efa_reliability: Reliability and common-variance coefficients for a factor...

In EFAtools: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

Reliability and common-variance coefficients for a factor solution

Description

Computes model-based reliability coefficients for a factor solution: McDonald's omega (total, hierarchical, and subscale), standardized Cronbach's alpha, and the H index. For a bifactor solution, it also computes two common-variance indices, ECV and PUC, for the general factor. The result is a tidy, long-format table with one row per coefficient.

Usage

efa_reliability(
  model = NULL,
  coefficients = NULL,
  g_name = "g",
  group_names = NULL,
  factor_map = NULL,
  variance = c("correlation", "sums_load"),
  var_names = NULL,
  fac_names = NULL,
  g_load = NULL,
  s_load = NULL,
  u2 = NULL,
  cormat = NULL,
  pattern = NULL,
  Phi = NULL
)

Arguments

model	an efa_schmid_leiman(), schmid (psych::schmid()), efa_fit() (oblique), or lavaan object; a raw bifactor loading matrix (general factor first), the loading table of an efa_schmid_leiman() solution, or the pattern matrix of an oblique solution together with its Phi; or NULL to supply the components manually via g_load, s_load, u2, and var_names.
coefficients	character. An optional subset of the coefficients to return, any of "omega_total", "omega_hierarchical", "omega_subscale", "alpha", "H", "ECV", and "PUC". Default NULL returns all of them.
g_name	character. The name of the general factor in the lavaan solution. Only needed for a lavaan second-order or bifactor fit. A fit in which every variable loads on a single factor has no general factor, and reads none. No other input is affected by it. Default is "g".
group_names	character. An optional vector of group names for a lavaan multiple-group fit. Its length must match the number of groups. Not used for any other input – including a single-group lavaan fit, since every one of those is scored as a single ungrouped solution with no group label.
factor_map	matrix. A logical or 0/1 matrix indicating which variable belongs to which group factor, with the same dimensions as the group loading matrix (cross-loadings are allowed). Match its columns to the group factors by position – a map given in a different factor order than the solution still runs, but produces meaningless subscale coefficients. The function warns if a mapped item loads weakly on its assigned factor and more strongly on another one. If NULL (default), each variable is assigned to the group factor on which it loads most strongly. Not used for lavaan input.
variance	character. The total-variance denominator for the coefficients. "correlation" (default) takes each composite's variance from the correlation matrix, giving the observed-score omega. "sums_load" uses the model-implied composite variance from the loadings and the uniquenesses instead; it needs no correlation matrix, so it is the way to score a bare loading matrix or manual components given without one. lavaan input fixes the convention: its composite variances are always model-implied, and include any freed residual covariance as well as the residual variances. Neither convention changes the metric the coefficients are on – with polychoric or tetrachoric correlations, or an ordered lavaan fit, both describe the latent-response composite rather than the ordinal sum score (see Details).
var_names	character. Subtest names in the row order of the loadings. Only needed when model is NULL.
fac_names	character. An optional vector of group-factor names in the column order of the loadings. Taken from the input if NULL. A single-factor solution has no group factors; fac_names then labels its one factor instead. If neither fac_names nor the solution names that factor, it is labelled "F1". Not used for lavaan input, whose factor labels come from the model syntax.
g_load	numeric. General-factor loadings. Only needed when model is NULL.
s_load	matrix. Group-factor loadings. Only needed when model is NULL.
u2	numeric. Uniquenesses. Only needed when model is NULL, or to override the communality-based default for a loading matrix. Under variance = "correlation", the coefficients follow from the loadings and the correlation matrix alone, so u2 only enters the check for an improper solution. Under "sums_load", it is part of every composite's variance.
cormat	matrix or data.frame. A correlation matrix used when variance = "correlation". It must hold the same variables as the solution: named variables in a different order are reordered to match; a different set of variables, or a different number of them, is an error. The matrix must use the solution's own variable names – for manually supplied components, that means the row names of s_load, not var_names, which only labels the output. Without row names, the variables are matched by position, so supply the matrix in the row order of the loadings. If NULL, it is taken from the input object, or reconstructed from pattern and Phi where possible. Supply the matrix on the same metric as the loadings: a polychoric or tetrachoric matrix keeps the coefficients on the latent-response metric, while a Pearson matrix given with loadings fitted to a polychoric one mixes the two (see Details).
pattern	matrix. Pattern coefficients from a separate oblique solution, used with Phi to reconstruct a correlation matrix when model is NULL. Supply it for a Schmid-Leiman input, whose s_load holds the orthogonalized group loadings rather than the oblique ones. It is an alternative to cormat, not a supplement: giving both is an error.
Phi	matrix. Factor intercorrelations. NULL (default) means uncorrelated group factors, as a Schmid-Leiman or bifactor solution has. It cannot be combined with a general factor: supplied together with a non-zero g_load, or with the loading table of an efa_schmid_leiman() solution, it is an error. Supply it together with s_load (manually supplied components, with g_load zero throughout), or with a loading matrix of two or more factors in model, to score the input as a correlated-factors solution instead. Without Phi, a loading matrix in model is read as a bifactor solution (general factor first). The one exception is a matrix that still carries the loading class efa_fit() gives its output – a class that subsetting a matrix or reordering its rows drops. Such a matrix is rejected instead; supply Phi to score it as a correlated-factors solution. With Phi, a matrix in model that carries no loading class is read as a correlated-factors solution, and a warning names this reading (drop Phi to read it as a bifactor matrix instead). Given together with pattern instead, see pattern above. With a fitted solution already in model, Phi is ignored, with a warning, since that solution carries its own factor intercorrelations.

