compRelSEM: Composite Reliability using SEM
In semTools: Useful Tools for Structural Equation Modeling
compRelSEM R Documentation
Composite Reliability using SEM
Description
Calculate composite reliability from estimated factor-model parameters
Usage
compRelSEM(object, W = NULL, return.total = FALSE, obs.var = TRUE,
tau.eq = FALSE, ord.scale = TRUE, shared = character(0),
config = character(0), add.IRR = FALSE, higher = character(0),
true = list(), dropSingle = TRUE, omit.factors = character(0),
omit.indicators = character(0), omit.imps = c("no.conv", "no.se"),
simplify = FALSE, return.df = simplify)
Arguments
object
A lavaan::lavaan or lavaan.mi::lavaan.mi object,
expected to contain only exogenous common factors (i.e., a CFA model).
W
Composite weights applied to observed variables prior to summing.
By default (NULL), unit-weights are applied to all indicators per factor
(as well as all modeled indicators when return.total=TRUE), which is
equivalent to specifying equal weights of any value to each indicator.
Weights can be a character string specifying any number of composites
using lavaan::model.syntax(), in the form COMPOSITE <~ weight*indicator
(any indicator without a numeric weight is given a unit weight = 1).
See Details and Examples about complicated CFAs (e.g., multilevel,
higher-order, or bifactor).
return.total
For multidimensional CFAs, this logical value
indicates whether to return a final index for the reliability of a
composite of all modeled indicators (labeled .TOTAL.). This is redundant
whenever there is already a common factor indicated by all items (e.g.,
the general factor in a bifactor model). This argument is ignored when
using the W= argument to specify composites (optionally with weights).
Setting a negative value (e.g., -1) returns only the .TOTAL.
composite reliability (i.e., excluding coefficients per factor).
obs.var
logical indicating whether to compute reliability
using observed (co)variances to compute the denominator. Setting FALSE
triggers using model-implied (co)variances to compute the denominator.
tau.eq
logical indicating whether to assume (essential)
tau-equivalence by calculating coefficient \alpha (on observed or
model-implied (co)variances, depending on obs.var=).
Triggers error if requested in combination with unequal weights in W=.
Setting FALSE (default) yields an "\omega"-type coefficient.
Optionally, a character vector of composite names can specify
calculating coefficient \alpha for a subset of all composites.
ord.scale
logical relevant only for composites of discrete items.
Setting TRUE (default) applies Green and Yang's (2009, formula 21)
method to calculate reliability of the actual composite (i.e., on the
actual ordinal response scale). Setting FALSE yields coefficients that
are only interpretable on the continuous latent-response scale, which can
be interpreted as the upper bound of reliability if items were more
approximately continuous.
Ignored for factors with continuous indicators.
Reliability cannot currently be calculated for composites of both
discrete and continuous indicators.
shared
character vector of composite names, to be interpreted
as representing (perhaps multidimensional) shared construct(s).
Lai's (2021) coefficient \omega^\textrm{B} or \alpha^\textrm{B}
is calculated to quantify reliability relative to error associated with
both indicators (measurement error) and subjects (sampling error), like a
generalizability coefficient. For purely scale reliability (relative
to item/measurement error alone, i.e., Lai's \omega^\textrm{2L}),
omit the composite(s) from the shared= argument.
config
Deprecated character vector.
add.IRR
logical indicating whether to calculate an additional
reliability coefficient for any composite listed in shared=. Given that
subjects can be considered as raters of their cluster's shared construct,
an interrater reliability (IRR) coefficient can quantify reliability
relative to rater/sampling error alone.
higher
Deprecated, supplanted by using the true= argument.
true
Optional list of character vectors, with list-element names
corresponding to composite names. Each composite can have a character
vector with names of any common factor(s) that should be considered the
source(s) of "true-score variance" in that composite. For any composite
with a specification in true=, the default is to consider all common
factors to contribute true-score variance to any items in the composite.
Specifying a composite in true= is only necessary to deviate from this
default, for example, to specify the "general" factor in a bifactor model,
in order to obtain "hierarchical omega" (\omega_\textrm{H}).
A shortcut for this is available when W=NULL, by specifying a single
character string (one of "omegaH", "omega.h", or "omega_h")
instead of a list.
dropSingle
When W=NULL, this logical indicates whether to exclude
single-indicator factors from the list of default composites.
Even when TRUE (default), single indicators are still included in
the .TOTAL. composite when return.total = TRUE.
omit.factors
Deprecated, supplanted by using the true= argument.
omit.indicators
Deprecated, supplanted by using the W= argument.
omit.imps
character vector specifying criteria for omitting
imputations from pooled results (using lavaan.mi::lavaan.mi).
Can include any of c("no.conv", "no.se", "no.npd"), the first 2 of which
are the default setting, which excludes any imputations that did not
converge or for which standard errors could not be computed. The
last option ("no.npd") would exclude any imputations which
yielded a nonpositive definite covariance matrix for observed or
latent variables, which would include any "improper solutions" such
as Heywood cases. NPD solutions are not excluded by default because
they are likely to occur due to sampling error, especially in small
samples. However, gross model misspecification could also cause
NPD solutions. Users can compare pooled results with and without
this setting as a sensitivity analysis to see whether some
imputations warrant further investigation.
simplify
logical indicating whether to return reliability
coefficients in a numeric vector (for single-group model) or data.frame
(one row per group, or per level in some cases).
Specifying a negative number (simplify = -1L) additionally removes the
informative headers printed to facilitate interpretation.
return.df
Deprecated logical argument, replaced by simplify=.
Details
Several coefficients for factor-analysis reliability have been termed
"omega", which Cho (2021) argues is a misleading misnomer and argues for
using \rho to represent them all, differentiated by descriptive
subscripts. In our package, we strive to provide unlabeled coefficients,
leaving it to the user to decide on a label in their report. But we do
use the symbols \alpha and \omega in the formulas below in order
to distinguish coefficients that do (not) assume essential tau-equivalence.
