headrick.sheng.lalpha: Sample Headrick and Sheng L-alpha
In lmomco: L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions
headrick.sheng.lalpha R Documentation
Sample Headrick and Sheng L-alpha
Description
Compute the sample “Headrick and Sheng L-alpha” (\alpha_L) (Headrick and Sheng, 2013) by
\alpha_L = \frac{d}{d-1}
\biggl(1 - \frac{\sum_j \lambda^{(j)}_2}{\sum_j \lambda^{(j)}_2 + \sum\sum_{j\ne j'} \lambda_2^{(jj')}} \biggr)\mathrm{,}
where j = 1,\ldots,d for dimensions d, the \sum_j \lambda^{(j)}_2 is the summation of all the 2nd order (univariate) L-moments (L-scales, \lambda^{(j)}_2), and the double summation is the summation of all the 2nd-order L-comoments (\lambda_2^{(jj')}). In other words, the double summation is the sum total of all entries in both the lower and upper triangles (not the primary diagonal) of the L-comoment matrix (the L-scale and L-coscale [L-covariance] matrix) (Lcomoment.matrix).
The \alpha_L is closely related in structural computation as the well-known “Cronbach alpha” (\alpha_C). These are coefficients of reliability, which commonly ranges from 0 to 1, that provide what some methodologists portray as an overall assessment of a measure's reliability. If all of the scale items are entirely independent from one another, meaning that they are not correlated or share no covariance, then \alpha_C is 0, and, if all of the items have high covariances, then \alpha_C will approach 1 as the number of items in the scale approaches infinity. The higher the \alpha_C coefficient, the more the items have shared covariance and probably measure the same underlying concept. Theoretically, there is no lower bounds for \alpha_{C,L}, which can add complicating nuances in bootstrap or simulation study of both \alpha_C and \alpha_L. Negative values are considered a sign of something potentially wrong about the measure related to items not being positively correlated with each other, or a scoring system for a question item reversed. (This paragraph in part paraphrases data.library.virginia.edu/using-and-interpreting-cronbachs-alpha/
(accessed May 21, 2023; dead link April 18, 2024) and other general sources.)
Usage
headrick.sheng.lalpha(x, bycovFF=FALSE, a=0.5, digits=8, ...)
lalpha(x, bycovFF=FALSE, a=0.5, digits=8, ...)
Arguments
x
An R data.frame of the random observations for the d random variables X, which must be suitable for internal dispatch to the Lcomoment.matrix function for computation of the 2nd-order L-comoment matrix. Alternatively, x can be a precomputed 2nd-order L-comoment matrix (L-scale and L-coscale matrix) as shown by the following usage: lalpha(Lcomoment.matrix(x, k=2)$matrix).
bycovFF
A logical triggering the covariance pathway for the computation and bypassing the call to the L-comoments. The additional arguments can be used to control the pp function that is called internally to estimate nonexceedance probabilities and the “covariance pathway” (see Details). If bycovFF is FALSE, then the direct to L-comoment computation is used.
a
The plotting position argument a to the pp function that is hardwired here to Hazen in contrast to the default a=0 of pp (Weibull) for reasoning shown in this documentation.
digits
Number of digits for rounding on the returned value(s).
...
Additional arguments to pass.
Details
Headrick and Sheng (2013) propose \alpha_L to be an alternative estimator of reliability based on L-comoments. Those authors describe its context as follows: “Consider [a statistic] alpha (\alpha) in terms of a model that decomposes an observed score into the sum of two independent components: a true unobservable score t_i and a random error component \epsilon_{ij}.”
Those authors continue “The model can be summarized as
X_{ij} = t_i + \epsilon_{ij}\mathrm{,} where X_{ij} is the observed score associated with the ith examinee on the jth test item, and where i = 1,...,n [for sample size n]; j = 1,\ldots,d; and the error terms (\epsilon_{ij}) are independent with a mean of zero.” Those authors comment that “inspection of [this model] indicates that this particular model restricts the true score t_i to be the same across all d test items.”
Those authors show empirical results for a simulation study, which indicate that \alpha_L can be “substantially superior” to [a different formulation of \alpha_C (Cronbach's alpha) based on product moments (the variance-covariance matrix)] in “terms of relative bias and relative standard error when distributions are heavy-tailed and sample sizes are small.”
