# cba

##### Calculate Cronbach's Alpha from supplied variables.

Calculates Cronbach's Alpha, a very commonly used index for assessing the reliability / internal consistency of a sum-score. Often interpreted as the mean correlation across all possible split-half alternate forms of the test.

##### Usage

`cba(x)`

##### Arguments

- x
A data-frame or matrix of numerical values where rows are across-items within-respondent observation vectors, and columns are within-item across-respondents observation vectors.

##### Value

Cronbach's Alpha for the sum-score of supplied variables.

##### Note

Missing values are treated by passing `na.rm = TRUE`

to the `var`

function call.

Be aware that this function does not issue a warning if there are negative correlations between variables in the supplied data-set.

##### References

Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika 16, 297--334. doi: 10.1007/BF02310555

##### Examples

```
# NOT RUN {
# Generate some fictional data. Say 100 students take a 50-item long test
# where all items are equally difficult.
set.seed(1234)
p.success <- rBeta.4P(100, .25, .75, 5, 3)
for (i in 1:50) {
if (i == 1) {
rawdata <- matrix(nrow = 100, ncol = 50)
}
rawdata[, i] <- rbinom(100, 1, p.success)
}
# To calculate Cronbach's Alpha for this test:
cba(rawdata)
# }
```

*Documentation reproduced from package betafunctions, version 1.2.2, License: CC0*