Explanatory Multidimensional Multilevel Random Item Response Model: An Application to Simultaneous Investigation of Word and Person Contributions to Multidimensional Lexical Representations

Psychometrika

Volumn 78, Issue 4, 2013, Pages 830-855

  Cho, Sun Joo ^a   Gilbert, Jennifer K ^a   Goodwin, Amanda P ^a  

^a VANDERBILT UNIVERSITY   (United States)

Author keywords

lexical representation; multidimensional item response model; multilevel item structure; random item parameters

Indexed keywords

ADOLESCENT; FEMALE; HUMAN; LANGUAGE; MALE; MULTILEVEL ANALYSIS; PROCEDURES; PSYCHOMETRY;

ADOLESCENT; FEMALE; HUMANS; LANGUAGE; MALE; MULTILEVEL ANALYSIS; PSYCHOMETRICS;

EID: 84884980302     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11336-013-9333-5     Document Type: Article

References (58)

1. Adams, R. J., Wilson, M., & Wu, M. (1997). Multilevel item response models: an approach to errors in variables regression. Journal of Educational and Behavioral Statistics, 22, 47-76.
2. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.
3. Anglin, J. M. (1993). Vocabulary development: a morphological analysis. Monographs of the Society for Research in Child Development, 58, 1-166. Serial #238.
4. Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project. Behavior Research Methods, 39, 445-459.
5. Bates, D., Maechler, M., & Bolker, B. (2011). lme4: linear mixed-effects models using S4 classes. R package version 0. 999375-39. http://CRAN. Rproject. org/package=lme4.
6. Baayen, R. H., Piepenbrock, R., & van Rijn, H. (1993). The CELEX lexical data base on CD-ROM. Philadelphia: Linguistic Data Consortium.
7. Bejar, I. I. (1993). A generative approach to psychological and educational measurement. In N. Frederiksen, R. J. Mislevy, & I. I. Bejar (Eds.), Test theory for a new generation of tests (pp. 323-359). Hillsdale: Erlbaum.
8. Bowey, J. A., & Muller, D. (2005). Phonological recoding and rapid orthographic learning in third graders' silent reading: a critical test of the self-teaching hypothesis. Journal of Experimental Child Psychology, 92, 203-219.
9. Carlisle, J. F., & Stone, C. A. (2005). Exploring the role of morphemes in word reading. Reading Research Quarterly, 40, 428-449.
10. Carlisle, J. F., & Katz, L. A. (2006). Effects of word and morpheme familiarity on reading of derived words. Reading & Writing, 19, 669-693.
11. Cho, S.-J., & Rabe-Hesketh, S. (2011). Alternating imputation posterior estimation of models with crossed random effects. Computational Statistics & Data Analysis, 55, 12-25.
12. Cho, S.-J., Partchev, I., & De Boeck, P. (2012). Parameter estimation of multiple item profiles models. British Journal of Mathematical & Statistical Psychology, 65, 438-466.
13. Cho, S.-J., & Rabe-Hesketh, S. (2012). Random item discrimination marginal maximum likelihood estimation of item response models. Paper presented at the National Council on Measurement in Education, Vancouver, British Columbia.
14. Cho, S.-J., De Boeck, P., Embretson, S., & Rabe-Hesketh, S. (2013). Additive item structure models with random residuals: item modeling for explanation and item generation. Unpublished manuscript.
15. Cunningham, A. E., Perry, K. E., Stanovich, K. E., & Share, D. L. (2002). Orthographic learning during reading: examining the role of self-teaching. Journal of Experimental Child Psychology, 82, 185-199.
16. De Boeck, P. (2008). Random item IRT models. Psychometrika, 73, 533-559.
17. De Boeck, P., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuerlinckx, F., & Partchev, I. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software, 39, 1-28.
18. De Boeck, P., & Wilson, M. (2004). Explanatory item response models: a generalized linear and nonlinear approach. New York: Springer.
19. Deacon, S. H., & Bryant, P. (2005). What children do and do not know about the spelling of inflections and derivations. Developmental Science, 8, 583-594.
20. Deacon, S. H., & Bryant, P. (2006). Getting to the root: young writers' sensitivity to the role of root morphemes in the spelling of inflected and derived words. Journal of Child Language, 33, 401-417.
21. Fischer, G. H. (1973). Linear logistic test model as an instrument in educational research. Acta Psychologica, 37, 359-374.
22. Geerlings, H., Glas, C. A. W., & van der Linden, W. J. (2011). Modeling rule-based item generation. Psychometrika, 76, 337-359.
23. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.
24. Glas, C. A. W., & van der Linden, W. J. (2003). Computerized adaptive testing with item cloning. Applied Psychological Measurement, 27, 247-261.
25. Goldstein, H., & Rasbash, J. (1996). Improved approximations for multilevel models with binary responses. Journal of the Royal Statistical Society. Series A, 159, 505-513.
26. González, F., De Boeck, P., & Tuerlinckx, F. (2008). A double-structure structural equation model for three-mode data. Psychological Methods, 13, 337-353.
27. Goodwin, A. P., Gilbert, J. K., & Cho, S.-J. (2013). Morphological contributions to adolescent word reading: an item response approach. Reading Research Quarterly, 48, 39-60.
28. Goodwin, A. P., Gilbert, J. K., Cho, S.-J., & Kearns, D. (2012). Probing lexical representations: simultaneous modeling of word and reader contributions to multidimensional lexical representations. Unpublished manuscript.