Those authors show (Headrick and Sheng, 2013, eqs. 4 and 5) the reader that the second L-comoments of X_j and X_{j'} can alternatively be expressed as
\lambda_2(X_j) = 2\mathrm{Cov}(X_j, F(X_j)) and \lambda_2(X_{j'}) = 2\mathrm{Cov}(X_{j'}, F(X_{j'})). The second L-comoments of X_j toward (with respect to) X_{j'} and X_{j'} toward (with respect to) X_j are \lambda_2^{(jj')} = 2\mathrm{Cov}(X_j, F(X_{j'})) and \lambda_2^{(j'j)} = 2\mathrm{Cov}(X_{j'}, F(X_j)). The respective cumulative distribution functions are denoted F(x_j) (nonexceedance probabilities). Evidently, those authors present the L-moments and L-comoments this way because their first example (thanks for detailed numerics!) already contain nonexceedance probabilities.
This apparent numerical difference between the version using estimates of nonexceedance probabilities for the data (the “covariance pathway”) compared to a “direct to L-comoment” pathway might be more than academic concern.
The Examples provide comparison and brief discussion of potential issues involved in the direct L-comoments and the covariance pathway. The discussion leads to interest in the effects of ties and their handling and the question of F(x_j) estimation by plotting position (pp). The Note section of this documentation provides expanded information and insights to \alpha_L computation.
Value
An R list is returned.
alpha
The \alpha_L statistic.
pitems
The number of items (column count) in the x.
n
The sample size (row count), if applicable, to the contents of x.
text
Any pertinent messages about the computations.
source
An attribute identifying the computational source of the Headrick and Sheng L-alpha: “headrick.sheng.lalpha” or “lalpha.star()”.
Note
Headrick and Sheng (2013) use k to represent d as used here. The change is made because k is an L-comoment order argument already in use by Lcomoment.matrix.
Demonstration of Nuances of L-alpha—Consider Headrick and Sheng (2013, tables 1 and 2) and the effect of those authors' covariance pathway to \alpha_L:
X1 <- c(2, 5, 3, 6, 7, 5, 2, 4, 3, 4) # Table 1 in Headrick and Sheng (2013)
X2 <- c(4, 7, 5, 6, 7, 2, 3, 3, 5, 4)
X3 <- c(3, 7, 5, 6, 6, 6, 3, 6, 5, 5)
X <- data.frame(X1=X1, X2=X2, X3=X3)
lcm2 <- Lcomoment.matrix(X, k=2)
print(lcm2$matrix, 3)
# [,1] [,2] [,3]
# [1,] 0.989 0.567 0.722
# [2,] 0.444 1.022 0.222
# [3,] 0.644 0.378 0.733
Now, compare the above matrix to Headrick and Sheng (2013, table 2) where it is immediately seen that the matrices are not the same before the summations are applied to compute \alpha_L.
# [,1] [,2] [,3]
# [1,] 0.989 0.500 0.789
# [2,] 0.500 1.022 0.411
# [3,] 0.667 0.333 0.733
Now, consider how the nonexceedances in Headrick and Sheng (2013, table 1) might have been computed w/o following their citation to original sources. It can be shown with reference to the first example above that these nonexceedance probabilities match.
FX1 <- rank(X$X1, ties.method="average") / length(X$X1)
FX2 <- rank(X$X2, ties.method="average") / length(X$X2)
FX3 <- rank(X$X3, ties.method="average") / length(X$X3)
Notice in Headrick and Sheng (2013, table 1) that there is no zero probability, but there is a unity and some of the probabilities are tied. Ties have numerical ramifications. Let us now look at other L-alphas using the nonexceedance pathway and use different definitions of nonexceedance estimation and inspect the results:
# lmomco documentation says pp() uses ties.method="first"
lalpha(X, bycovFF=TRUE, a=0 )$alpha
# [1] 0.7448583 # unbiased probs all distributions
lalpha(X, bycovFF=TRUE, a=0.3173)$alpha
# [1] 0.7671384 # Median probs for all distributions
lalpha(X, bycovFF=TRUE, a=0.35 )$alpha
# [1] 0.7695105 # Often used with probs-weighted moments
lalpha(X, bycovFF=TRUE, a=0.375 )$alpha
# [1] 0.771334 # Blom, nearly unbiased quantiles for normal
lalpha(X, bycovFF=TRUE, a=0.40 )$alpha
# [1] 0.7731661 # Cunnane, appox quantile unbiased
lalpha(X, bycovFF=TRUE, a=0.44 )$alpha
# [1] 0.7761157 # Gringorten, optimized for Gumbel
lalpha(X, bycovFF=TRUE, a=0.5 )$alpha
# [1] 0.7805825 # Hazen, traditional choice
# This the plotting position (i-0.5) / n
This is not a particularly pleasing situation because the choice of the plotting position affects the \alpha_L. The Hazen definition lalpha(X[,1:3], bycovFF=FALSE) using direct to L-comoments matches the last computation shown (\alpha_L = 0.7805825). A question, thus, is does this matching occur because of the nature of the ties and structure of the L-comoment algorithm itself? A note to this question involves a recognition that the answer is yes because L-comoments use a sort() operation and does not use rank() because the weights for the linear combinations are used and the covariance pathway 2*cov(x$X3, x$FX2), for instance.