Details

The function reads many kinds of input: a Schmid-Leiman solution (efa_schmid_leiman() or psych::schmid()), an oblique efa_fit() (correlated-factors) solution, a lavaan fit (single-factor, correlated-factors, second-order, or bifactor), a raw bifactor loading matrix, an oblique pattern matrix given with its factor intercorrelations, or manually supplied components.

Coefficients

The reliability coefficients are McDonald's omegas (McDonald, 1978, 1985, 1999; Zinbarg et al., 2005, 2006, for omega hierarchical specifically), standardized Cronbach's alpha (Cronbach, 1951), and the H index (construct replicability; Hancock & Mueller, 2001).

The omegas give the share of true score variance in a unit-weighted composite. Omega total is the share due to all factors together. Omega hierarchical is the share due to the general factor only. Omega subscale is the share due to the group factors: for the whole scale, or for one specific group factor in a subscale composite.

Alpha is the standardized coefficient, computed from the correlation matrix of the items. Where no such matrix is available – for lavaan input, and for components supplied without one – alpha is computed from the model-implied correlation matrix instead, so it then reflects the fitted model rather than the raw data.

The H index is the reliability of an optimally weighted composite. A low value means the factor is not well defined by its indicators.

All of these coefficients describe the raw sum of the variables as fitted, without reverse-coding. If some items are keyed in the opposite direction – for example, a reverse-worded item that was not reverse-scored – they lower that sum's true-score variance. The coefficients then look poor even though the model fits well. The function warns when it detects this. Reverse-code such items before fitting the solution (Flora, 2020).

The sum these coefficients describe is not always the sum of the raw answers. Polychoric and tetrachoric correlations describe the continuous latent responses assumed to underlie ordinal answers; a lavaan fit that declares its indicators ordered does the same. In that case the loadings, the uniquenesses, the correlation matrix, and the coefficients are all on that latent-response metric. They give the reliability of the unit-weighted sum of the latent responses, which is not observed – not the reliability of the ordinal sum score the user actually computes from the answers. The two can differ substantially, especially where the answers use few categories or are strongly skewed. Green and Yang (2009) give an omega for the ordinal sum score itself, computed from the fitted model and its thresholds; this package does not compute it. Pearson correlations raise no such distinction, because their metric is the answers as scored.

Omega total is lower when a solution reproduces the observed correlations poorly, because residual covariance does not count as true score. psych::omega() computes the whole-scale omega total differently: residually, from the observed total-score variance. The two agree when the model reproduces the correlations exactly, and diverge otherwise.

The three coefficients are not generally additive. Omega total need not equal omega hierarchical plus omega subscale, except on the whole-scale row under variance = "sums_load".

A single-factor solution is scored as such on every input route, and returns omega total, alpha, and the H index. Alpha assumes essentially tau-equivalent items, an assumption nested within a one-factor model – so a single factor is the case where reporting alpha is defensible, not merely possible.