29. Harm, M. W., & Seidenberg, M. S. (2004). Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes. Psychological Review, 111, 662-720.
30. Hickendorff, M., Heiser, W. J., van Putten, C. M., & Verhelst, N. D. (2009). Solution strategies and achievement in Dutch complex arithmetic: latent variable modeling of change. Psychometrika, 74, 331-350.
31. Hofman, A., & De Boeck, P. (2010). The utility of models with multidimensional random person and random item (stimuli) variables: a recovery study with lmer. Unpublished manuscript, University of Amsterdam.
32. Janssen, R., & De Boeck, P. (1999). Confirmatory analyses of componential test structure using multidimensional item response theory. Multivariate Behavioral Research, 34, 245-268.
33. Janssen, R., Schepers, J., & Peres, D. (2004). Models with item and item group predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: a generalized linear and nonlinear approach (pp. 189-212). New York: Springer.
34. Jeon, M., & Rabe-Hesketh, S. (2012). Profile-likelihood approach for estimating generalized linear mixed models with factor structures. Journal of Educational and Behavioral Statistics, 37, 518-542.
35. Johnson, M. S., & Sinharay, S. (2005). Calibration of polytomous item families using Bayesian hierarchical modeling. Applied Psychological Measurement, 29, 369-400.
36. MacGinitie, W. H., MacGinitie, R. K., Maria, K., & Dreyer, L. G. (2000). Gates-MacGinitie reading tests. Itasca: Riverside Publishing.
37. Mahony, D. L. (1994). Using sensitivity to word structure to explain variance in high school and college level reading ability. Reading & Writing: An Interdisciplinary Journal, 6, 19-44.
38. McCutchen, D., Logan, B., & Biangardi-Orpe, U. (2009). Making meaning: children's sensitivity to morphological information during word reading. Reading Research Quarterly, 44, 360-376.
39. Mislevy, R. J. (1988). Exploiting auxiliary information about items in the estimation of Rasch item difficulty parameters. Applied Psychological Measurement, 12, 281-296.
40. Nagy, W. E., & Anderson, R. C. (1984). How many words are there in printed school English? Reading Research Quarterly, 19, 304-440.
41. Nation, K., Angell, P., & Castles, A. (2007). Orthographic learning via self-teaching in children learning to read English: effects of exposure, durability, and context. Journal of Experimental Child Psychology, 96, 71-84.
42. Perfetti, C. A. (2007). Reading ability: lexical quality to comprehension. Scientific Studies of Reading, 11, 357-383.
43. Perfetti, C. A., & Hart, L. (2001). The lexical basis of comprehension skill. In D. S. Gorfein (Ed.), On the consequences of meaning selection: perspectives on resolving lexical ambiguity, Washington: American Psychological Association.
44. Perfetti, C. A., & Hart, L. (2002). The lexical quality hypothesis. In L. Verhoeven, C. Elbro, & P. Reitsma (Eds.), Precursors of Functional Literacy. Amsterdam/Philadelphia: Benjamins.
45. Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2005). Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects. Journal of Econometrics, 128, 301-323.
46. Raudenbush, S. W., Yang, M., & Yosef, M. (2000). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. Journal of Computational and Graphical Statistics, 9, 141-157.
47. Rodríguez, G., & Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses. Journal of the Royal Statistical Society. Series A, 158, 73-89.
48. Rodríguez, G., & Elo, I. (2003). Intra-class correlation in random-effects models for binary data. Stata Journal, 3, 32-46.
49. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
50. Singson, M., Mahony, D., & Mann, V. (2000). The relation between reading ability and morphological skills: evidence from derivational suffixes. Reading & Writing: An Interdisciplinary Journal, 12, 219-252.
51. Sinharay, S., Johnson, M. S., & Williamson, D. M. (2003). Calibrating item families and summarizing the results using family expected response functions. Journal of Educational and Behavioral Statistics, 28, 295-313.
52. Tennessee Department of Education Report Card (2011). Tennessee Department of Education Report Card. Retrieved from http://edu. reportcard. state. tn. us/pls/apex/f?p=200: 1: 4463180647001600on8-28-11.
53. Tyler, A., & Nagy, W. (1989). The acquisition of English derivational morphology. Journal of Memory and Language, 28, 649-667.
54. Tyler, A., & Nagy, W. E. (1990). Use of derivational morphology during reading. Cognition, 36, 17-34.
55. Vaida, F., & Blanchard, S. (2005). Conditional Akaike information for mixed effects models. Biometrika, 92, 351-370.
56. Vellutino, F. R., Tunmer, W. E., Jaccard, J. J., & Chen, R. (2007). Components of reading ability: multivariate evidence for a convergent skills model. Scientific Studies of Reading, 11, 3-32.
57. Verhelst, N. D., & Verstralen, H. H. F. M. (2002). Structural analysis of a univariate latent variable (SAUL) (computer program and manual). Arnhem: CITO.
58. Zeno, S. M., Ivens, S. H., Millard, R. T., & Duvvuri, R. (1995). The educator's word frequency guide. Brewster: Touchstone Applied Science Associates.