Recognizing that the direct to L-comoments alpha equals the covariance pathway with Hazen plotting positions, let us look at L-comoments:
lmomco::Lcomoment.Lk12 ------> snippet
X12 <- X1[sort(X2, decreasing = FALSE, index.return = TRUE)$ix]
n <- length(X1)
SUM <- sum(sapply(1:n, function(r) { Lcomoment.Wk(k, r, n) * X12[r] }))
return(SUM/n)
Notice that a ties.method is not present but kind of implicit as ties first by the index return of the sort() and notice the return of a SUM/n though this is an L-comoment and not an nonexceedance probability.
Let us run through the tie options using a plotting position definition (i / n) matching the computations of Headrick and Sheng (2013) ("average", A=0, B=0 for pp) and the first computation \alpha_L = 0.807 matches that reported by Headrick and Sheng (2013, p. 4):
for(tie in c("average", "first", "last", "min", "max")) { # random left out
Lalpha <- lalpha(X, bycovFF=TRUE,
a=NULL, A=0, B=0, ties.method=tie)$alpha
message("Ties method ", stringr::str_pad(tie, 7, pad=" "),
" provides L-alpha = ", Lalpha)
}
# Ties method average provides L-alpha = 0.80747664
# Ties method first provides L-alpha = 0.78058252
# Ties method last provides L-alpha = 0.83243243
# Ties method min provides L-alpha = 0.81363468
# Ties method max provides L-alpha = 0.80120709
Let us run through the tie options again using a different plotting position estimator ((n-0.5) / (n+0.5)):
for(tie in c("average", "first", "last", "min", "max")) { # random left out
Lalpha <- lalpha(X, bycovFF=TRUE,
a=NULL, A=-0.5, B=0.5, ties.method=tie)$alpha
message("Ties method ", stringr::str_pad(tie, 7, pad=" "),
" provides L-alpha = ", Lalpha)
}
# Ties method average provides L-alpha = 0.78925733
# Ties method first provides L-alpha = 0.76230208
# Ties method last provides L-alpha = 0.81431215
# Ties method min provides L-alpha = 0.79543602
# Ties method max provides L-alpha = 0.78296931
We see obviously that the decision on how to treat ties has some influence on the computation involving the covariance pathway. This is not an entirely satisfactory situation, but perhaps the distinction does not matter much? The direct L-comoment pathway seems to avoid this issue because sort() is stable and like ties.method="first". Experiments suggest that a=0.5 (Hazen plotting positions) produces the same results as direct L-comoment (see the next section). However, as the following code set shows:
for(tie in c("average", "first", "last", "min", "max")) { # random left out
Lalpha1 <- lalpha(X, bycovFF=TRUE, a=0.5, ties.method=tie)$alpha
Lalpha2 <- lalpha(X, bycovFF=TRUE, a=NULL, A=-0.5, B=0, ties.method=tie)$alpha
Lalpha3 <- lalpha(X, bycovFF=TRUE, a=NULL, A=-1 , B=0, ties.method=tie)$alpha
Lalpha4 <- lalpha(X, bycovFF=TRUE, a=NULL, A= 0, B=0, ties.method=tie)$alpha
print(c(Lalpha1, Lalpha2, Lalpha3, Lalpha4))
}
The \alpha_L for a given tie setting are all equal as long as the demoninator of the plotting position ((i + A) / (n + B)) has B=0. The a=0.5 produces Hazen, the a=NULL, A=-0.5 produces Hazen, though a=NULL, A=-1 (lower limit of A) and a=NULL, A=0 (upper limit of A given B) also produces the same. This gives us as-implemented-proof that the sensitivity to the \alpha_L computation is in the sorting and the denominator of the plotting position formula. The prudent default settings for when the bycovFF argument is true seems to have the a=-0.5 as nonexceedance probabilities are computed by the well-known Hazen description and with the tie method as first, the computations match direct to L-comoments.
