R: Computes local dependence indices for all pairs of items

ChenThissen1997 {rpf} | R Documentation

## Computes local dependence indices for all pairs of items

### Description

Item Factor Analysis makes two assumptions: (1) that the latent distribution is reasonably approximated by the multivariate Normal and (2) that items are conditionally independent. This test examines the second assumption. The presence of locally dependent items can inflate the precision of estimates causing a test to seem more accurate than it really is.

### Usage

    ChenThissen1997(grp, ..., data = NULL, inames = NULL, qwidth = 6,
      qpoints = 49, method = "pearson", .twotier = TRUE, .parallel = TRUE)

### Arguments

`grp` | a list with the spec, param, mean, and cov describing the group
`...` | Not used. Forces remaining arguments to be specified by name.
`data` | data
`inames` | a subset of items to examine
`qwidth` | quadrature width
`qpoints` | number of equally spaced quadrature points
`method` | method to use to calculate P values. The default is the Pearson X^2 statistic. Use "lr" for the similar likelihood ratio statistic.
`.twotier` | whether to enable the two-tier optimization
`.parallel` | whether to take advantage of multiple CPUs (default TRUE)

### Details

Statically significant entries suggest that the item pair has local dependence. Since log(.01)=-4.6, an absolute magitude of 5 is a reasonable cut-off. Positive entries indicate that the two item residuals are more correlated than expected. These items may share an unaccounted for latent dimension. Consider a redesign of the items or the use of testlets for scoring. Negative entries indicate that the two item residuals are less correlated than expected.

### Value

a list with raw, pval and detail. The pval matrix is a lower triangular matrix of log P values with the sign determined by relative association between the observed and expected tables (see `ordinal.gamma`)

### References

Chen, W.-H. & Thissen, D. (1997). Local dependence indexes for item pairs using Item Response Theory. Journal of Educational and Behavioral Statistics, 22(3), 265-289.

Thissen, D., Steinberg, L., & Mooney, J. A. (1989). Trace lines for testlets: A use of multiple-categorical-response models. Journal of Educational Measurement, 26 (3), 247–260.

Wainer, H. & Kiely, G. L. (1987). Item clusters and computerized adaptive testing: A case for testlets. Journal of Educational measurement, 24(3), 185–201.

### See Also

ifaTools

[Package rpf version 0.56.1 Index]