Bentler (1968) first introduced factor-analysis reliability for a
unidimensional factor model with congeneric indicators, labeling the
coefficients \alpha. McDonald (1999) later referred to this
and other reliability coefficients, first as \theta (in 1970),
then as \omega, which is a source of confusion when reporting
coefficients (Cho, 2021). Coefficients based on factor models were later
generalized to account for multidimenisionality (possibly with
cross-loadings) and correlated errors. The general \omega formula
implemented in this function is:
\omega=\frac{\bold{w}^{\prime} \Lambda \Phi \Lambda^{\prime} \bold{w}
}{ \bold{w}^{\prime} \hat{\Sigma} \bold{w} },
where \hat{\Sigma} can be the model-implied covariance matrix from
either the saturated model (i.e., the "observed" covariance matrix, used by
default) or from the hypothesized CFA model, controlled by the obs.var=
argument. All elements of matrices in the numerator and denominator are
effectively summed by the multiplication of the outer terms \bold{w},
a k-dimensional vector of composite weights typically consisting of
\bold{1}s, unless otherwise specified with the W= argument), and
k is the number of variables in the composite. Reliability of subscale
composites (or simply for separate factors in a joint CFA) can be calculated
by setting omitted-indicator weights to 0. For unidimensional constructs
with simple structure, the equation above is often simplified to a scalar
representation (e.g., McDonald, 1999, Eq. 6.20b):
\omega = \frac{ \left( \sum^{k}_{i = 1} \lambda_i \right)^{2}
Var\left( \psi \right) }{ \left( \sum^{k}_{i = 1} \lambda_i \right)^{2}
Var\left( \psi \right) + \sum^{k}_{i = 1} \theta_{ii} },
Note that all coefficients are calculated from total factor variances:
lavInspect(object, "cov.lv"), which assumes the fitted object= is a CFA,
not a full SEM with latent regression slopes. If there is a Beta matrix, it
should only contain higher-order factor loadings (see details below).
When the fitted CFA imposes constraints consistent with (essential)
tau-equivalence, \omega is equivalent to coefficient \alpha
(Cronbach, 1951):
\alpha = \frac{k}{k - 1}\left[ 1 -
\frac{ \textrm{tr} \left( \hat{\Sigma} \right)
}{ \bold{1}^{\prime} \hat{\Sigma} \bold{1} }
\right],
where \textrm{tr} \left( . \right) is the trace operation (i.e., the
sum of diagonal elements). Setting tau.eq=TRUE triggers the application of
this formula (rather than \omega above) to the model-implied or
observed covariance matrix (again controlled by the obs.var= argument).
Higher-Order Factors:
For higher-order constructs with latent indicators, only \omega is
available because \alpha was not derived from CFA parameters (although
it can be expressed in a particular restricted CFA specification).
The reliability of a composite that represents a higher-order construct
requires partitioning the model-implied factor covariance matrix \Phi
in order to isolate the common-factor variance associated only with the
higher-order factor. Using a second-order factor model, the model-implied
covariance matrix of observed indicators \hat{\Sigma} can be
partitioned into 3 sources:
the second-order common-factor (co)variance:
\Lambda \bold{B} \Phi_2 \bold{B}^{\prime} \Lambda^{\prime}
the residual variance of the first-order common factors (i.e., not
accounted for by the second-order factor):
\Lambda \Psi_{u} \Lambda^{\prime}
the measurement error of observed indicators: \Theta
where \Lambda contains first-order factor loadings, \bold{B}
contains second-order factor loadings, \Phi_2 is the model-implied
covariance matrix of the second-order factor(s), and \Psi_{u} is the
covariance matrix of first-order factor disturbances. In practice, we can
use the full \bold{B} matrix and full model-implied \Phi matrix
(i.e., including all latent factors) because the zeros in \bold{B}
will cancel out unwanted components of \Phi. Thus, we can calculate
the proportion of variance of a composite score that is attributable to the
second-order factor:
\omega=\frac{\bold{w}^{\prime} \Lambda \bold{B} \Phi \bold{B}^{\prime}
\Lambda^{\prime} \bold{w} }{ \bold{w}^{\prime} \hat{\Sigma} \bold{w}},
where \bold{w}, \hat{\Sigma}, and k are defined as above.
Note that if a higher-order factor also has observed indicators, it is
necessary to model the observed indicators as single-indicator lower-order
constructs, so that all of the higher-order factor indicators are latent
(with loadings in the Beta matrix, not Lambda); otherwise, higher-order
factor variance in the observed indicator is not captured in the numerator.
Bifactor or Multitrait–Multimethod (MTMM) Models:
These multidimensional models partition sources of common variance that are
due to the factor of interest (e.g., a trait) as well as non-target factors
(e.g., "method factors", such as item wording or type of respondent).
The latter can be considered as systematic (i.e., non-random) sources of
error, to be excluded from the numerator of a reliability coefficient,
yielding so-called "hierarchical omega" (\omega_\textrm{H}). On the
other hand, non-target variance that can be expected in repeated measurement
meets the classical test theory definition of reliability. Including method
factors in the numerator yields so-called "omega total"
(\omega_\textrm{T}), which is the default approach in compRelSEM()
because it is consistent with the classical test theory definition of
reliability. However, users can obtain \omega_\textrm{H} for a
composite by using the true= argument to specify any factor(s) to be
treated as representing true scores. The same approach can be taken to
obtain the proportion of a (sub)scale composite's variance due to method
factors (by listing those in true=), if that is of interest.
Categorical Indicators:
When all indicators (per composite) are ordinal, a CFA can be fitted that
includes a threshold model (sometimes called Item Factor Analysis: IFA),
which assumes a normally distributed latent response underlies each observed
ordinal response. Despite making this assumption, a composite of ordinal
items can only be calculated by assigning numerical values to the ordinal
categories, so that the pseudo-numerical variables can be summed into a
composite variable that is more approximately continuous than its items.