Demonstration of Computational Times—A considerable amount of documentation and examples are provided here about the two pathways that \alpha_L can be computed: (1) direct by L-comoments or (2) covariance pathway requiring precomputed estimates of the nonexceedance probabilities using a ties.method="first" (default pp). The following example shows numerical congruence between the two pathways if the so-called Hazen plotting positions (a=0.5, see pp) are requested with the implicit default of ties.method="first". However, the computational time of the direct method is quite a bit longer because of latencies in the weight factor computations involved in the L-comoments and nested for loops.
set.seed(1)
R <- 1:10; nsam <- 1E5 # random and uncorrelated scores in this measure
Z <- data.frame( I1=sample(R, nsam, replace=TRUE),
I2=sample(R, nsam, replace=TRUE),
I3=sample(R, nsam, replace=TRUE),
I4=sample(R, nsam, replace=TRUE) )
system.time(AnF <- headrick.sheng.lalpha(Z, bycovFF=FALSE)$alpha)
system.time(AwF <- headrick.sheng.lalpha(Z, bycovFF=TRUE )$alpha) # Hazen
# user system elapsed
# 30.382 0.095 30.501 AnF ---> 0.01370302
# user system elapsed
# 5.054 0.030 5.092 AwF ---> 0.01370302
Author(s)
W.H. Asquith
References
Headrick, T.C., and Sheng, Y., 2013, An alternative to Cronbach's alpha—An L-moment-based measure of internal-consistency reliability: in Millsap, R.E., van der Ark, L.A., Bolt, D.M., Woods, C.M. (eds) New Developments in Quantitative Psychology, Springer Proceedings in Mathematics and Statistics, v. 66, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-1-4614-9348-8_2")}.
Headrick, T.C., and Sheng, Y., 2013, A proposed measure of internal consistency reliability—Coefficient L-alpha: Behaviormetrika, v. 40, no. 1, pp. 57–68, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2333/bhmk.40.57")}.
Béland, S., Cousineau, D., and Loye, N., 2017, Using the McDonald's omega coefficient instead of Cronbach's alpha [French]: McGill Journal of Education, v. 52, no. 3, pp. 791–804, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.7202/1050915ar")}.
See Also
Lcomoment.matrix, pp
Examples
# Table 1 in Headrick and Sheng (2013)
TV1 <- # Observations in cols 1:3, estimated nonexceedance probabilities in cols 4:6
c(2, 4, 3, 0.15, 0.45, 0.15, 5, 7, 7, 0.75, 0.95, 1.00,
3, 5, 5, 0.35, 0.65, 0.40, 6, 6, 6, 0.90, 0.80, 0.75,
7, 7, 6, 1.00, 0.95, 0.75, 5, 2, 6, 0.75, 0.10, 0.75,
2, 3, 3, 0.15, 0.25, 0.15, 4, 3, 6, 0.55, 0.25, 0.75,
3, 5, 5, 0.35, 0.65, 0.40, 4, 4, 5, 0.55, 0.45, 0.40)
T1 <- matrix(ncol=6, nrow=10)
for(r in seq(1,length(TV1), by=6)) T1[(r/6)+1, ] <- TV1[r:(r+5)]
colnames(T1) <- c("X1", "X2", "X3", "FX1", "FX2", "FX3"); T1 <- as.data.frame(T1)
lco2 <- matrix(nrow=3, ncol=3)
lco2[1,1] <- lmoms(T1$X1)$lambdas[2]
lco2[2,2] <- lmoms(T1$X2)$lambdas[2]
lco2[3,3] <- lmoms(T1$X3)$lambdas[2]
lco2[1,2] <- 2*cov(T1$X1, T1$FX2); lco2[1,3] <- 2*cov(T1$X1, T1$FX3)
lco2[2,1] <- 2*cov(T1$X2, T1$FX1); lco2[2,3] <- 2*cov(T1$X2, T1$FX3)
lco2[3,1] <- 2*cov(T1$X3, T1$FX1); lco2[3,2] <- 2*cov(T1$X3, T1$FX2)
headrick.sheng.lalpha(lco2)$alpha # Headrick and Sheng (2013): alpha = 0.807
# 0.8074766
headrick.sheng.lalpha(Lcomoment.matrix(T1[,1:3], k=2)$matrix)$alpha
# 0.7805825
headrick.sheng.lalpha(T1[,1:3])$alpha # FXs not used: alpha = 0.781
# 0.7805825
headrick.sheng.lalpha(T1[,1:3], bycovFF=TRUE)$alpha # a=0.5, Hazen by default
# 0.7805825
headrick.sheng.lalpha(T1[,1:3], bycovFF=TRUE, a=0.5)$alpha
# 0.7805825
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| headrick.sheng.lalpha | R Documentation |
Sample Headrick and Sheng L-alpha
Description
Compute the sample “Headrick and Sheng L-alpha” (\alpha_L) (Headrick and Sheng, 2013) by
\alpha_L = \frac{d}{d-1}
\biggl(1 - \frac{\sum_j \lambda^{(j)}_2}{\sum_j \lambda^{(j)}_2 + \sum\sum_{j\ne j'} \lambda_2^{(jj')}} \biggr)\mathrm{,}