The other coefficients are omitted because a single factor does not define them: omega subscale is the variance due to the group factors, and there are none; omega hierarchical would equal omega total, since the one factor accounts for all common variance; and ECV and PUC would both be 1 by construction, which reflects the number of factors in the model rather than evidence of unidimensionality.

Each coefficient answers a different question:

• Omega hierarchical: can the total score be read as a measure of one construct?

• Omega subscale: does a subscale score add anything beyond the general factor?

• The H index: is a factor well defined by its indicators?

• ECV together with PUC: is a unidimensional model defensible?

Alpha assumes essentially tau-equivalent items. Factor analysis instead yields congeneric solutions, for which alpha is only a lower bound. For a multidimensional scale, alpha is rarely the coefficient to report (Gignac, 2014).

Composite reliability and average variance extracted (AVE) are not among the coefficients this function computes. Use semTools::compRelSEM() and semTools::AVE() to compute them from a lavaan fit.

The common-variance indices ECV and PUC (Bonifay et al., 2015; Reise et al., 2013; Rodriguez et al., 2016a, 2016b) describe the general factor, so they are reported for the general factor only. ECV is the share of the common variance explained by the general factor. PUC is the proportion of correlations that reflect general-factor variance alone – correlations between indicators of different group factors. The higher the PUC, the more the general factor resembles the single factor of a unidimensional model.

Input

efa_reliability() reads several kinds of input, illustrated in the examples below.

• An oblique efa_fit() solution is scored as the correlated-factors model it is. It has no general factor, so it omits the bifactor indices (omega hierarchical, ECV, and PUC). Its whole-scale row is labelled "total" rather than "g".

• An efa_schmid_leiman() solution – or a schmid object from psych::schmid(), or a raw bifactor loading matrix with the general factor in its first column – is scored as a bifactor solution, with the whole-scale row labelled "g". Indicator-to-factor correspondences come from factor_map if supplied (see factor_map below). Without one, an efa_schmid_leiman() or psych::schmid() solution is mapped by each variable's strongest group loading. A raw bifactor matrix instead defaults to its exact zero pattern – supply factor_map explicitly if that matrix has no exact zeros (e.g. an estimated rather than a target matrix).

• A lavaan fit – single-factor, correlated-factors, second-order, or bifactor – is scored per its structure. The structure is detected automatically, not taken from g_name. The general-factor coefficients need the latent factors to be uncorrelated: fit a bifactor model with orthogonal = TRUE (lavaan's default leaves the factors correlated), and leave a second-order model's first-order factor covariances at zero. A fit whose factors correlate is rejected rather than scored as though they did not. variance is not used for lavaan input: its composite variances are always model-implied, computed separately per group for a multiple-group fit.

• A pattern matrix with its Phi – for example, the loadings and Phi of an oblique efa_fit() solution – is scored as the correlated-factors solution it represents. Uniquenesses are derived from the loadings under Phi, unless u2 is given. Supply the pattern matrix, not the structure matrix – the structure matrix would be read as a pattern too, giving the wrong result. Unlike a fitted solution, this pair carries no correlation matrix. variance = "correlation" then needs one, supplied in cormat, and errors without it. variance = "sums_load" needs none.

A one-factor solution – a one-column loading matrix, a single-factor lavaan fit, a single-factor efa_fit() solution, or single-factor components – is scored the same way regardless of route (see Coefficients above for what it returns). Its row is labelled with the factor's own name, with fac_names if supplied, or with "F1" if neither names it; it is never labelled "g".

Manually supplied components (g_load, s_load, u2, var_names, Phi) follow the same reading rules as the matrix routes above. Do not pass a correlation matrix as s_load (or as model, for the matrix routes) – supply it as cormat instead.

Value

An object of class efa_reliability: a long-format data frame with one row per computed coefficient, with columns

coefficient	the coefficient name (e.g. "omega_total").
level	"general" for the general-factor row, "total" for the whole-scale row of a correlated-factors solution, and "group" for every other row. A single-factor solution has only the general-factor row.
factor	the factor label: "g" for the general factor of a solution with group factors. A single-factor solution's one factor takes its own name, or "F1" if neither the input nor fac_names names it. A correlated-factors solution has no general factor, so its first row is labelled "total" instead – it describes the composite of every variable, not a factor of the model.
group	the group label, or NA for a single ungrouped solution.
value	the coefficient value.