Applying the formulas above to IFA parameters provides the
hypothetical reliability of a composite of latent responses: a composite
which cannot be calculated in practice. Nonetheless, this hypothetical
reliability can be interpreted as an estimate of what reliability could be
if a more approximately continuous response scale were used (e.g., with
sufficiently many response categories that the standardized solutions are
equivalent between a fitted IFA and a fitted CFA that treats the ordinal
responses as numeric; Chalmers, 2018). This can be requested by setting
ord.scale=FALSE, in which case \hat\Sigma in the formulas above
is a polychoric correlation matrix.
When ord.scale=FALSE and tau.eq=TRUE, this results in what Zumbo et al.
(2007) termed "ordinal \alpha" (see criticisms by Chalmers, 2018, and
and a rejoinder by Zumbo & Kroc, 2019).
Alternatively, Green and Yang (2009, Eq. 21) derived a method to calculate
model-based reliability (\omega) from IFA parameters (i.e.,
incorporating the latent-response assumption) but that applies to the actual
(i.e., ordinal) observed response scale (the default: ord.scale=TRUE).
Lu et al. (2020) showed how to incorporate unequal weights into Green and
Yang's (2009) formula, so W= can be used to estimate the (maximal)
reliability of a weighted composite of ordinal variables.
However, combining ord.scale=TRUE with tau.eq=TRUE is not available.
For \alpha to be interpretable on the observed ordinal scale,
users must choose whether to (a) release the latent-response assumption, by
fitting a CFA without a threshold model, or (b) fit an IFA model with
constraints consistent with the assumption of (essential) tau-equivalence
(i.e., equal factor loadings).
No method analogous to Green and Yang (2009, Eq. 21) has yet been proposed
to calculate reliability with a mixture of categorical and continuous
indicators, so any such composite is skipped with a warning.
Multilevel Measurement Models:
How to define reliability coefficients for scales employed in nested designs
is an ongoing topic of methodological development, with some ongoing
controversies about best practice when the target of measurement is the
"cluster" or between-level (i.e., Level 2 in a 2-level design).
Geldhof et al. (2014) proposed applying the standard formulas above to each
level's CFA parameters and/or (model-implied) covariance matrix, whereas
Lai (2021) proposed different formulas that account for all sources of
variance in composites of observed variables.
There is no controversy about how to define a within-level reliability,
coefficient, which can be interpreted as the reliability of a composite
calculated by first centering each indicator around its cluster mean, then
calculating the composite from the cluster-mean-centered items. Equivalently
(i.e., the same formula), this can be interpreted as the hypothetical
reliability of a composite of the items' latent Level-1 components. This
coefficient can be requested with lavaan::model.syntax (to pass to the
W= argument) that specifies a composite in a Level-1 "block", which not
have the same name as any composite in the Level-2 block. If users do not
use W= (i.e., calculate a reliability index per modeled common factor),
then this can be accomplished by using unique factor names across levels.
This contrasts with reliability indices for between-level composites:
The reliability of a hypothetical composite of items' latent between-level
components (using formulas proposed by Geldhof et al., 2014) is not
equivalent to the coefficient for a composite of items' observed cluster
means, using generalizations of formulas proposed by Lai (2021):
\omega^\textrm{B} =
\frac{\bold{w}^{\prime} \Lambda^\textrm{B} \Phi^\textrm{B} \Lambda^{\textrm{B}\prime} \bold{w}
}{ \bold{w}^{\prime} \hat{\Sigma}^\textrm{B} \bold{w} +
\frac{1}{\tilde{n}_\textrm{clus}} \left(
\bold{w}^{\prime} \hat{\Sigma}^\textrm{W} \bold{w} \right) },
\alpha^\textrm{B} = \frac{2k}{k - 1}\left[
\frac{ \sum^{k}_{i=2} \sum^{i-1}_{j=1} \hat\sigma^\textrm{B}_{ij}
}{ \bold{1}^{\prime} \hat\Sigma^\textrm{B} \bold{1} +
\frac{1}{\tilde{n}_\textrm{clus}} \left(
\bold{1}^{\prime} \hat\Sigma^\textrm{W} \bold{1} \right) }
\right],
where \tilde{n}_\textrm{clus} is the harmonic-mean cluster size, and
superscripts B and W indicate between- and within-level parameters.
Obtaining these estimates of composite reliability requires fitting a
2-level CFA that provides the same factor structure and factor names in
the models at both levels (following the advice of Jak et al., 2021), as
well as the same composite name in both levels/blocks of syntax passed to
W= (if used). Furthermore, the between-level composite name must be
passed to the shared= argument; otherwise, the same factor/composite name
across levels will yield Lai's (2021) coefficient for a configural construct
(see Examples):
\omega^\textrm{2L} =
\frac{\bold{w}^{\prime} \left(
\Lambda^\textrm{W} \Phi^\textrm{W} \Lambda^{\textrm{W}\prime} +
\Lambda^\textrm{B} \Phi^\textrm{B} \Lambda^{\textrm{B}\prime}
\right) \bold{w}
}{ \bold{w}^{\prime} \hat\Sigma^\textrm{B} \bold{w} +
\bold{w}^{\prime} \hat\Sigma^\textrm{W} \bold{w} },
\alpha^\textrm{2L} = \frac{2k}{k - 1}\left[
\frac{ \sum^{k}_{i=2} \sum^{i-1}_{j=1} \left( \hat\sigma^\textrm{W}_{ij} +
\hat\sigma^\textrm{B}_{ij} \right)
}{ \bold{1}^{\prime} \hat\Sigma^\textrm{B} \bold{1} +
\bold{1}^{\prime} \hat\Sigma^\textrm{W} \bold{1} }
\right],
This can be interpreted as the scale-reliability coefficient ignoring the
nested design, as both the common-factor variance of the Level-1 factor
and of its Level-2 cluster means are treated as true-score variance.