where j = 1,\ldots,d for dimensions d, the \sum_j \lambda^{(j)}_2 is the summation of all the 2nd order (univariate) L-moments (L-scales, \lambda^{(j)}_2), and the double summation is the summation of all the 2nd-order L-comoments (\lambda_2^{(jj')}). In other words, the double summation is the sum total of all entries in both the lower and upper triangles (not the primary diagonal) of the L-comoment matrix (the L-scale and L-coscale [L-covariance] matrix) (Lcomoment.matrix).
The \alpha_L is closely related in structural computation as the well-known “Cronbach alpha” (\alpha_C). These are coefficients of reliability, which commonly ranges from 0 to 1, that provide what some methodologists portray as an overall assessment of a measure's reliability. If all of the scale items are entirely independent from one another, meaning that they are not correlated or share no covariance, then \alpha_C is 0, and, if all of the items have high covariances, then \alpha_C will approach 1 as the number of items in the scale approaches infinity. The higher the \alpha_C coefficient, the more the items have shared covariance and probably measure the same underlying concept. Theoretically, there is no lower bounds for \alpha_{C,L}, which can add complicating nuances in bootstrap or simulation study of both \alpha_C and \alpha_L. Negative values are considered a sign of something potentially wrong about the measure related to items not being positively correlated with each other, or a scoring system for a question item reversed. (This paragraph in part paraphrases data.library.virginia.edu/using-and-interpreting-cronbachs-alpha/
(accessed May 21, 2023; dead link April 18, 2024) and other general sources.)
Usage
headrick.sheng.lalpha(x, bycovFF=FALSE, a=0.5, digits=8, ...)
lalpha(x, bycovFF=FALSE, a=0.5, digits=8, ...)
Arguments
x |
An R |
bycovFF |
A logical triggering the covariance pathway for the computation and bypassing the call to the L-comoments. The additional arguments can be used to control the |
a |
The plotting position argument |
digits |
Number of digits for rounding on the returned value(s). |
... |
Additional arguments to pass. |
Details
Headrick and Sheng (2013) propose \alpha_L to be an alternative estimator of reliability based on L-comoments. Those authors describe its context as follows: “Consider [a statistic] alpha (\alpha) in terms of a model that decomposes an observed score into the sum of two independent components: a true unobservable score t_i and a random error component \epsilon_{ij}.”
Those authors continue “The model can be summarized as
X_{ij} = t_i + \epsilon_{ij}\mathrm{,} where X_{ij} is the observed score associated with the ith examinee on the jth test item, and where i = 1,...,n [for sample size n]; j = 1,\ldots,d; and the error terms (\epsilon_{ij}) are independent with a mean of zero.” Those authors comment that “inspection of [this model] indicates that this particular model restricts the true score t_i to be the same across all d test items.”
Those authors show empirical results for a simulation study, which indicate that \alpha_L can be “substantially superior” to [a different formulation of \alpha_C (Cronbach's alpha) based on product moments (the variance-covariance matrix)] in “terms of relative bias and relative standard error when distributions are heavy-tailed and sample sizes are small.”
Those authors show (Headrick and Sheng, 2013, eqs. 4 and 5) the reader that the second L-comoments of X_j and X_{j'} can alternatively be expressed as
\lambda_2(X_j) = 2\mathrm{Cov}(X_j, F(X_j)) and \lambda_2(X_{j'}) = 2\mathrm{Cov}(X_{j'}, F(X_{j'})). The second L-comoments of X_j toward (with respect to) X_{j'} and X_{j'} toward (with respect to) X_j are \lambda_2^{(jj')} = 2\mathrm{Cov}(X_j, F(X_{j'})) and \lambda_2^{(j'j)} = 2\mathrm{Cov}(X_{j'}, F(X_j)). The respective cumulative distribution functions are denoted F(x_j) (nonexceedance probabilities). Evidently, those authors present the L-moments and L-comoments this way because their first example (thanks for detailed numerics!) already contain nonexceedance probabilities.
