What can we learn from Plausible Values?

Psychometrika

Volumn 81, Issue 2, 2016, Pages 274-289

  Marsman, Maarten ^a,b   Maris, Gunter ^a,b   Bechger, Timo ^b   Glas, Cees ^c  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b Cito group   (Netherlands)

^c UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Bayesian theory; educational surveys; item response theory; plausible values

Indexed keywords

BAYES THEOREM; EDUCATION; HUMAN; PSYCHOMETRY; QUESTIONNAIRE; STATISTICS; THEORETICAL MODEL;

BAYES THEOREM; EDUCATIONAL MEASUREMENT; HUMANS; MODELS, THEORETICAL; PSYCHOMETRICS; STATISTICS AS TOPIC; SURVEYS AND QUESTIONNAIRES;

EID: 84962646200     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11336-016-9497-x     Document Type: Article

References (16)

1. Chang, H. (1996). The asymptotic posterior normality of the latent trait for polytomous IRT models. Psychometrika, 61(3), 445–463.
2. Chang, H., & Stout, W. (1993). The asymptotic posterior normality of the latent trait in an IRT model. Psychometrika, 58, 37–52.
3. Cover, T., & Thomas, J. (1991). Elements of information theory. New York: Wiley-Interscience.
4. DasGupta, A. (2008). Asymptotic theory of statistics and probability. New York: Springer.
5. Gelman, A., Carlin, B., Stern, H., & Rubin, D. (2004). Bayesian data analysis. Boca Raton: Chapman & Hall/CRC.
6. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.
7. Kreiner, S., & Christensen, K. (2014). Analyses of model fit and robustness. A new look at the PISA scaling model underlying ranking of countries according to reading literacy. Psychometrika, 79(2), 210–231.
8. Kullback, S., & Leibler, R. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22, 79–86.
9. Mislevy, R. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56, 177–196.
10. Mislevy, R., Beaton, A., Kaplan, B., & Sheehan, K. (1993). Estimating population characteristics from sparse matrix samples of item responses. Journal of Educational Measurement, 29, 133–161.
11. Schofield, L., Junker, B., Taylor, L., & Black, D. (2015). Predictive inference using latent variables with covariates. Psychometrika, 80(3), 727–747.
12. Venkatesh, S. (2013). The theory of probability: Explorations and applications. Cambridge: Cambridge University Press.
13. Verhelst, N., & Glas, C. (1995). The one parameter logistic model: OPLM. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch models: Foundations, recent developments and applications (pp. 215–238). New York: Springer.
14. Verhelst, N., Glas, C., & Verstralen, H. (1995). OPLM: Computer program and manual (Computer software manual). Arnhem: Cito.
15. Warm, T. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427–450.
16. White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica, 50, 1–25.