Note that Lai's (2021) between-level reliability coefficients for a
shared construct quantify generalizability across both indicators and
raters (i.e., subjects rating their cluster's construct).
Lüdtke et al. (2011) refer to these as measurement error and sampling error,
respectively. From this perspective (and following from generalizability
theory), an IRR coefficient can also be calculated:
\textrm{IRR} =
\frac{\bold{w}^{\prime} \left( \hat{\Sigma}^\textrm{B} \right) \bold{w}
}{ \bold{w}^{\prime} \hat\Sigma^\textrm{B} \bold{w} +
\bold{w}^{\prime} \hat\Sigma^\textrm{W} \bold{w} },
which quantifies generalizability across rater/sampling-error only, and can
be returned for any shared= construct's composite by setting add.IRR=TRUE.
Value
By default (simplify=FALSE) a list of numeric vectors (1 per
composite) is returned. In multigroup CFA, the vector contains a reliability
index for each group in which the composite can be computed.
Each composite's vector has a attr(..., "header") with information to
facilitate interpretation of that index:
A list of variables in the composite, which determines the
composite's total variance (denominator of reliability)
Whether that total variance (denominator) is determined from
the restricted model (i.e., CFA parameters) or unrestricted
model (i.e., a freely estimated covariance matrix)
Whether the variables in the composite are (a transformation
of) observed variables, or whether they are latent
(components of) variables. The latter (e.g., latent responses
assumed to underlie observed ordinal indicators, or latent
level-specific components of variables in a multilevel CFA)
cannot be used to calculated an observed composite variable,
so the resulting coefficient should be cautiously interpreted
as a "hypothetical reliability" (Chalmers, 2018; Lai, 2021).
The latent variables that contribute common-factor variance
to the composite, which determine the composite's
"true-score" variance (numerator of reliability)
Which reliability formula was used: model-based reliability
(so-called "omega") or coefficient alpha (a model-free
lower-bound estimate of true reliability, equivalent to
a model-based reliability that assumes tau-equivalence)
This header will be printed immediately above each composite's
reliability coefficient. When multiple reliability coefficients are
returned, and each vector in the list has the same length, then
setting simplify=TRUE will collect the list of single
coefficients into a vector, or the list of multiple coefficients
into a data.frame, and their headers will be concatenated to be
printed above the coefficients. Setting simplify = -1L (or any
negative number) will omit the informative headers.
Author(s)
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Uses hidden functions to implement Green & Yang's (2009) reliability for
categorical indicators, written by Sunthud Pornprasertmanit
(psunthud@gmail.com) for the deprecated reliability() function.
References
Bentler, P. M. (1968). Alpha-maximized factor analysis (alphamax): Its
relation to alpha and canonical factor analysis. Psychometrika, 33(3),
335–345. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02289328")}
Chalmers, R. P. (2018). On misconceptions and the limited usefulness of
ordinal alpha. Educational and Psychological Measurement, 78(6),
1056–1071. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0013164417727036")}
Cho, E. (2021) Neither Cronbach’s alpha nor McDonald’s omega: A commentary
on Sijtsma and Pfadt. Psychometrika, 86(4), 877–886.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-021-09801-1")}
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of
tests. Psychometrika, 16(3), 297–334. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02310555")}
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability
estimation in a multilevel confirmatory factor analysis framework.
Psychological Methods, 19(1), 72–91. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/a0032138")}
Green, S. B., & Yang, Y. (2009). Reliability of summed item scores using
structural equation modeling: An alternative to coefficient alpha.
Psychometrika, 74(1), 155–167. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-008-9099-3")}
Jak, S., Jorgensen, T. D., & Rosseel, Y. (2021). Evaluating cluster-level
factor models with lavaan and Mplus. Psych, 3(2),
134–152. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/psych3020012")}
Lai, M. H. C. (2021). Composite reliability of multilevel data: It’s about
observed scores and construct meanings. Psychological Methods, 26(1),
90–102. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000287")}
Lu, Z., Hong, M., & Kim, S. (2021). Formulas of multilevel reliabilities for
tests with ordered categorical responses.
In M. Wiberg, D. Molenaar, J. González, U.Böckenholt, & J.-S. Kim (Eds.),
Quantitative psychology: The 85th annual meeting of the Psychometric Society, Virtual
(pp. 103–112). Springer. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-030-74772-5_10")}
Lüdtke, O., Marsh, H. W., Robitzsch, A., & Trautwein, U. (2011).
A 2 \times 2 taxonomy of multilevel latent contextual models:
Accuracy–bias trade-offs in full and partial error correction models.
Psychological Methods, 16(4), 444–467. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/a0024376")}
McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ:
Erlbaum.
Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of
coefficients alpha and theta for Likert rating scales.
Journal of Modern Applied Statistical Methods, 6(1), 21–29.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.22237/jmasm/1177992180")}
Zumbo, B. D., & Kroc, E. (2019). A measurement is a choice and Stevens’
scales of measurement do not help make it: A response to Chalmers.
