maximal reliability of unit-weighted composites peter m. bentler university of california, los...
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Maximal Reliability of Unit-Weighted Composites
Peter M. Bentler
University of California, Los Angeles
SAMSI, NOV 05
In S. Y. Lee (Ed.) (in press). Handbook of Structural Equation Models.
“Reliability” = Internal Consistency of the composite X based on p components
1 1 2 2 p pX=w Y +w Y +...+w Y (X=w Y)
(all 1); , uncorrelatedY C U p C U
ccov( ) ( psd, rank<p; diag or pd)cY
1cxx
w w w w
w w w w
(Bentler, 1968, eq. 12; Heise & Bohrnstedt, 1970, eq. 32;
this is H-B's coefficient - special case is McDonald's 1999 )
c
is a not necessarily a measure of homogeneity
or unidimensionality. A coefficient for uni-
dimensional common scores requires, in addition,
that rank( ) 1.
What , defined below, has to say (or, no
xx
t say)
about unidimensionality has been an issue
widely discussed in the literature.
Since uniqueness = specificity + error, i.e.,
. If both are p.d.,s e *
*
is a lower bound to "true" reliability,
where
1
xx xx
exx
w w
w w
In this talk I will emphasize
, the unit vector.
This excludes maximal reliability.
w 1
Under mild assumptions consistent with the above, Novick & Lewis (1967) showed that
xx
'1
1 '
where, with diag( ) and a unit vector,
is the well-known alpha coefficient. This says
nothing about as a lower bound to a uni-
dimensional component of a multidimensional
1 D 1p
p 1 1
D 1
c.
cWhat models for are consistent with ?
Let be the average covariance, and defineij
=( ), where =diag( ). ThenijD I D
. It follows thatc ijD I
.xx
cThis choice does not guarantee that has
any interesting properties, e.g., psd, rank 1.
This is not the only condition for .xx
If ,
where are communalities, we only needc H
H
D D
D
c
( ) /
Interesting seem not to be known.
Another well known case of equivalence
for a rank 1 is given below.
H ij
H
1 D 1 p
D
cActually, I will argue that should be psd,
which is not guaranteed for arbitrary .HD
This Hoyt-Guttman-Cronbach α is by far the
most widely used measure of the internal con-
sistency reliability of a composite X. In prac-
tice, one substitutes the sample covariance
matrix S and its diagonal DS to get
'ˆ 1 .
1 '
This is not necessarily a lower bound to
population internal consistency.
S1 D 1p
p 1 S1
Some Recent References• Becker, G. (2000). Coefficient alpha: Some terminological ambiguities and
related misconceptions. Psychological Reports, 86, 365-372.• Bonett, D. G. (2003). Sample size requirement for testing and estimating
coefficient alpha. Journal of Educational and Behavioral Statistics, 27, 335-340. • Enders, C. K., & Bandalos, D. L. (1999). The effects of heterogeneous item
distributions on reliability. Applied Measurement in Education, 12, 133-150. • Green, S. B., & Hershberger, S. L. (2000). Correlated errors in true score models
and their effect on coefficient alpha. Structural Equation Modeling, 7, 251-270. • Hakstian, A. R., & Barchard, K. A. (2000). Toward more robust inferential
procedures for coefficient alpha under sampling of both subjects and conditions. Multivariate Behavioral Research, 35, 427-456.
• Kano, Y., & Azuma, Y. (2003). Use of SEM programs to precisely measure scale reliability. In H. Yanai, A. Okada, K. Shigemasu, Y. Kano, & J. J. Meulman (Eds.), New developments in psychometrics (pp. 141-148). Tokyo: Springer-Verlag.
• Komaroff, E. (1997). Effect of simultaneous violations of essential tau-equivalence and uncorrelated error on coefficient alpha. Applied Psychological Measurement, 21, 337-348.
• Miller, M. B. (1995). Coefficient alpha: A basic introduction from the perspectives of classical test theory and structural equation modeling. Structural Equation Modeling, 2, 255-273.
• Raykov, T. (1998). Coefficient alpha and composite reliability with interrelated nonhomogeneous items. Applied Psychological Measurement, 22, 375-385.
• Raykov, T. (2001). Bias of coefficient α for fixed congeneric measures with correlated errors. Applied Psychological Measurement, 25, 69-76.
• Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8, 350-353.
