which is the same as directly factoring the Phi matrix call this f1

f1 <- fa(f3$Phi)  #the factor intercorrelation

f1$loadings #show the loadings Loadings: Loadings:

MR1  0.819
MR3  0.560
MR2 -0.598

Now, to find the general factor loadings, take these f1$loadings times the f3 loadings

 g <-f3$loadings %*% f1$loadings

compare to the omega solution general factor loadings found in the schmid$sl object

om2$schmid$sl[,1]  #the first column are the g loadings

Now, finally, the sum of these loadings squared (Vg <- sum(g %*% t(g)) is the amount of the total variance attributed to a general factor. Omega_general is the ratio of Vg to the sum of the correlations. (Note, that some of the items in spi[select2] need to be reverse scored before we do all of these calculations. This was done in the normal omega function, but for the numbers to match here, we need to find Vg <- sum(abs(g %*% t(g)). Then omega_h <- Vg/Vt = .69 (as reported by the omega function).

While I am at it, I might add that omega_hierarchical is really only defined if we have enough items to find a meaningful second order factor. To be defined, that requires at least three group factors. If there are only two group factors, the general factor loadings for the second order factor are not uniquely determined and can range from 1 to the correlation between the two lower level factors. If you specify that you want just two lower level factors, you are given a warning and then omega finds the higher level factor as the square root of the correlation between the lower level factors. You have the option of having this be 1 and r or r and 1.

  • $\begingroup$ Fantastic! Thanks a million for the thorough walk-through! $\endgroup$ Commented Sep 12, 2017 at 6:00

Since you are working on the assumption of 5 different dimensions (based upon the fa), and you are trying to find various internal consistency estimates of each of the scales developed to measure these 5 dimensions, you should apply omega to those five scales separately. That is, you do not want one overall omega, but rather one for each subset. This can be done fairly easily (although there is not one function to do it, you just need to string together a few functions.)

I will do this for the spi data set (which is included in the latest release of psych 1.7.8). The spi data set represents 4000 responses to 135 personality items given as part of the sapa-project.org for personality research. See David Condon's description of the SAPA Personality Inventory (SPI) development at https://sapa-project.org/research/SPI/SPIdevelopment.pdf .


#drop the demographic variables from the spi
spi <- spi[11:145]
#then find 5 factors from this set
f5 <- fa(spi,5)
#extract the items that have their highest loadings on each factor
keys <- factor2cluster(f5)
#make this a list of five different keys to be easier to handle
keys.list <- keys2list(keys)

#now, for each of the five factors, expressed as scoring keys
#find alpha  (by specifying the keys, we are also specifying the direction to score the item
alphaf1 <- alpha(spi,keys.list[1])
alphaf2  <- alpha(spi, keys.list[2])
# etc

#find omega  (we need to select just the items
 select1 <- selectFromKeys(keys.list[1])
select2 <- selectFromKeys(keys.list[2])
## etc
#now, find omega for each subset of items
omf1 <- omega(spi[select1])
omf2 <- omega(spi[select2])

I do not recommend using polychorics to find the omega or alpha values. For doing the factor analysis, yes, but if you use polychorics for the internal consistency estimates, you will get over estimates. We discuss this problem in an in press chapter on reliability: https://www.personality-project.org/revelle/publications/reliability-final.pdf

  • $\begingroup$ Thank you for your reply! And thank you for clarifying the choice of correlation technique for internal consistency. Regarding the main question, I got the same output as you got using my code in the example above, although my code is much clumsier. I'm having a hard time understanding the output of the omega procedure, though. Why are there three factors (F1* - F3*) to calculate omega for one construct? I have tried to expand this question in an EDIT to my original question. $\endgroup$ Commented Sep 11, 2017 at 19:03
  • $\begingroup$ Omega_hierarchical is defined as the amount of item variance associated with the variance common to all of the items. We attempt to partition the variance of the items into general variance, group variance, and residual or specific variance. The group variance is associated with just some of the items, the general variance with all of the items. $\endgroup$ Commented Sep 11, 2017 at 19:39
  • $\begingroup$ If I understand the sentence "Omega_hierarchical is defined as the amount of item variance associated with the variance common to all of the items." then omega_h should not depend on the number of groups? But playing with the data, I see that omega estimates vary with the number of groups (nfactors parameter; see new EDIT). How should the number of groups be chosen? Suppose that there is no rationale for assuming that there are any groups - only the general factor. What then? $\endgroup$ Commented Sep 11, 2017 at 20:03
  • $\begingroup$ If there are no groups, their is no meaning to a general factor. However, this case (all items having equal covariances with one factor) is unlikely. Most scales are "lumpy". That is, they have subsets of items that have higher covariances within lumps (clumps, clusters, factors, what ever term you prefer) than they do between lumps/clumps. In order to show picture of this structure, I will add another answer. $\endgroup$ Commented Sep 12, 2017 at 20:56

To understand the meaning of internal structure, the following figure might help.enter image description here

The top panel represents the correlations of 6 items, the bottom 12 items. Each correlation matrix in row 1 has the same alpha (.72), but omega_h varies from 0 to 72. Similarly, the the correlations in the second row form one "scale" with alpha of .83, but the omega ranges from .83 to 0. What omega is trying to do is to decompose the covariances of these matrices into that which is common to all items and that which is common to just some items (group variance), and that which is common to just the items.

This idea is discussed in fairly extensive detail in my book on psychometric theory ( ) In particular see page 222 in chapter 7. This is probably more than you want to know, but you did ask.

  • $\begingroup$ Brilliant! I'll try and interpret this using the example in the schmid() help: If the "lumps" (tense anxiety, panic disorder, etc) share similarities, then omega is high (approaching alpha). If the item variability is partitioned more toward the lumps than g (general neuroticism) then omega drops. That also implies that omega is more sensitive to fewer variables (where group factors may be harder to rationalise). And there may also be a contextual element: A strong rationale for intermediate group factors in psychometry, whereas other query material may not be equally composed of subgroups. $\endgroup$ Commented Sep 12, 2017 at 21:42

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