aisp {mokken} | R Documentation |
Automated Item Selection Procedure (AISP) for Mokken Scale Analysis
Description
Returns a matrix with as many rows as there are items, indicating to which scale an item belongs for each lowerbound.
Usage
aisp(X, lowerbound=.3, search="normal", alpha=.05, StartSet=FALSE, popsize=20,
maxgens=default.maxgens, pxover=0.5, pmutation=0.1, verbose=FALSE,
type.z = "Z", test.Hi = FALSE, level.two.var = NULL)
Arguments
X |
matrix or data frame of numeric data
containing the responses of |
search |
Type of item selection procedure: "normal": Mokken's automated item selection procedure (Mokken, 1971; Molenaar & Sijtsma, 2000; Sijtsma & Molenaar, 2002); "ga": item selection using a genetic algorithm (Straat, van der Ark, & Sijtsma, 2013). The default is "normal". |
lowerbound |
Value or vector with numeric scaling criteria; 0 <= |
alpha |
Type I error level. The default is |
StartSet |
Startset of items for the first scale. Vector of item numbers. If |
popsize |
Size of the population of items in genetic. algorithm The default is |
maxgens |
Number of generations in genetic algorithm. The default is |
pxover |
Cross-over probability in genetic algorithm. The default is |
pmutation |
Mutation probability in genetic algorithm. The default is |
verbose |
Logical, indicating whether should output to the screen the results of the model. If |
type.z |
Indicates which type of Z-test is used to evaluate whether coefficients meet the scaling criteria: "WB": Wald-based z-score based on standard errors as approximated by the delta method (Kuijpers et al., 2013; Koopman et al., in press a); "RP": Range-preserving z-score, also based on the delta method (Koopman, et al., in press b); "Z": uses original Z-test (Mokken, 1971; Molenaar & Sijtsma, 2000; Sijtsma & Molenaar, 2002). The default is "Z", but is changed to "WB" for test.Hi == TRUE or if a level.two.var is given. |
test.Hi |
If |
level.two.var |
vector of length |
Details
Each scale must consist of at least two items, hence the number of Mokken scales cannot exceed ncol(X)/2
.
Procedure may be slow for large data sets. Especially if the genetic algorithm is used.
There is not yet an option search="extended"
.
aisp
replaces the function search.normal
in earlier versions.
Value
An matrix with J rows. Each entry refers to an item. Items with same integer belong to the same Mokken scale. A zero indicates an unscalable item. If n is the largest integer, then n Mokken scales were found.
Author(s)
L. A. van der Ark L.A.vanderArk@uva.nl, J. H. Straat, L. Koopman
References
Koopman, L., Zijlstra, B. J. H., & Van der Ark, L. A. (in press a). A two-step, test-guided Mokken scale analysis for nonclustered and clustered data. Quality of Life Research. (advanced online publication) doi:10.1007/s11136-021-02840-2
Koopman, L., Zijlstra, B. J. H., & Van der Ark, L. A. (in press b). Range-preserving confidence intervals and significance tests for scalability coefficients in Mokken scale analysis. In M. Wiberg, D. Molenaar, J. Gonzalez, & Kim, J.-S. (Eds.), Quantitative Psychology; The 1st Online Meeting of the Psychometric Society, 2020. Springer. doi:10.1007/978-3-030-74772-5_16
Kuijpers, R. E., Van der Ark, L. A., & Croon, M. A. (2013). Standard errors and confidence intervals for scalability coefficients in Mokken scale analysis using marginal models. Sociological Methodology, 43, 42-69. doi:10.1177/0081175013481958
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.
Straat, J. H., Van der Ark, L. A., & Sijtsma, K. (2013). Comparing optimization algorithms for item selection in Mokken scale analysis. Journal of Classification, 30, 72-99. doi:10.1007/s00357-013-9122-y
Van der Ark, L. A. (2007). Mokken scale analysis in R. Journal of Statistical Software. doi:10.18637/jss.v020.i11
Van der Ark, L. A. (2012). New developments in Mokken scale analysis in R. Journal of Statistical Software, 48. doi:10.18637/jss.v048.i05
See Also
coefH
, check.iio
, check.monotonicity
, check.pmatrix
, check.reliability
,check.restscore
Examples
data(acl)
# Select the scale Communality consisting of 10 items.
Communality <- acl[,1:10]
# Partition these 10 items into mokken scales using Mokken's automated item selection procedure.
scale <- aisp(Communality)
coefH(Communality[,scale==1], se = FALSE)
# Same but using items 1 and 2 in the startset.
scale <- aisp(Communality, StartSet = c(1, 2), verbose = TRUE)
coefH(Communality[,scale==1])
# Perform aisp for increasing lowerbounds
scales <- aisp(Communality, lowerbound = seq(0, .55, .05))
scales
# Use a significant test for criteria Hi > c (rather than the point estimate)
scale <- aisp(Communality, type.z = "WB", test.Hi = TRUE, verbose = TRUE)
coefH(Communality[,scale==1])
# Partition these 10 items into mokken scales using a genetic algorithm.
scale <- aisp(Communality,search="ga",maxgens=1000)
coefH(Communality[,scale==1])
# Perform aisp on two-level data
data(autonomySupport)
scores <- autonomySupport[, -1]
classes <- autonomySupport[, 1]
scale <- aisp(scores, type.z = "WB", level.two.var = classes)
coefH(scores[, scale==1], level.two.var = classes)