• Shevlin, M., Miles, J. N. V., Davies, M. N. O., & Walker, S. (2000). Coefficient alpha: A useful indicator of reliability? Personality & Individual Differences, 28, 229-237.
• Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8, 350-353.
• Yuan, K.-H., Guarnaccia, C. A., & Hayslip, B. J. (2003). A study of the distribution of sample coefficient alpha with the Hopkins Symptom Checklist: Bootstrap versus asymptotics. Educational & Psychological Measurement, 63, 5-23.
Some Advantages of
• Widely taught and known
• Simple to compute and explain
• Available in most computer packages is a lower bound to population internal
consistency under reasonable conditions (not true of sample )
• Not reliant on researcher judgments (i.e., same data, same result for everyone)
Note: Independence of researcher judgment is not totally correct
• Different covariance matrices, or scalings of the variables, yield different
and hence different
• Sometimes alpha is computed from the correlation matrix, and not the covariance matrix, implying the sum X is a sum of standardized variables
Some Disadvantages of • α is not a measure of homogeneity or
unidimensionality• It may underestimate or overestimate uni- dimensional reliability• Since it will overestimate reliability if , the average covariance, is spuriously high;
or underestimate it if the average is spuriously low
2 /ijp 1 1 ij
This illustrates the advantages and disadvantages of alpha
• The plus: The average covariance is what it is, period. No judgment can change it. Hence, no judgment will change the reported .
• The minus: A researcher’s model of sources of variance does not influence the reported reliability (alpha) when, perhaps, it should.
• Some examples from Kano/Azuma (2003) illustrate this.
If for a one factor model and possibly correlated residuals ( not necessarily diagonal)
with (p 1), then c
Three examples from Kano & Azuma (2003) showing alpha as accurate, and as an overestimate. 1-factor reliability (rho= omega) ignoring correlated errors can overestimate.
Alpha as a unidimensional lower-bound: the comparison of in a 1-factor model
If , and is px1, is diagonal,
. Example: McDonald (1999) showed
with equality when
11,
11
1
11( ) { ( )} 01 i
p1 1 Var
p
c1
11
11
11a 11b 11c
If ,
there is no known general relation of to .
In fact, (= ) is itself not defined. We can
have , , etc., depending on how the
1-factor model is computed (ML,LS,GLS,etc.)
[Th
11
at is, how ( ) is fit to under this
misspecification. No sample data are involved.]
This nonuniqueness of under misspecification
is a weakness of this model-based coefficient.
Thus,
• α is a good indicator of association
but it is not clear what interesting sets of models it represents in the general case
• A model based coefficient such as
provides a clear partitioning of variance,
but it also can be (1) misleading (e.g., Kano
-Azuma), (2) not relevant
( )ij
11( )
(e.g., )
To give up α for a model-based coefficient, the model must be correct for Σ. That is, it should fit the data (say, sample covariance matrix S). I would conclude:
• If is computed, the researcher must also provide evidence of acceptability of the 1-factor model.
• If a modified estimator
is computed, the researcher should provide an argument for the variance partition.
11
11ˆ( with nondiagonal )
Should the correlated error be part of the residual covariance Ψ (on left), or part of the common variance Σc (on right)? Substantive reasoning should determine the variance partitioning. This is more than just model fit.
What other model-based coefficients could be
used instead? • Arbitrary latent variable model (Raykov &
Shrout, 2002; EQS 6)• Dimension-free lower bound (Bentler,
1972; bias correction Shapiro & ten Berge, 2000)
• Greatest lower bound (Woodhouse & Jackson, 1977; Bentler & Woodward, 1980 etc.; ten Berge, Snijders, & Zegers, 1981; bias correction Li & Bentler, 2001)
Arbitrary LV Model
c
model c
Suppose an arbitrary SEM (e.g., a LISREL
model) contains additive errors, whether
correlated or not. That is, = ( )= .
It must be acceptable statistically (fit ). Then
( ) /( )
is a
S
1 1 1' 1
11 more meaningful coefficient than (= ).
It is probably more meaningful than .
Dimension-free Lower Bound
+
*11 +
for some arbitrary, unknown
number of factors
is diagonal, and
min subject to above.
This has the property that ( , )
c
c
cxx
xx
1 1
1 1
Greatest Lower Bound
This is a constrained version of the dimension-free coefficient. In addition to
glb
+
*glb
for some number of factors,
is not only diagonal, but also psd. Thus
,
with equality when has no Heywood
variables (no negative variances).