This apparent numerical difference between the version using estimates of nonexceedance probabilities for the data (the “covariance pathway”) compared to a “direct to L-comoment” pathway might be more than academic concern.
The Examples provide comparison and brief discussion of potential issues involved in the direct L-comoments and the covariance pathway. The discussion leads to interest in the effects of ties and their handling and the question of F(x_j) estimation by plotting position (pp). The Note section of this documentation provides expanded information and insights to \alpha_L computation.
Value
An R list is returned.
alpha |
The |
pitems |
The number of items (column count) in the |
n |
The sample size (row count), if applicable, to the contents of |
text |
Any pertinent messages about the computations. |
source |
An attribute identifying the computational source of the Headrick and Sheng L-alpha: “headrick.sheng.lalpha” or “lalpha.star()”. |
Note
Headrick and Sheng (2013) use k to represent d as used here. The change is made because k is an L-comoment order argument already in use by Lcomoment.matrix.
Demonstration of Nuances of L-alpha—Consider Headrick and Sheng (2013, tables 1 and 2) and the effect of those authors' covariance pathway to \alpha_L:
X1 <- c(2, 5, 3, 6, 7, 5, 2, 4, 3, 4) # Table 1 in Headrick and Sheng (2013) X2 <- c(4, 7, 5, 6, 7, 2, 3, 3, 5, 4) X3 <- c(3, 7, 5, 6, 6, 6, 3, 6, 5, 5) X <- data.frame(X1=X1, X2=X2, X3=X3) lcm2 <- Lcomoment.matrix(X, k=2) print(lcm2$matrix, 3) # [,1] [,2] [,3] # [1,] 0.989 0.567 0.722 # [2,] 0.444 1.022 0.222 # [3,] 0.644 0.378 0.733
Now, compare the above matrix to Headrick and Sheng (2013, table 2) where it is immediately seen that the matrices are not the same before the summations are applied to compute \alpha_L.
# [,1] [,2] [,3] # [1,] 0.989 0.500 0.789 # [2,] 0.500 1.022 0.411 # [3,] 0.667 0.333 0.733
Now, consider how the nonexceedances in Headrick and Sheng (2013, table 1) might have been computed w/o following their citation to original sources. It can be shown with reference to the first example above that these nonexceedance probabilities match.
FX1 <- rank(X$X1, ties.method="average") / length(X$X1) FX2 <- rank(X$X2, ties.method="average") / length(X$X2) FX3 <- rank(X$X3, ties.method="average") / length(X$X3)
Notice in Headrick and Sheng (2013, table 1) that there is no zero probability, but there is a unity and some of the probabilities are tied. Ties have numerical ramifications. Let us now look at other L-alphas using the nonexceedance pathway and use different definitions of nonexceedance estimation and inspect the results:
# lmomco documentation says pp() uses ties.method="first"
lalpha(X, bycovFF=TRUE, a=0 )$alpha
# [1] 0.7448583 # unbiased probs all distributions
lalpha(X, bycovFF=TRUE, a=0.3173)$alpha
# [1] 0.7671384 # Median probs for all distributions
lalpha(X, bycovFF=TRUE, a=0.35 )$alpha
# [1] 0.7695105 # Often used with probs-weighted moments
lalpha(X, bycovFF=TRUE, a=0.375 )$alpha
# [1] 0.771334 # Blom, nearly unbiased quantiles for normal
lalpha(X, bycovFF=TRUE, a=0.40 )$alpha
# [1] 0.7731661 # Cunnane, appox quantile unbiased
lalpha(X, bycovFF=TRUE, a=0.44 )$alpha
# [1] 0.7761157 # Gringorten, optimized for Gumbel
lalpha(X, bycovFF=TRUE, a=0.5 )$alpha
# [1] 0.7805825 # Hazen, traditional choice
# This the plotting position (i-0.5) / n
This is not a particularly pleasing situation because the choice of the plotting position affects the \alpha_L. The Hazen definition lalpha(X[,1:3], bycovFF=FALSE) using direct to L-comoments matches the last computation shown (\alpha_L = 0.7805825). A question, thus, is does this matching occur because of the nature of the ties and structure of the L-comoment algorithm itself? A note to this question involves a recognition that the answer is yes because L-comoments use a sort() operation and does not use rank() because the weights for the linear combinations are used and the covariance pathway 2*cov(x$X3, x$FX2), for instance.