Educational and Psychological Measurement, 79(6), 1184–1197.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0013164419844305")}
See Also
maximalRelia() for the maximal reliability of weighted composite
Examples
data(HolzingerSwineford1939)
HS9 <- HolzingerSwineford1939[ , paste0("x",1:9)]
HSbinary <- as.data.frame( lapply(HS9, cut, 2, labels=FALSE) )
names(HSbinary) <- paste0("y",1:9)
HS <- cbind(HolzingerSwineford1939, HSbinary)
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ y7 + y8 + y9 '
fit <- cfa(HS.model, data = HS, ordered = c("y7","y8","y9"), std.lv = TRUE)
fitg <- cfa(HS.model, data = HS, ordered = c("y7","y8","y9"), std.lv = TRUE,
group = "school")
## works for factors with exclusively continuous OR categorical indicators
compRelSEM(fit)
compRelSEM(fitg)
## reliability for composite of ALL indicators only available when they are
## all continuous or all categorical. The example below calculates a
## composite of continuous items from 2 factors (visual and textual)
## using the custom-weights syntax (note the "<~" operator)
w.tot <- '
visual <~ x1 + x2 + x3
textual <~ x4 + x5 + x6
total <~ x1 + x2 + x3 + x4 + x5 + x6
'
compRelSEM(fit, W = w.tot)
## ----------------------
## Higher-order construct
## ----------------------
## Reliability of a composite that represents a higher-order factor
mod.hi <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
general =~ visual + textual + speed '
fit.hi <- cfa(mod.hi, data = HolzingerSwineford1939)
## "general" is the factor representing "true scores", but it has no
## observed indicators. Must use custom-weights syntax:
compRelSEM(fit.hi, W = 'g <~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9')
## ----------------------
## Hierarchical omega
## and omega Total
## ----------------------
mod.bi <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
general =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 '
fit.bi <- cfa(mod.bi, data = HolzingerSwineford1939,
orthogonal = TRUE, std.lv = TRUE)
compRelSEM(fit.bi, return.total = -1) # omega_Total
compRelSEM(fit.bi, return.total = -1, # omega_Hierarchical
true = list(.TOTAL. = "general"))
## ----------------------
## Multilevel Constructs
## ----------------------
## Same factor structure with metric invariance across levels (Jak et al., 2021)
model2 <- '
level: 1
f1 =~ y1 + L2*y2 + L3*y3
f2 =~ y4 + L5*y5 + L6*y6
level: 2
f1 =~ y1 + L2*y2 + L3*y3
f2 =~ y4 + L5*y5 + L6*y6
'
fit2 <- sem(model2, data = Demo.twolevel, cluster = "cluster")
## Lai's (2021, Eq. 13) omega index for a configural (Level-1) construct,
## treating common-factor variance at both levels as "true" variance
compRelSEM(fit2)
## Lai's (2021, Eq. 17) omega index for a shared (Level-2) construct
## (also its interrater reliability coefficient)
compRelSEM(fit2, shared = c("f1","f2"), add.IRR = TRUE)
## Geldhof et al.'s (2014) level-specific indices imply a different
## composite (hypothetically) calculated per level. Thus, use
## unique composite names per level.
W2.Geldhof <- ' level: 1
F1w <~ y1 + y2 + y3
F2w <~ y4 + y5 + y6
level: 2
F1b <~ y1 + y2 + y3
F2b <~ y4 + y5 + y6
'
compRelSEM(fit2, W = W2.Geldhof)
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| compRelSEM | R Documentation |
Composite Reliability using SEM
Description
Calculate composite reliability from estimated factor-model parameters
Usage
compRelSEM(object, W = NULL, return.total = FALSE, obs.var = TRUE,
tau.eq = FALSE, ord.scale = TRUE, shared = character(0),
config = character(0), add.IRR = FALSE, higher = character(0),
true = list(), dropSingle = TRUE, omit.factors = character(0),
omit.indicators = character(0), omit.imps = c("no.conv", "no.se"),
simplify = FALSE, return.df = simplify)
Arguments
object |
A lavaan::lavaan or lavaan.mi::lavaan.mi object, expected to contain only exogenous common factors (i.e., a CFA model). |
W |
Composite weights applied to observed variables prior to summing.
By default ( |
return.total |
For multidimensional CFAs, this |
obs.var |
|
tau.eq |
|
ord.scale |
|
shared |
|
config |
Deprecated |
add.IRR |
|
higher |
Deprecated, supplanted by using the |
true |
Optional |
dropSingle |
When |
omit.factors |
Deprecated, supplanted by using the |
omit.indicators |
Deprecated, supplanted by using the |
omit.imps |
|
simplify |
|
return.df |
Deprecated |
Details
Several coefficients for factor-analysis reliability have been termed
"omega", which Cho (2021) argues is a misleading misnomer and argues for
using \rho to represent them all, differentiated by descriptive
subscripts. In our package, we strive to provide unlabeled coefficients,
leaving it to the user to decide on a label in their report. But we do
use the symbols \alpha and \omega in the formulas below in order
to distinguish coefficients that do (not) assume essential tau-equivalence.
Bentler (1968) first introduced factor-analysis reliability for a
unidimensional factor model with congeneric indicators, labeling the
coefficients \alpha. McDonald (1999) later referred to this
and other reliability coefficients, first as \theta (in 1970),
then as \omega, which is a source of confusion when reporting
coefficients (Cho, 2021). Coefficients based on factor models were later
generalized to account for multidimenisionality (possibly with
cross-loadings) and correlated errors. The general \omega formula
implemented in this function is:
\omega=\frac{\bold{w}^{\prime} \Lambda \Phi \Lambda^{\prime} \bold{w}
}{ \bold{w}^{\prime} \hat{\Sigma} \bold{w} },
where \hat{\Sigma} can be the model-implied covariance matrix from
either the saturated model (i.e., the "observed" covariance matrix, used by
default) or from the hypothesized CFA model, controlled by the obs.var=
argument. All elements of matrices in the numerator and denominator are
effectively summed by the multiplication of the outer terms \bold{w},
a k-dimensional vector of composite weights typically consisting of
\bold{1}s, unless otherwise specified with the W= argument), and
k is the number of variables in the composite. Reliability of subscale
composites (or simply for separate factors in a joint CFA) can be calculated
by setting omitted-indicator weights to 0. For unidimensional constructs
with simple structure, the equation above is often simplified to a scalar
representation (e.g., McDonald, 1999, Eq. 6.20b):
\omega = \frac{ \left( \sum^{k}_{i = 1} \lambda_i \right)^{2}
Var\left( \psi \right) }{ \left( \sum^{k}_{i = 1} \lambda_i \right)^{2}
Var\left( \psi \right) + \sum^{k}_{i = 1} \theta_{ii} },
Note that all coefficients are calculated from total factor variances:
lavInspect(object, "cov.lv"), which assumes the fitted object= is a CFA,
not a full SEM with latent regression slopes. If there is a Beta matrix, it
should only contain higher-order factor loadings (see details below).
