coefZ | R Documentation |
Computes Zij-values of item pairs, Zi-values of items, and Z-value of the entire scale,
which are used to test whether Hij, Hi, and H, respectively, are significantly
greater than zero using the original method Z
(Molenaar and Sijtsma, 2000, pp. 59-62; Sijtsma and Molenaar, p. 40; Van der Ark, 2007; 2010)
or the Wald-based method (WB
) or range-preserving method (RP
)
(Kuijpers et al., 2013; Koopman et al., in press a, in press b).
The Wald-based method and range-preserving method can also handle nested data and can test other lowerbounds than zero.
Used in the function aisp
coefZ(X, lowerbound = 0, type.z = "Z", level.two.var = NULL)
X |
matrix or data frame of numeric data
containing the responses of |
lowerbound |
Value of the null hypothesis to which the scalability are compared to compute the Z-score (see details),
0 <= |
type.z |
Indicates which type of z-score is computed: "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 and is only appropriate to test lowerbound = 0 (Mokken, 1971; Molenaar and Sijtsma, 2000; Sijtsma and Molenaar, 2002). The default is "Z". |
level.two.var |
vector of length |
For the estimated item-pair coefficient Hij
with standard error SE(Hij)
, the Z-score is computed as
Zij = (Hij - lowerbound) / SE(Hij)
if type.z = "WB"
, and the Z-score is computed as
Zij = -(log(1 - Hij) - log(1 - lowerbound)) / (SE(Hij) / (1 - Hij))
if type.z = "RP"
(Koopman et al., in press b).
For the estimate item-scalability coefficients Hi
and total-scalbility coefficients H
a similar procedure is used.
Standard errors of the Z-scores are not provided.
Zij |
matrix containing the Z-values of the item-pairs |
Zi |
vector containing Z-values of the items |
Z |
Z-value of the entire scale |
L. A. van der Ark L.A.vanderArk@uva.nl L. Koopman
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) \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0081175013481958")}
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v020.i11")}
Van der Ark, L. A. (2010). Getting started with Mokken scale analysis in R. Unpublished manuscript. https://sites.google.com/a/tilburguniversity.edu/avdrark/mokken
coefH
, aisp
data(acl)
Communality <- acl[,1:10]
# Compute the Z-score of each coefficient
coefH(Communality)
coefZ(Communality)
# Using lowerbound .3
coefZ(Communality, lowerbound = .3, type.z = "WB")
# Z-scores for nested data
data(autonomySupport)
scores <- autonomySupport[, -1]
classes <- autonomySupport[, 1]
coefH(scores, level.two.var = classes)
coefZ(scores, type.z = "WB", level.two.var = classes)
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