mokken (version 3.1.2)

summary.monotonicity.class: Summarize monotonicity.class objects

Description

S3 Method for summary of objects of class monotonicity.class. Summarizes checks of monotonicity

Usage

# S3 method for monotonicity.class
summary(object, ...)

Value

Matrix with ncol(X) rows and 10 columns, showing for each item a summary of the violations of monotonicity:

itemH = Item-scalability coefficient;

#ac = number of active pairs that were investigated;

#vi = number of violations in which the item is involved;

#vi/#ac = propotion of active pairs that is involved in a violation;

maxvi = maximum violation;

sum = sum of all violations;

zmax = maximum z-value;

zsig = number of significant z-values;

crit = Crit value (Molenaar & Sijtsma, 2000, pp. 49, 74).

Arguments

object

list produced by check.monotonicity

...

Optional parameters will be ignored

Author

L. A. van der Ark

References

Koopman, L., Zijlstra, B. J. H., & Van der Ark, L. A. (2023a). Assumptions and Properties of Two-Level Nonparametric Item Response Theory Models. Manuscript submitted for publication.

Koopman, L., Zijlstra, B. J. H., & Van der Ark, L. A. (2023b). Evaluating Model Fit in Two-Level Mokken Scale Analysis. Manuscript submitted for publication.

Mokken, R. J. (1971) A Theory and Procedure of Scale Analysis. De Gruyter.

Molenaar, I.W., & Sijtsma, K. (2000) User's Manual MSP5 for Windows [Software manual]. IEC ProGAMMA.

Sijtsma, K., & Molenaar, I. W. (2002) Introduction to nonparametric item response theory. Sage.

Van der Ark, L. A. (2007). Mokken scale analysis in R. Journal of Statistical Software. tools:::Rd_expr_doi("10.18637/jss.v020.i11")

See Also

check.monotonicity, plot.monotonicity.class

Examples

data(acl)
Communality <- acl[,1:10]
monotonicity.list <- check.monotonicity(Communality)
plot(monotonicity.list)
summary(monotonicity.list)

# Compute two-level fit statistics (Koopman et al., 2023a, 2023b)
data("autonomySupport")
dat <- autonomySupport[, -1]
groups <- autonomySupport[, 1]
autonomyMM <- check.monotonicity(dat, level.two.var = groups)
summary(autonomyMM)