Structurally undefined cells (for example, ECV and PUC on a group factor) are omitted. The object also carries a settings attribute (the total-variance convention used, and whether the solution has a general factor) and a kind attribute tagging each coefficient as a reliability coefficient or a common-variance index. It has a print.efa_reliability() method.

Source

McDonald, R. P. (1978). Generalizability in factorable domains: Domain validity and generalizability. Educational and Psychological Measurement, 38, 75-79.

McDonald, R. P. (1985). Factor analysis and related methods. Hillsdale, NJ: Erlbaum.

McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.

Gignac, G. E. (2014). On the inappropriateness of using items to calculate total scale score reliability via coefficient alpha for multidimensional scales. European Journal of Psychological Assessment, 30, 130-139.

Flora, D. B. (2020). Your coefficient alpha is probably wrong, but which coefficient omega is right? A tutorial on using R to obtain better reliability estimates. Advances in Methods and Practices in Psychological Science, 3, 484-501.

Green, S. B., & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74, 155-167.

Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach's alpha, Revelle's beta, and McDonald's omega H: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70, 123-133.

Zinbarg, R. E., Yovel, I., Revelle, W., & McDonald, R. P. (2006). Estimating generalizability to a latent variable common to all of a scale's indicators: A comparison of estimators for omega H. Applied Psychological Measurement, 30, 121-144.

Hancock, G. R., & Mueller, R. O. (2001). Rethinking construct reliability within latent variable systems. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future - A Festschrift in honor of Karl Jöreskog (pp. 195-216). Lincolnwood, IL: Scientific Software International.

Bonifay, W. E., Reise, S. P., Scheines, R., & Meijer, R. R. (2015). When are multidimensional data unidimensional enough for structural equation modeling? An evaluation of the DETECT multidimensionality index. Structural Equation Modeling, 22, 504-516.

Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73, 5-26.

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016a). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment, 98, 223-237.

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016b). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21, 137-150.

See Also

efa_fit() for the solution these are computed from, and OMEGA(), the superseded function that returns these same coefficients in a wide, per-factor layout.

Other reliability coefficients: efa_schmid_leiman(), print.efa_reliability()

Examples

## From an oblique EFA (correlated-factors) solution. With no factor_map, each
## item is auto-assigned to its highest-loading factor.
efa_mod <- efa_fit(test_models$baseline$cormat, N = 500, n_factors = 3,
                   estimator = "PAF", rotation = "promax")
efa_reliability(efa_mod)

## From a Schmid-Leiman solution, with an explicit indicator-to-factor map.
sl_mod <- efa_schmid_leiman(efa_mod, estimator = "PAF")
fc <- sl_mod$sl[, c("F1", "F2", "F3")] >= .2
efa_reliability(sl_mod, factor_map = fc)

## Request a subset of the coefficients only.
efa_reliability(sl_mod, factor_map = fc,
                coefficients = c("omega_total", "alpha"))

## From an oblique pattern matrix and its factor intercorrelations. This is
## the same correlated-factors solution, and gives the same coefficients.
efa_reliability(efa_mod$rot_loadings, Phi = efa_mod$Phi,
                cormat = test_models$baseline$cormat)

## From lavaan fits: a bifactor solution, and a correlated-factors one.

if (requireNamespace("lavaan", quietly = TRUE)) {
mod_cf <- 'F1 =~ V1 + V2 + V3 + V4 + V5 + V6
           F2 =~ V7 + V8 + V9 + V10 + V11 + V12
           F3 =~ V13 + V14 + V15 + V16 + V17 + V18'
mod <- paste(mod_cf, 'g =~ V1 + V2 + V3 + V4 + V5 + V6 + V7 + V8 + V9 + V10 +
                           V11 + V12 + V13 + V14 + V15 + V16 + V17 + V18',
             sep = "\n")
fit <- lavaan::cfa(mod, sample.cov = test_models$baseline$cormat,
                   sample.nobs = 500, estimator = "ml", orthogonal = TRUE)
print(efa_reliability(fit, g_name = "g"))

## No general factor: omega hierarchical, ECV, and PUC are omitted.
fit_cf <- lavaan::cfa(mod_cf, sample.cov = test_models$baseline$cormat,
                      sample.nobs = 500, estimator = "ml")
efa_reliability(fit_cf)
}

EFAtools documentation built on Aug. 21, 2026, 5:16 p.m.