Also, .xx
+ glb
Every researcher will get the same values of
and . They are based on a model that
does not depend on researcher choices.
Since off-diag( ) [off-diag( ), as replaces
in practice] is exactly rep
S S
+ glb
roduced, all
covariation is assumed to stem from common
variance. However, and do not allow
nondiagonal .
Maximal Unidimensional Reliability
• The problem with α and the multi-dimensional coefficients seems to be that they do not represent unidimensional reliability
• Although not obvious, unidimensional reliability can be defined for multi-
dimensional latent variable models. That is the main new result in this talk.
T E
1,
p
iX X 1
p
iT T 1
p
iE E
2
21T T E
Xxx
1 1 1 1
1 1 1 1
Repeating the Basic Setup
Xi = Ti + Ei,
X = T + E ,
2 221
2
but we compute something like
( )( )1d
p
T i u
X11
1 11 1 1
1 1 1 1 1 1 1 1
0. ., truth is ui e
Can we have something like1-Factor Based Reliability when the latent variables are multidimensional?
Maximal Unit-weighted Reliability x (p x k) for some k
(“small k” <
or “large k”)
.5(2 1 8 1)p p
for some acceptable k-factor model
[ | ] , where is (px1) and is (px(k-1))
contains unrestricted free parameters.01 , that is, the k-1 columns of sum to zero.
contains free parameters subject to (k-1)(k-2)/2 restrictions (usual EFA identification conditions)
Reliability under this Parameterization
X 1 x 1 1
1[ | ] [ | ] [ | 0] [ | 0]
[ | 0]
p
i
x
1 1 1 1 1
1X X d dX T E X is based on 1 factor!
2 2
2 2 2d
X
T X
X Xkk
2
2
( )
( ) ( )
1 1 1
1 1 1 1 1
2
2 2
ˆˆ ˆ ˆ1
ˆ ˆ ˆ ˆˆ
X
X
X
kk
1 1 1 1
1 1 1 1
This is Maximal Unit-Weighted Reliability
Let and let t be a normal vector ( 1)t t
Then the factor loading vector t that maximizes 2( )1
is given by 1/ 2( )1 1 1 and the residual factors
where have zero column sums
( 0).1
Applications to Arbitrary Structural Models
Any structural model with additive errors: ( )
Linear structural model with additive errors:
( )
Let 1 1( ) ( )I B I B with
1 1/ 2( )I B
Greatest lower bound:
1 max min 1 ,
with ( ) and
glb
1 1 1 1
1 1 1 1
psd
ˆ ˆ ˆS
EFA Example (It’s all in EQS)/TITLEMaximum Reliability EFA Model SetupNine Psychological Variables/SPECIFICATIONSVARIABLES= 9; CASES=101;MATRIX=COVARIANCE; METHOD=ML;/EQUATIONSV1=*F1+*F2+0F3+E1;V2=*F1+*F2+*F3+E2;V3=*F1+*F2+*F3+E3;V4=*F1+*F2+*F3+E4;V5=*F1+*F2+*F3+E5;V6=*F1+*F2+*F3+E6;V7=*F1+*F2+*F3+E7;V8=*F1+*F2+*F3+E8;V9=*F1+*F2+*F3+E9;
/VARIANCESF1 TO F3 = 1.0;E1 TO E9 = .5*;/CONSTRAINTS(V1,F2)+(V2,F2)+(V3,F2)+(V4,F2)+(V5,F2)+(V6,F2)+(V7,F2)+(V8,F2)+(V9,F2)=0;(V2,F3)+(V3,F3)+(V4,F3)+(V5,F3)+(V6,F3)+(V7,F3)+(V8,F3)+(V9,F3)=0;
/MATRIX1.00 .75 1.00 .78 .72 1.00 .44 .52 .47 1.00 .45 .53 .48 .82 1.00 .51 .58 .54 .82 .74 1.00 .21 .23 .28 .33 .37 .35 1.00 .30 .32 .37 .33 .36 .38 .45 1.00 .31 .30 .37 .31 .36 .38 .52 .67 1.00/END
for 9 Psychological Variables ˆ .886
Variable Number
1-Factor
Model
3-Factor
Model
1 .636 .727
2 .697 .738
3 .667 .754
4 .867 .789
5 .844 .767
6 .879 .803
7 .424 .492
8 .466 .597
9 .462 .635
Sum 5.942 6.302
227 190.6
ˆ .88011
212 1.6
ˆ .939kk