When the fitted CFA imposes constraints consistent with (essential)
tau-equivalence, \omega is equivalent to coefficient \alpha
(Cronbach, 1951):
\alpha = \frac{k}{k - 1}\left[ 1 -
\frac{ \textrm{tr} \left( \hat{\Sigma} \right)
}{ \bold{1}^{\prime} \hat{\Sigma} \bold{1} }
\right],
where \textrm{tr} \left( . \right) is the trace operation (i.e., the
sum of diagonal elements). Setting tau.eq=TRUE triggers the application of
this formula (rather than \omega above) to the model-implied or
observed covariance matrix (again controlled by the obs.var= argument).
Higher-Order Factors:
For higher-order constructs with latent indicators, only \omega is
available because \alpha was not derived from CFA parameters (although
it can be expressed in a particular restricted CFA specification).
The reliability of a composite that represents a higher-order construct
requires partitioning the model-implied factor covariance matrix \Phi
in order to isolate the common-factor variance associated only with the
higher-order factor. Using a second-order factor model, the model-implied
covariance matrix of observed indicators \hat{\Sigma} can be
partitioned into 3 sources:
the second-order common-factor (co)variance:
\Lambda \bold{B} \Phi_2 \bold{B}^{\prime} \Lambda^{\prime}the residual variance of the first-order common factors (i.e., not accounted for by the second-order factor):
\Lambda \Psi_{u} \Lambda^{\prime}the measurement error of observed indicators:
\Theta
where \Lambda contains first-order factor loadings, \bold{B}
contains second-order factor loadings, \Phi_2 is the model-implied
covariance matrix of the second-order factor(s), and \Psi_{u} is the
covariance matrix of first-order factor disturbances. In practice, we can
use the full \bold{B} matrix and full model-implied \Phi matrix
(i.e., including all latent factors) because the zeros in \bold{B}
will cancel out unwanted components of \Phi. Thus, we can calculate
the proportion of variance of a composite score that is attributable to the
second-order factor:
\omega=\frac{\bold{w}^{\prime} \Lambda \bold{B} \Phi \bold{B}^{\prime}
\Lambda^{\prime} \bold{w} }{ \bold{w}^{\prime} \hat{\Sigma} \bold{w}},
where \bold{w}, \hat{\Sigma}, and k are defined as above.
Note that if a higher-order factor also has observed indicators, it is
necessary to model the observed indicators as single-indicator lower-order
constructs, so that all of the higher-order factor indicators are latent
(with loadings in the Beta matrix, not Lambda); otherwise, higher-order
factor variance in the observed indicator is not captured in the numerator.
Bifactor or Multitrait–Multimethod (MTMM) Models:
These multidimensional models partition sources of common variance that are
due to the factor of interest (e.g., a trait) as well as non-target factors
(e.g., "method factors", such as item wording or type of respondent).
The latter can be considered as systematic (i.e., non-random) sources of
error, to be excluded from the numerator of a reliability coefficient,
yielding so-called "hierarchical omega" (\omega_\textrm{H}). On the
other hand, non-target variance that can be expected in repeated measurement
meets the classical test theory definition of reliability. Including method
factors in the numerator yields so-called "omega total"
(\omega_\textrm{T}), which is the default approach in compRelSEM()
because it is consistent with the classical test theory definition of
reliability. However, users can obtain \omega_\textrm{H} for a
composite by using the true= argument to specify any factor(s) to be
treated as representing true scores. The same approach can be taken to
obtain the proportion of a (sub)scale composite's variance due to method
factors (by listing those in true=), if that is of interest.
Categorical Indicators:
When all indicators (per composite) are ordinal, a CFA can be fitted that includes a threshold model (sometimes called Item Factor Analysis: IFA), which assumes a normally distributed latent response underlies each observed ordinal response. Despite making this assumption, a composite of ordinal items can only be calculated by assigning numerical values to the ordinal categories, so that the pseudo-numerical variables can be summed into a composite variable that is more approximately continuous than its items.
Applying the formulas above to IFA parameters provides the
hypothetical reliability of a composite of latent responses: a composite
which cannot be calculated in practice. Nonetheless, this hypothetical
reliability can be interpreted as an estimate of what reliability could be
if a more approximately continuous response scale were used (e.g., with
sufficiently many response categories that the standardized solutions are
equivalent between a fitted IFA and a fitted CFA that treats the ordinal
responses as numeric; Chalmers, 2018). This can be requested by setting
ord.scale=FALSE, in which case \hat\Sigma in the formulas above
is a polychoric correlation matrix.
When ord.scale=FALSE and tau.eq=TRUE, this results in what Zumbo et al.
(2007) termed "ordinal \alpha" (see criticisms by Chalmers, 2018, and
and a rejoinder by Zumbo & Kroc, 2019).
Alternatively, Green and Yang (2009, Eq. 21) derived a method to calculate
model-based reliability (\omega) from IFA parameters (i.e.,
incorporating the latent-response assumption) but that applies to the actual
(i.e., ordinal) observed response scale (the default: ord.scale=TRUE).
Lu et al. (2020) showed how to incorporate unequal weights into Green and
Yang's (2009) formula, so W= can be used to estimate the (maximal)
reliability of a weighted composite of ordinal variables.
However, combining ord.scale=TRUE with tau.eq=TRUE is not available.
For \alpha to be interpretable on the observed ordinal scale,
users must choose whether to (a) release the latent-response assumption, by
fitting a CFA without a threshold model, or (b) fit an IFA model with
constraints consistent with the assumption of (essential) tau-equivalence
(i.e., equal factor loadings).
No method analogous to Green and Yang (2009, Eq. 21) has yet been proposed to calculate reliability with a mixture of categorical and continuous indicators, so any such composite is skipped with a warning.
