check.errors | R Documentation |
Returns a lost containing outlier scores Gplus (number of Guttman errors; Guttman, 1944) and Oplus for each respondent (Zijlstra, van der Ark & Sijtsma, 2007).
check.errors(X, returnGplus = TRUE, returnOplus = FALSE)
X |
matrix or data frame of numeric data
containing the responses of |
returnGplus |
Boolean. If |
returnOplus |
Boolean. If |
List. Depending on the values of returnGplus
and returnOplus
, the output contains outlier score Gplus (the number of Guttman errors)
and Oplusfor each respondent
L. A. van der Ark L.A.vanderArk@uva.nl
Guttman, L. (1944) A basis for scaling qualitative data. American Sociological Review, 9, 139-150.
Meijer, R. R. (1994) The number of Guttman errors as a simple and powerful person-fit statistic. Applied Psychological Measurement, 18, 311-314. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/014662169401800402")}
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v020.i11")}
Zijlstra, W. P., Van der Ark, L. A., & Sijtsma, K. (e2007). Outlier detection in test and questionnaire data. Multivariate Behavioral Research, 42, 531-555. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273170701384340")}
check.ca
,
check.iio
,
check.monotonicity
,
check.pmatrix
,
check.reliability
coefH
,
plot.restscore.class
,
summary.restscore.class
data(acl)
Communality <- acl[,1:10]
Gplus <- check.errors(Communality, TRUE, FALSE)$Gplus
Oplus <- check.errors(Communality, FALSE, TRUE)$Oplus
hist(Gplus, breaks = 0:max(Gplus))
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