Recognizing that the direct to L-comoments alpha equals the covariance pathway with Hazen plotting positions, let us look at L-comoments:
lmomco::Lcomoment.Lk12 ------> snippet
X12 <- X1[sort(X2, decreasing = FALSE, index.return = TRUE)$ix]
n <- length(X1)
SUM <- sum(sapply(1:n, function(r) { Lcomoment.Wk(k, r, n) * X12[r] }))
return(SUM/n)
Notice that a ties.method is not present but kind of implicit as ties first by the index return of the sort() and notice the return of a SUM/n though this is an L-comoment and not an nonexceedance probability.
Let us run through the tie options using a plotting position definition (i / n) matching the computations of Headrick and Sheng (2013) ("average", A=0, B=0 for pp) and the first computation \alpha_L = 0.807 matches that reported by Headrick and Sheng (2013, p. 4):
for(tie in c("average", "first", "last", "min", "max")) { # random left out
Lalpha <- lalpha(X, bycovFF=TRUE,
a=NULL, A=0, B=0, ties.method=tie)$alpha
message("Ties method ", stringr::str_pad(tie, 7, pad=" "),
" provides L-alpha = ", Lalpha)
}
# Ties method average provides L-alpha = 0.80747664
# Ties method first provides L-alpha = 0.78058252
# Ties method last provides L-alpha = 0.83243243
# Ties method min provides L-alpha = 0.81363468
# Ties method max provides L-alpha = 0.80120709
Let us run through the tie options again using a different plotting position estimator ((n-0.5) / (n+0.5)):
for(tie in c("average", "first", "last", "min", "max")) { # random left out
Lalpha <- lalpha(X, bycovFF=TRUE,
a=NULL, A=-0.5, B=0.5, ties.method=tie)$alpha
message("Ties method ", stringr::str_pad(tie, 7, pad=" "),
" provides L-alpha = ", Lalpha)
}
# Ties method average provides L-alpha = 0.78925733
# Ties method first provides L-alpha = 0.76230208
# Ties method last provides L-alpha = 0.81431215
# Ties method min provides L-alpha = 0.79543602
# Ties method max provides L-alpha = 0.78296931
We see obviously that the decision on how to treat ties has some influence on the computation involving the covariance pathway. This is not an entirely satisfactory situation, but perhaps the distinction does not matter much? The direct L-comoment pathway seems to avoid this issue because sort() is stable and like ties.method="first". Experiments suggest that a=0.5 (Hazen plotting positions) produces the same results as direct L-comoment (see the next section). However, as the following code set shows:
for(tie in c("average", "first", "last", "min", "max")) { # random left out
Lalpha1 <- lalpha(X, bycovFF=TRUE, a=0.5, ties.method=tie)$alpha
Lalpha2 <- lalpha(X, bycovFF=TRUE, a=NULL, A=-0.5, B=0, ties.method=tie)$alpha
Lalpha3 <- lalpha(X, bycovFF=TRUE, a=NULL, A=-1 , B=0, ties.method=tie)$alpha
Lalpha4 <- lalpha(X, bycovFF=TRUE, a=NULL, A= 0, B=0, ties.method=tie)$alpha
print(c(Lalpha1, Lalpha2, Lalpha3, Lalpha4))
}
The \alpha_L for a given tie setting are all equal as long as the demoninator of the plotting position ((i + A) / (n + B)) has B=0. The a=0.5 produces Hazen, the a=NULL, A=-0.5 produces Hazen, though a=NULL, A=-1 (lower limit of A) and a=NULL, A=0 (upper limit of A given B) also produces the same. This gives us as-implemented-proof that the sensitivity to the \alpha_L computation is in the sorting and the denominator of the plotting position formula. The prudent default settings for when the bycovFF argument is true seems to have the a=-0.5 as nonexceedance probabilities are computed by the well-known Hazen description and with the tie method as first, the computations match direct to L-comoments.