Multilevel Measurement Models:
How to define reliability coefficients for scales employed in nested designs is an ongoing topic of methodological development, with some ongoing controversies about best practice when the target of measurement is the "cluster" or between-level (i.e., Level 2 in a 2-level design). Geldhof et al. (2014) proposed applying the standard formulas above to each level's CFA parameters and/or (model-implied) covariance matrix, whereas Lai (2021) proposed different formulas that account for all sources of variance in composites of observed variables.
There is no controversy about how to define a within-level reliability,
coefficient, which can be interpreted as the reliability of a composite
calculated by first centering each indicator around its cluster mean, then
calculating the composite from the cluster-mean-centered items. Equivalently
(i.e., the same formula), this can be interpreted as the hypothetical
reliability of a composite of the items' latent Level-1 components. This
coefficient can be requested with lavaan::model.syntax (to pass to the
W= argument) that specifies a composite in a Level-1 "block", which not
have the same name as any composite in the Level-2 block. If users do not
use W= (i.e., calculate a reliability index per modeled common factor),
then this can be accomplished by using unique factor names across levels.
This contrasts with reliability indices for between-level composites: The reliability of a hypothetical composite of items' latent between-level components (using formulas proposed by Geldhof et al., 2014) is not equivalent to the coefficient for a composite of items' observed cluster means, using generalizations of formulas proposed by Lai (2021):
\omega^\textrm{B} =
\frac{\bold{w}^{\prime} \Lambda^\textrm{B} \Phi^\textrm{B} \Lambda^{\textrm{B}\prime} \bold{w}
}{ \bold{w}^{\prime} \hat{\Sigma}^\textrm{B} \bold{w} +
\frac{1}{\tilde{n}_\textrm{clus}} \left(
\bold{w}^{\prime} \hat{\Sigma}^\textrm{W} \bold{w} \right) },
\alpha^\textrm{B} = \frac{2k}{k - 1}\left[
\frac{ \sum^{k}_{i=2} \sum^{i-1}_{j=1} \hat\sigma^\textrm{B}_{ij}
}{ \bold{1}^{\prime} \hat\Sigma^\textrm{B} \bold{1} +
\frac{1}{\tilde{n}_\textrm{clus}} \left(
\bold{1}^{\prime} \hat\Sigma^\textrm{W} \bold{1} \right) }
\right],
where \tilde{n}_\textrm{clus} is the harmonic-mean cluster size, and
superscripts B and W indicate between- and within-level parameters.
Obtaining these estimates of composite reliability requires fitting a
2-level CFA that provides the same factor structure and factor names in
the models at both levels (following the advice of Jak et al., 2021), as
well as the same composite name in both levels/blocks of syntax passed to
W= (if used). Furthermore, the between-level composite name must be
passed to the shared= argument; otherwise, the same factor/composite name
across levels will yield Lai's (2021) coefficient for a configural construct
(see Examples):
\omega^\textrm{2L} =
\frac{\bold{w}^{\prime} \left(
\Lambda^\textrm{W} \Phi^\textrm{W} \Lambda^{\textrm{W}\prime} +
\Lambda^\textrm{B} \Phi^\textrm{B} \Lambda^{\textrm{B}\prime}
\right) \bold{w}
}{ \bold{w}^{\prime} \hat\Sigma^\textrm{B} \bold{w} +
\bold{w}^{\prime} \hat\Sigma^\textrm{W} \bold{w} },
\alpha^\textrm{2L} = \frac{2k}{k - 1}\left[
\frac{ \sum^{k}_{i=2} \sum^{i-1}_{j=1} \left( \hat\sigma^\textrm{W}_{ij} +
\hat\sigma^\textrm{B}_{ij} \right)
}{ \bold{1}^{\prime} \hat\Sigma^\textrm{B} \bold{1} +
\bold{1}^{\prime} \hat\Sigma^\textrm{W} \bold{1} }
\right],
This can be interpreted as the scale-reliability coefficient ignoring the nested design, as both the common-factor variance of the Level-1 factor and of its Level-2 cluster means are treated as true-score variance.
Note that Lai's (2021) between-level reliability coefficients for a
shared construct quantify generalizability across both indicators and
raters (i.e., subjects rating their cluster's construct).
Lüdtke et al. (2011) refer to these as measurement error and sampling error,
respectively. From this perspective (and following from generalizability
theory), an IRR coefficient can also be calculated:
\textrm{IRR} =
\frac{\bold{w}^{\prime} \left( \hat{\Sigma}^\textrm{B} \right) \bold{w}
}{ \bold{w}^{\prime} \hat\Sigma^\textrm{B} \bold{w} +
\bold{w}^{\prime} \hat\Sigma^\textrm{W} \bold{w} },
which quantifies generalizability across rater/sampling-error only, and can
be returned for any shared= construct's composite by setting add.IRR=TRUE.
Value
By default (simplify=FALSE) a list of numeric vectors (1 per
composite) is returned. In multigroup CFA, the vector contains a reliability
index for each group in which the composite can be computed.
Each composite's vector has a attr(..., "header") with information to
facilitate interpretation of that index:
A list of variables in the composite, which determines the composite's total variance (denominator of reliability)
Whether that total variance (denominator) is determined from the restricted model (i.e., CFA parameters) or unrestricted model (i.e., a freely estimated covariance matrix)
Whether the variables in the composite are (a transformation of) observed variables, or whether they are latent (components of) variables. The latter (e.g., latent responses assumed to underlie observed ordinal indicators, or latent level-specific components of variables in a multilevel CFA) cannot be used to calculated an observed composite variable, so the resulting coefficient should be cautiously interpreted as a "hypothetical reliability" (Chalmers, 2018; Lai, 2021).
