# Estimating congeneric reliability with R

I was recently tasked with estimating the reliability of scores composed of items on different scales. The lack of a common scale could be seen in widely varying item score means and variances. As discussed by Graham, it’s not widely known in applied educational research that Cronbach’s alpha assumes essential tau-equivalence and underestimates reliability when the assumption isn’t met. A friend made a good suggestion to consider stratified alpha, which is commonly used to estimate internal consistency when scores are composed of multiple choice items (scored 0-1) and constructed response items (e.g., scored 0-4). However, the assessment with which I was concerned does not have clear item-type strata. I decided to estimate congeneric reliability because it makes few assumptions (e.g., unidimensionality) and doesn’t require grouping items into essentially tau-equivalent strata.

With the help of Graham’s paper and a LISREL tutorial by Raykov I wrote an program that estimates congeneric reliability. The program uses Fox’s sem() library to conduct the confirmatory factor analysis with minimal constraints (variance of common true score fixed at 1 for identifiability). The estimated loadings and error variances are then summed to calculate reliability (i.e., the ratio of true score variance to observed score variance) as:

$hat{rho}_{XX'} = frac { left( sum hat{lambda}_i right) ^ 2} {left( sum hat{lambda}_i right) ^ 2 + sum hat{delta}_{i} }$.

I obtained a congeneric reliability estimate of 0.80 and an internal consistency estimate of 0.58 for the scores I was analyzing. If the items had been essentially tau-equivalent, then the reliability estimates would have been the same. If I had assumed tau-equivalence, than I would have underestimated the reliability of the total scores (and overestimated standard errors of measurement). The example below replicates Graham’s heuristic example.

```> ############################################
> #Replicate results from Graham (2006) to check congeneric reliability code/calculations.
>
> library(sem)
> library(psych)
> library(stringr)
>
> #Variance/covariance matrix
> S.graham <- readMoments(diag = T, names = paste("x", 1:7, sep = ""))
1:  4.98
2:  4.60  5.59
4:  4.45  4.42  6.30
7:  3.84  3.81  3.66  6.44
11:  5.71  5.67  5.52  4.91 11.86
16: 23.85 23.68 22.92 19.87 34.28 127.65
22: 46.53 46.20 44.67 38.57 62.30 244.36 471.95
29:
>
> ############################################
> #A function to estimate and compare congeneric reliability and internal consistency
> funk.congeneric <- function(cfa.out) {
+   names.errors <- str_detect(names(cfa.out\$coeff), "error")
+   round(c("Congeneric" = r.congeneric, "Alpha" = alpha(cfa.out\$S)\$total\$raw_alpha), 2)
+ }
>
> ############################################
> #Congeneric model; tau-equivalent items
> model.graham <- specifyModel()
6: x1 <-> x1, error1
7: x2 <-> x2, error2
8: x3 <-> x3, error3
9: x4 <-> x4, error4
10: x5 <-> x5, error5
11: T <-> T, NA, 1
12:
> cfa.out <- sem(model = model.graham, S = S.graham, N = 60)
> summary(cfa.out)
Model Chisquare =  0.11781   Df =  5 Pr(>Chisq) = 0.99976
Chisquare (null model) =  232.13   Df =  10
Goodness-of-fit index =  0.9992
RMSEA index =  0   90% CI: (NA, NA)
Bentler-Bonnett NFI =  0.99949
Tucker-Lewis NNFI =  1.044
Bentler CFI =  1
SRMR =  0.0049092
AIC =  20.118
AICc =  4.6076
BIC =  41.061
CAIC =  -25.354
Normalized Residuals
Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
-0.038700 -0.008860 -0.000002  0.004700  0.002450  0.119000
R-square for Endogenous Variables
x1     x2     x3     x4     x5
0.9286 0.8175 0.6790 0.4962 0.5968
Parameter Estimates
Estimate Std Error z value Pr(>|z|)
error1   0.35559  0.17469   2.0356  4.1793e-02 x1 <--> x1
error2   1.02000  0.25339   4.0255  5.6861e-05 x2 <--> x2
error3   2.02222  0.41688   4.8509  1.2293e-06 x3 <--> x3
error4   3.24471  0.62679   5.1767  2.2583e-07 x4 <--> x4
error5   4.78227  0.94911   5.0387  4.6871e-07 x5 <--> x5
Iterations =  21
> pathDiagram(cfa.out, edge.labels = "values", ignore.double = F, rank.direction = "TB")
```

```> funk.congeneric(cfa.out)
Congeneric      Alpha
0.91       0.91
>
> ############################################
> #Congeneric model; tau-inequivalent items
> model.graham <- specifyModel()
6: x1 <-> x1, error1
7: x2 <-> x2, error2
8: x3 <-> x3, error3
9: x4 <-> x4, error4
10: x7 <-> x7, error7
11: T <-> T, NA, 1
12:
> cfa.out <- sem(model = model.graham, S = S.graham, N = 60)
> summary(cfa.out)
Model Chisquare =  0.0072298   Df =  5 Pr(>Chisq) = 1
Chisquare (null model) =  353.42   Df =  10
Goodness-of-fit index =  0.99995
RMSEA index =  0   90% CI: (NA, NA)
Bentler-Bonnett NFI =  0.99998
Tucker-Lewis NNFI =  1.0291
Bentler CFI =  1
SRMR =  0.0010915
AIC =  20.007
AICc =  4.497
BIC =  40.951
CAIC =  -25.464
Normalized Residuals
Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
-2.76e-02 -1.27e-04 -1.00e-07 -1.92e-03  1.96e-04  3.70e-03
R-square for Endogenous Variables
x1     x2     x3     x4     x7
0.9303 0.8171 0.6778 0.4942 0.9902
Parameter Estimates
Estimate Std Error z value  Pr(>|z|)
error1    0.34688 0.090227   3.84451 1.2080e-04 x1 <--> x1
error2    1.02240 0.200635   5.09584 3.4720e-07 x2 <--> x2
error3    2.02973 0.382466   5.30694 1.1148e-07 x3 <--> x3
error4    3.25764 0.605376   5.38118 7.3998e-08 x4 <--> x4
error7    4.64661 6.387765   0.72742 4.6697e-01 x7 <--> x7
Iterations =  38
> pathDiagram(cfa.out, edge.labels = "values", ignore.double = F, rank.direction = "TB")
```

```> funk.congeneric(cfa.out)
Congeneric      Alpha
0.99       0.56
```

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