PerFit-package: Person Fit

Description Details Author(s) References Examples

Description

Person fit consists of a set of techniques aimed at detecting unusual responses to tests or questionnaires. There are several person-fit statistics available in the literature, see Karabatsos (2003) and Meijer and Sijtsma (2001) for comprehensive reviews. Both dichotomous and polytomous types of items are considered. This R-package outputs the values of the chosen person-fit statistic, the IDs of the respondents that were flagged, and plots the sample distribution of the scores of the person-fit statistic. Nonparametric person response functions (Sijtsma and Meijer, 2001) may also be requested in order to help interpreting individual answering behaviors (dichotomous data only).

Details

Package: PerFit
Type: Package
Version: 1.4
Date: 2015-07-13
License: GPL Version 2 or later

The PerFit package contains several person-fit functions. The goal is to detect response vectors that seem to be strange in terms of the sample of respondents or in terms to an item response theory (IRT) model.

There are many person-fit statistics available in the literature. Statistics are typically categorized according to the type of items (Dicho = dichotomous, Poly = polytomous) and the type of IRT model (NParam=nonparametric, Param=parametric) that they apply to. The current version of PerFit includes the following statistics:

Person-fit statistic (R function) Reference Type item Type IRT model
r.pbis Donlon and Fisher (1968) Dicho NParam
C.Sato Sato (1975) Dicho NParam
G, Gnormed van der Flier (1977), Meijer (1994) Dicho NParam
A.KB, D.KB, E.KB Kane and Brennan (1980) Dicho NParam
U3, ZU3 van der Flier (1980, 1982) Dicho NParam
Cstar Harnisch and Linn (1981) Dicho NParam
NCI Tatsuoka and Tatsuoaka (1982, 1983) Dicho NParam
lz Drasgow, Levine, and Williams (1985) Dicho Param
lzpoly Drasgow, Levine, and Williams (1985) Poly Param
Ht Sijtsma (1986) Dicho NParam
Gpoly Molenaar (1991) Poly NParam
Gnormed.poly Molenaar (1991), Emons (2008) Poly NParam
lzstar Snijders (2001) Dicho Param
U3poly Emons (2008) Poly NParam

All functions above have an output of class PerFit.

The package provides other functions that help analyzing the data when conducting person-fit analyses:

Function Description
cutoff Estimate cutoff values for the person-fit statistics, to be used as decision rules.
flagged.resp Identify which respondents were flagged according to the chosen cutoff.
plot (class PerFit) Plot the distribution of person-fit scores with the cutoff superimposed.
PRFplot Plot the nonparametric person response function (Sijtsma and Meijer, 2001).

More person-fit statistics will be added to the package in future updates.

Versions

Author(s)

Jorge N. Tendeiro

Maintainer: Jorge N. Tendeiro j.n.tendeiro@rug.nl

References

Donlon, T. F., and Fischer, F. E. (1968) An index of an individual's agreement with group-defined item difficulties. Educational and Psychological Measurement, 28(1), 105–113.

Drasgow, F., Levine, M. V., and Williams, E. A. (1985) Appropriateness measurement with polychotomous item response models and standardized indices. British Journal of Mathematical and Statistical Psychology, 38(1), 67–86.

Emons, W. M. (2008) Nonparametric person-fit analysis of polytomous item scores. Applied Psychological Measurement, 32(3), 224–247.

Harnisch, D. L., and Linn, R. L. (1981) Analysis of item response patterns: Questionable test data and dissimilar curriculum practices. Journal of Educational Measurement, 18(3), 133–146.

Kane, M. T., and Brennan, R. L. (1980) Agreement coefficients as indices of dependability for domain-referenced tests. Applied Psychological Measurement, 4(1), 105–126.

Karabatsos, G. (2003) Comparing the Aberrant Response Detection Performance of Thirty-Six Person-Fit Statistics. Applied Measurement In Education, 16(4), 277–298.

Meijer, R. R. (1994) The number of Guttman errors as a simple and powerful person-fit statistic. Applied Psychological Measurement, 18(4), 311–314.

Meijer, R. R., and Sijtsma, K. (2001) Methodology review: Evaluating person fit. Applied Psychological Measurement, 25(2), 107–135.

Molenaar, I. W. (1991) A weighted Loevinger H-coefficient extending Mokken scaling to multicategory items. Kwantitatieve Methoden, 12(37), 97–117.

Sato, T. (1975) The construction and interpretation of S-P tables. Tokyo: Meiji Tosho.

Sijtsma, K. (1986) A coefficient of deviance of response patterns. Kwantitatieve Methoden, 7, 131–145.

Sijtsma, K., and Meijer, R. R. (2001) The person response function as a tool in person-fit research. Psychometrika, 66(2), 191–207.

Snijders, T. B. (2001) Asymptotic null distribution of person fit statistics with estimated person parameter. Psychometrika, 66(3), 331–342.

Tatsuoka, K. K., and Tatsuoka, M. M. (1982) Detection of aberrant response patterns and their effect on dimensionality. Journal of Educational Statistics, 7(3), 215–231.

Tatsuoka, K. K., and Tatsuoka, M. M. (1983) Spotting erroneous rules of operation by the individual consistency index. Journal of Educational Measurement, 20(3), 221–230.

Tendeiro, J. N., Meijer, R. R., and Niessen, A. S. M. (2016). PerFit: An R Package for Person-Fit Analysis in IRT. Journal of Statistical Software, 74(5), 1–27.

van der Flier, H. (1977) Environmental factors and deviant response patterns. In Y. H. Poortinga (Ed.), Basic problems in cross-cultural psychology. Amsterdam: The Netherlands.

van der Flier, H. (1980) Vergelijkbaarheid van individuele testprestaties [Comparability of individual test performance]. Lisse: The Netherlands.

van der Flier, H. (1982) Deviant response patterns and comparability of test scores. Journal of Cross-Cultural Psychology, 13(3), 267–298.

Examples

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# Load the inadequacy scale data (dichotomous item scores):
data(InadequacyData)

# As an example, compute the Ht person-fit scores:
Ht.out <- Ht(InadequacyData)
# Ht.out$PFscores

# Compute the cutoff value at 1% level:
set.seed(124) # To fix the random seed generator.
Ht.cut <- cutoff(Ht.out, Blvl=.01)

# Plot the sample distribution of the Ht scores with the above cutoff superimposed:
plot(Ht.out, cutoff.obj=Ht.cut)

# Determine which respondents were flagged by Ht at 1% level:
flagged.resp(Ht.out, cutoff.obj=Ht.cut, scores=FALSE)
# Flagged respondents: 30, 37, 46, 49,...

# Plot the person response function of respondent 30 (flagged as aberrant):
Resp30 <- PRFplot(InadequacyData, respID=30)
# Plot the person response function of respondent 35 (not flagged as aberrant):
Resp35 <- PRFplot(InadequacyData, respID=35)

PerFit documentation built on May 31, 2017, 5 a.m.

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