The latent variables that contribute common-factor variance to the composite, which determine the composite's "true-score" variance (numerator of reliability)
Which reliability formula was used: model-based reliability (so-called "omega") or coefficient alpha (a model-free lower-bound estimate of true reliability, equivalent to a model-based reliability that assumes tau-equivalence)
This header will be printed immediately above each composite's
reliability coefficient. When multiple reliability coefficients are
returned, and each vector in the list has the same length, then
setting simplify=TRUE will collect the list of single
coefficients into a vector, or the list of multiple coefficients
into a data.frame, and their headers will be concatenated to be
printed above the coefficients. Setting simplify = -1L (or any
negative number) will omit the informative headers.
Author(s)
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Uses hidden functions to implement Green & Yang's (2009) reliability for
categorical indicators, written by Sunthud Pornprasertmanit
(psunthud@gmail.com) for the deprecated reliability() function.
References
Bentler, P. M. (1968). Alpha-maximized factor analysis (alphamax): Its relation to alpha and canonical factor analysis. Psychometrika, 33(3), 335–345. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02289328")}
Chalmers, R. P. (2018). On misconceptions and the limited usefulness of ordinal alpha. Educational and Psychological Measurement, 78(6), 1056–1071. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0013164417727036")}
Cho, E. (2021) Neither Cronbach’s alpha nor McDonald’s omega: A commentary on Sijtsma and Pfadt. Psychometrika, 86(4), 877–886. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-021-09801-1")}
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02310555")}
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/a0032138")}
Green, S. B., & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74(1), 155–167. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-008-9099-3")}
Jak, S., Jorgensen, T. D., & Rosseel, Y. (2021). Evaluating cluster-level
factor models with lavaan and Mplus. Psych, 3(2),
134–152. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/psych3020012")}
Lai, M. H. C. (2021). Composite reliability of multilevel data: It’s about observed scores and construct meanings. Psychological Methods, 26(1), 90–102. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000287")}
Lu, Z., Hong, M., & Kim, S. (2021). Formulas of multilevel reliabilities for tests with ordered categorical responses. In M. Wiberg, D. Molenaar, J. González, U.Böckenholt, & J.-S. Kim (Eds.), Quantitative psychology: The 85th annual meeting of the Psychometric Society, Virtual (pp. 103–112). Springer. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-030-74772-5_10")}
Lüdtke, O., Marsh, H. W., Robitzsch, A., & Trautwein, U. (2011).
A 2 \times 2 taxonomy of multilevel latent contextual models:
Accuracy–bias trade-offs in full and partial error correction models.
Psychological Methods, 16(4), 444–467. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/a0024376")}
McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.
Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.22237/jmasm/1177992180")}
Zumbo, B. D., & Kroc, E. (2019). A measurement is a choice and Stevens’ scales of measurement do not help make it: A response to Chalmers. Educational and Psychological Measurement, 79(6), 1184–1197. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0013164419844305")}
See Also
maximalRelia() for the maximal reliability of weighted composite
Examples
data(HolzingerSwineford1939)
HS9 <- HolzingerSwineford1939[ , paste0("x",1:9)]
HSbinary <- as.data.frame( lapply(HS9, cut, 2, labels=FALSE) )
names(HSbinary) <- paste0("y",1:9)
HS <- cbind(HolzingerSwineford1939, HSbinary)
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ y7 + y8 + y9 '
fit <- cfa(HS.model, data = HS, ordered = c("y7","y8","y9"), std.lv = TRUE)
fitg <- cfa(HS.model, data = HS, ordered = c("y7","y8","y9"), std.lv = TRUE,
group = "school")
## works for factors with exclusively continuous OR categorical indicators
compRelSEM(fit)
compRelSEM(fitg)
## reliability for composite of ALL indicators only available when they are
## all continuous or all categorical. The example below calculates a
## composite of continuous items from 2 factors (visual and textual)
## using the custom-weights syntax (note the "<~" operator)
w.tot <- '
visual <~ x1 + x2 + x3
textual <~ x4 + x5 + x6
total <~ x1 + x2 + x3 + x4 + x5 + x6
'
compRelSEM(fit, W = w.tot)
## ----------------------
## Higher-order construct
## ----------------------
## Reliability of a composite that represents a higher-order factor
mod.hi <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
general =~ visual + textual + speed '
fit.hi <- cfa(mod.hi, data = HolzingerSwineford1939)
## "general" is the factor representing "true scores", but it has no
## observed indicators. Must use custom-weights syntax:
compRelSEM(fit.hi, W = 'g <~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9')
## ----------------------
## Hierarchical omega
## and omega Total
## ----------------------
mod.bi <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
general =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 '
fit.bi <- cfa(mod.bi, data = HolzingerSwineford1939,
orthogonal = TRUE, std.lv = TRUE)
compRelSEM(fit.bi, return.total = -1) # omega_Total
compRelSEM(fit.bi, return.total = -1, # omega_Hierarchical
true = list(.TOTAL. = "general"))
## ----------------------
## Multilevel Constructs
## ----------------------
## Same factor structure with metric invariance across levels (Jak et al., 2021)
model2 <- '
level: 1
f1 =~ y1 + L2*y2 + L3*y3
f2 =~ y4 + L5*y5 + L6*y6
level: 2
f1 =~ y1 + L2*y2 + L3*y3
f2 =~ y4 + L5*y5 + L6*y6
'
fit2 <- sem(model2, data = Demo.twolevel, cluster = "cluster")
## Lai's (2021, Eq. 13) omega index for a configural (Level-1) construct,
## treating common-factor variance at both levels as "true" variance
compRelSEM(fit2)
## Lai's (2021, Eq. 17) omega index for a shared (Level-2) construct
## (also its interrater reliability coefficient)
compRelSEM(fit2, shared = c("f1","f2"), add.IRR = TRUE)
## Geldhof et al.'s (2014) level-specific indices imply a different
## composite (hypothetically) calculated per level. Thus, use
## unique composite names per level.
W2.Geldhof <- ' level: 1
F1w <~ y1 + y2 + y3
F2w <~ y4 + y5 + y6
level: 2
F1b <~ y1 + y2 + y3
F2b <~ y4 + y5 + y6
'
compRelSEM(fit2, W = W2.Geldhof)
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