| Package: |
| sirt |
| Type: |
| Package |
| Version: |
| 1.14 |
| Publication Year: |
| 2017 |
| License: |
| GPL (>= 2) |
This package enables the estimation of following models:
rasch.mml2 and the argument itemtype="raschtype".
This model also allows the estimation of the 4PL item
response model (Loken & Rulison, 2010).
Multiple group estimation, latent regression models and
plausible value imputation are supported. In addition, pseudo-likelihood
estimation for fractional item response data can be conducted.
smirt function and the options irtmodel="noncomp"
, irtmodel="comp" and irtmodel="partcomp".
rasch.mml2 with itemtype="ramsay.qm".
rasch.mml2 with itemtype="npirt".
Kernel smoothing for item response function estimation (Ramsay, 1991)
is implemented in np.dich.
rasch.copula3.
rasch.jml function. Bias correction methods
for item parameters are included in rasch.jml.jackknife1
and rasch.jml.biascorr.
rasch.mirtlc.
rm.facets. A hierarchical rater model based on
signal detection theory (DeCarlo, Kim & Johnson, 2011) can be conducted
with rm.sdt. A simple latent class model for two exchangeable
raters is implemented in lc.2raters.
gom.em.
mcmc.2pno.ml.
rasch.pairwise or
rasch.pairwise.itemcluster.
rasch.pml3. In this function
local dependence can be handled by imposing residual error structure
or omitting item pairs within a dependent item cluster from the
estimation.
The function rasch.evm.pcm estimates the mutiple group
partial credit model based on the pairwise eigenvector approach
which avoids iterative estimation.
mcmc.2pno
the two-parameter normal ogive model can be estimated. A hierarchical
version of this model (Janssen, Tuerlinckx, Meulders & de Boeck, 2000)
is implemented in mcmc.2pnoh. The 3PNO testlet model
(Wainer, Bradlow & Wang, 2007; Glas, 2012) can be estimated with
mcmc.3pno.testlet.
Some hierarchical IRT models and random item models
(van den Noortgate, de Boeck & Meulders, 2003) can be estimated
with mcmc.2pno.ml.
R2noharm runs NOHARM from within R. Note that NOHARM must be
downloaded from http://noharm.niagararesearch.ca/nh4cldl.html
at first. A pure R implementation of the NOHARM model with some extensions
can be found in noharm.sirt.
isop.dich or isop.poly.
Item scoring within this theory can be conducted with
isop.scoring.
f1d.irt.
rasch.va.
prob.guttman.
wle.rasch.jackknife.
greenyang.reliability and the
marginal true score method of Dimitrov (2003) using the function
marginal.truescore.reliability.
conf.detect.
linking.haberman. See also
equating.rasch and linking.robust.
The alignment procedure (Asparouhov & Muthen, 2013)
invariance.alignment is originally for comfirmatory factor
analysis and aims at obtaining approximate invariance.
personfit.stat.
lsdm.
lsem.estimate function.
amh function.
Deterministic optimization of the posterior distribution (maximum
posterior estimation or penalized maximum likelihood estimation) can be
conduction with the pmle function which is based on
stats::optim. Note that the amh and
pmle functions are not particularly created for use with
random effects models like IRT models. For these model classes, the
xxirt function is more appropriate.
mlnormal
function. Prior distributions or regularization methods (lasso penalties)
are also accomodated. Missing values on dependent variables can be
treated by full information maximum likelihood methods.
Bartolucci, F. (2007). A class of multidimensional IRT models for testing unidimensionality and clustering items. Psychometrika, 72, 141-157.
Braeken, J. (2011). A boundary mixture approach to violations of conditional independence. Psychometrika, 76, 57-76.
DeCarlo, T., Kim, Y., & Johnson, M. S. (2011). A hierarchical rater model for constructed responses, with a signal detection rater model. Journal of Educational Measurement, 48, 333-356.
Dimitrov, D. (2003). Marginal true-score measures and reliability for binary items as a function of their IRT parameters. Applied Psychological Measurement, 27, 440-458.
Dimitrov, D. M. (2007). Least squares distance method of cognitive validation and analysis for binary items using their item response theory parameters. Applied Psychological Measurement, 31, 367-387.
Erosheva, E. A., Fienberg, S. E., & Joutard, C. (2007). Describing disability through individual-level mixture models for multivariate binary data. Annals of Applied Statistics, 1, 502-537.
Fox, J.-P., & Verhagen, A.-J. (2010). Random item effects modeling for cross-national survey data. In E. Davidov, P. Schmidt, & J. Billiet (Eds.), Cross-cultural Analysis: Methods and Applications (pp. 467-488), London: Routledge Academic.