Demonstration of Computational Times—A considerable amount of documentation and examples are provided here about the two pathways that \alpha_L can be computed: (1) direct by L-comoments or (2) covariance pathway requiring precomputed estimates of the nonexceedance probabilities using a ties.method="first" (default pp). The following example shows numerical congruence between the two pathways if the so-called Hazen plotting positions (a=0.5, see pp) are requested with the implicit default of ties.method="first". However, the computational time of the direct method is quite a bit longer because of latencies in the weight factor computations involved in the L-comoments and nested for loops.
set.seed(1)
R <- 1:10; nsam <- 1E5 # random and uncorrelated scores in this measure
Z <- data.frame( I1=sample(R, nsam, replace=TRUE),
I2=sample(R, nsam, replace=TRUE),
I3=sample(R, nsam, replace=TRUE),
I4=sample(R, nsam, replace=TRUE) )
system.time(AnF <- headrick.sheng.lalpha(Z, bycovFF=FALSE)$alpha)
system.time(AwF <- headrick.sheng.lalpha(Z, bycovFF=TRUE )$alpha) # Hazen
# user system elapsed
# 30.382 0.095 30.501 AnF ---> 0.01370302
# user system elapsed
# 5.054 0.030 5.092 AwF ---> 0.01370302
Author(s)
W.H. Asquith
References
Headrick, T.C., and Sheng, Y., 2013, An alternative to Cronbach's alpha—An L-moment-based measure of internal-consistency reliability: in Millsap, R.E., van der Ark, L.A., Bolt, D.M., Woods, C.M. (eds) New Developments in Quantitative Psychology, Springer Proceedings in Mathematics and Statistics, v. 66, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-1-4614-9348-8_2")}.
Headrick, T.C., and Sheng, Y., 2013, A proposed measure of internal consistency reliability—Coefficient L-alpha: Behaviormetrika, v. 40, no. 1, pp. 57–68, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2333/bhmk.40.57")}.
Béland, S., Cousineau, D., and Loye, N., 2017, Using the McDonald's omega coefficient instead of Cronbach's alpha [French]: McGill Journal of Education, v. 52, no. 3, pp. 791–804, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.7202/1050915ar")}.
See Also
Lcomoment.matrix, pp
Examples
# Table 1 in Headrick and Sheng (2013)
TV1 <- # Observations in cols 1:3, estimated nonexceedance probabilities in cols 4:6
c(2, 4, 3, 0.15, 0.45, 0.15, 5, 7, 7, 0.75, 0.95, 1.00,
3, 5, 5, 0.35, 0.65, 0.40, 6, 6, 6, 0.90, 0.80, 0.75,
7, 7, 6, 1.00, 0.95, 0.75, 5, 2, 6, 0.75, 0.10, 0.75,
2, 3, 3, 0.15, 0.25, 0.15, 4, 3, 6, 0.55, 0.25, 0.75,
3, 5, 5, 0.35, 0.65, 0.40, 4, 4, 5, 0.55, 0.45, 0.40)
T1 <- matrix(ncol=6, nrow=10)
for(r in seq(1,length(TV1), by=6)) T1[(r/6)+1, ] <- TV1[r:(r+5)]
colnames(T1) <- c("X1", "X2", "X3", "FX1", "FX2", "FX3"); T1 <- as.data.frame(T1)
lco2 <- matrix(nrow=3, ncol=3)
lco2[1,1] <- lmoms(T1$X1)$lambdas[2]
lco2[2,2] <- lmoms(T1$X2)$lambdas[2]
lco2[3,3] <- lmoms(T1$X3)$lambdas[2]
lco2[1,2] <- 2*cov(T1$X1, T1$FX2); lco2[1,3] <- 2*cov(T1$X1, T1$FX3)
lco2[2,1] <- 2*cov(T1$X2, T1$FX1); lco2[2,3] <- 2*cov(T1$X2, T1$FX3)
lco2[3,1] <- 2*cov(T1$X3, T1$FX1); lco2[3,2] <- 2*cov(T1$X3, T1$FX2)
headrick.sheng.lalpha(lco2)$alpha # Headrick and Sheng (2013): alpha = 0.807
# 0.8074766
headrick.sheng.lalpha(Lcomoment.matrix(T1[,1:3], k=2)$matrix)$alpha
# 0.7805825
headrick.sheng.lalpha(T1[,1:3])$alpha # FXs not used: alpha = 0.781
# 0.7805825
headrick.sheng.lalpha(T1[,1:3], bycovFF=TRUE)$alpha # a=0.5, Hazen by default
# 0.7805825
headrick.sheng.lalpha(T1[,1:3], bycovFF=TRUE, a=0.5)$alpha
# 0.7805825
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