Glas, C. A. W. (2012). Estimating and testing the extended testlet model. LSAC Research Report Series, RR 12-03.
Green, S.B., & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74, 155-167.
Haberman, S. J. (2009). Linking parameter estimates derived from an item respone model through separate calibrations. ETS Research Report ETS RR-09-40. Princeton, ETS.
Ip, E. H., Molenberghs, G., Chen, S. H., Goegebeur, Y., & De Boeck, P. (2013). Functionally unidimensional item response models for multivariate binary data. Multivariate Behavioral Research, 48, 534-562.
Janssen, R., Tuerlinckx, F., Meulders, M., & de Boeck, P. (2000). A hierarchical IRT model for criterion-referenced measurement. Journal of Educational and Behavioral Statistics, 25, 285-306.
Linacre, J. M. (1994). Many-Facet Rasch Measurement. Chicago: MESA Press.
Loken, E. & Rulison, K. L. (2010). Estimation of a four-parameter item response theory model. British Journal of Mathematical and Statistical Psychology, 63, 509-525.
McDonald, R. P. (1997). Normal-ogive multidimensional model. In W. van der Linden & R. K. Hambleton (1997): Handbook of modern item response theory (pp. 257-269). New York: Springer.
Meijer, R. R., & Sijtsma, K. (2001). Methodology review: Evaluating person fit. Applied Psychological Measurement, 25, 107-135.
Proctor, C. H. (1970). A probabilistic formulation and statistical analysis for Guttman scaling. Psychometrika, 35, 73-78.
Ramsay, J. O. (1989). A comparison of three simple test theory models. Psychometrika, 54, 487-499.
Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation. Psychometrika, 56, 611-630.
Reckase, M.D. (2009). Multidimensional item response theory. New York: Springer.
Rijmen, F., & Vomlel, J. (2008). Assessing the performance of variational methods for mixed logistic regression models. Journal of Statistical Computation and Simulation, 78, 765-779.
Renard, D., Molenberghs, G., & Geys, H. (2004). A pairwise likelihood approach to estimation in multilevel probit models. Computational Statistics & Data Analysis, 44, 649-667.
Rossi, N., Wang, X. & Ramsay, J. O. (2002). Nonparametric item response function estimates with the EM algorithm. Journal of Educational and Behavioral Statistics, 27, 291-317.
Rusch, T., Mair, P., & Hatzinger, R. (2013). Psychometrics with R: A Review of CRAN Packages for Item Response Theory. http://epub.wu.ac.at/4010/1/resrepIRThandbook.pdf.
Scheiblechner, H. (1995). Isotonic ordinal probabilistic models (ISOP). Psychometrika, 60, 281-304.
Scheiblechner, H. (1999). Additive conjoint isotonic probabilistic models (ADISOP). Psychometrika, 64, 295-316.
Stout, W., Habing, B., Douglas, J., & Kim, H. R. (1996). Conditional covariance-based nonparametric multidimensionality assessment. Applied Psychological Measurement, 20, 331-354.
Stukel, T. A. (1988). Generalized logistic models. Journal of the American Statistical Association, 83, 426-431.
Uenlue, A., & Yanagida, T. (2011). R you ready for R?: The CRAN psychometrics task view. British Journal of Mathematical and Statistical Psychology, 64, 182-186.
van den Noortgate, W., De Boeck, P., & Meulders, M. (2003). Cross-classification multilevel logistic models in psychometrics. Journal of Educational and Behavioral Statistics, 28, 369-386.
Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427-450.
Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet response theory and its applications. Cambridge: Cambridge University Press.
Zwinderman, A. H. (1995). Pairwise parameter estimation in Rasch models. Applied Psychological Measurement, 19, 369-375.
For conditional maximum likelihood estimation see the eRm package.
For pairwise estimation likelihood methods (also known as composite likelihood methods) see pln or lavaan.
The estimation of cognitive diagnostic models is possible using the CDM package.
For the multidimensional latent class IRT model see the MultiLCIRT package which also allows the estimation IRT models with polytomous item responses.
Latent class analysis can be carried out with covLCA, poLCA, BayesLCA, randomLCA or lcmm packages.
Markov Chain Monte Carlo estimation for item response models can also
be found in the MCMCpack package (see the MCMCirt functions
therein).
See Rusch, Mair and Hatzinger (2013) and Uenlue and Yanagida (2011) for reviews of psychometrics packages in R.
##
## |-----------------------------------------------------------------|
## | sirt 0.40-4 (2013-11-26) |
## | Supplementary Item Response Theory |
## | Maintainer: Alexander Robitzsch <a.robitzsch at bifie.at > |
## | https://sites.google.com/site/alexanderrobitzsch/software |
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