Item Response Theory

Item Response Theory

Volumn , Issue , 2012, Pages 1-144

  DeMars, Christine ^a  

^a NONE

Author keywords

Abilities; Attitudes; Graduate Record examination; Item Response Theory; Mathematical Models; Questionnaires; Survey Instruments

Indexed keywords

EID: 84886503695     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1093/acprof:oso/9780195377033.001.0001     Document Type: Book

References (115)

1. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1999). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.
2. Andrich, D. (1978). Application of a psychometric rating model to ordered categories which are scored with successive integers. Applied Psychological Measurement, 2, 581-594.
3. Andrich, D. (2002). Understanding resistance to the data-model relationship in Rasch's paradigm: A reflection for the next generation. Journal of Applied Measurement, 3, 325-359.
4. Baker, F. B., & Kim, S.-H.. (2004). Item response theory parameter estimation techniques (2nd ed.). New York: Marcel Dekker.
5. Barnes, L. L. B., & Wise, S. L. (1991). The utility of a modified one-parameter IRT model with small samples. Applied Measurement in Education, 4, 143-157.
6. Beretvas, S. N. (2004). Comparison of Bookmark difficulty locations under different item response models. Applied Psychological Measurement, 28, 25-47.
7. Bock, R. D. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika, 37, 29-51.
8. Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46, 443-459.
9. Bollen, K. A.. (1989). Structural equations with latent variables. New York: John Wiley and Sons.
10. Bolt, D. M. (2002). A Monte-Carlo comparison of parametric and nonparametric polytomous DIF detection methods. Applied Measurement in Education, 15, 113-141.
11. Bond, T. G., & Fox, C. M.. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates.
12. Buckendahl, C. W., Impara, J. C., & Plake, B. S. (2002). District accountability without a state assessment: A proposed model. Educational measurement: Issues and Practice, 21(4), 6-16.
13. Chen, W-H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22, 265-289.
14. Choi, S. W., Cook, K. F., & Dodd, B. G. (1997). Parameter recovery for the partial credit model using MULTILOG. Journal of Outcome Measurement, 1, 114-142.
15. Cook, L. L., Eignor, D. R., & Taft, H. L. (1988). A comparative study of the effects of recency of instruction on the stability of IRT and conventional item parameter estimates. Journal of Educational Measurement, 25, 31-45.
16. De Ayala, R. J. (1993). An introduction to polytomous item response theory models. Measurement and Evaluation in Counseling and Development, 25, 172-189.
17. De Ayala, R. J., & Hertzog, M. A. (1991). The assessment of dimensionality for use in item response theory. Multivariate Behavioral Research, 26, 765-792.
18. De Ayala, R. J., & Sava-Bolesta, M. (1999). Item parameter recovery for the nominal response model. Applied Psychological Measurement, 23, 3-19.
19. De Champlain, A. F. (1999). Assessing the dimensionality of simulated LSAT item response matrices with small sample sizes and short test lengths. (Report No. LSAC-96-01). Princeton, NJ: Law School Admissions Council (ERIC Document Reproduction Service No ED467805)
20. De Champlain, A., & Gessaroli, M. E. (1998). Assessing the dimensionality of item response matrices with small sample sizes and short test lengths. Applied Measurement in Education, 11, 231-253.
21. DeMars, C. E. (2003a). Sample size and the recovery of nominal response model item parameters. Applied Psychological Measurement, 27, 275-288.
22. DeMars, C. E. (2003b). Detecting multidimensionality due to curricular differences. Journal of Educational Measurement, 40, 29-51.
23. DeMars, C. E. (2005). Type I error rates for PARSCALE's fit index. Educational and Psychological Measurement, 65, 42-50.
24. Donoghue, J. R., & Humbo, C. M. (2003, April). A corrected asymptotic distribution of an IRT fit measure that accounts for the effects of item parameter estimation. Paper presented at the annual meeting of the American Educational Research Association, Chicago.
25. Douglas, J., & Cohen, A. (2001). Nonparametric item response function estimation for assessing parametric model fit. Applied Psychological Measurement, 25, 234-243.
26. Drasgow, F. (1989). An evaluation of marginal maximum likelihood estimation for the twoparameter logistic model. Applied Psychological Measurement, 13, 77-90.
27. Drasgow, F., & Lissak, R. I. (1983). Modified parallel analysis: A procedure for examining the latent dimensionality of dichotomously scored item responses. Journal of Applied Psychology, 68, 363-373.
28. Embretson, S. E., & Reise, S. P.. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.
29. Engelhard, G., Jr. (1992). The measurement of writing ability with a manyfaceted Rasch model. Applied Measurement in Education, 5, 171-191.
30. Fan, X. (1998). Item response theory and classical test theory: An empirical comparison of their item/person statistics. Educational and Psychological Measurement, 58, 357-381.
31. Feldt, L. S., & Brennan, R. L. (1989). Reliability. In R. L. Linn (Ed.), Educational Measurement (3rd ed., pp. 105-146). Phoenix, AZ: American Council on Education/Oryx Press.
32. Finch, H., & Habing, B. (2007). Performance of DIMTEST- and NOHARM-based statistics for testing unidimensionality. Applied Psychological Measurement, 31, 292-307.
33. Finney, S. J., and DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock and R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 269-314). Greenwich, CT: Information Age Publishing.
34. Fraser, C., & McDonald, R. P. (2003). NOHARM 3.0. Available at: Gao, F., & Chen, L. (2005). Bayesian or non-Bayesian: A comparison study of item parameter estimation in the three-parameter logistic model. Applied Measurement in Education, 18, 351-380.
35. Gessaroli, M. E., & De Champlain, A. F. (1996). Using an approximate chi-square statistic to test the number of dimensions underlying the responses to a set of items. Journal of Educational Measurement, 33, 157-179.
36. Glas, C. A. W., & Suárez Falcón, J. C. (2003). A comparison of item-fit statistics for the three-parameter logistic model. Applied Psychological Measurement, 27, 87-106.
37. Green, B. F., Bock, R. D., Humphreys, L. G., Linn, R. B., & Reckase, M. D. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21, 347-360.
38. Hagtvet, K. A., & Benson, J. (1997). The motive to avoid failure and test anxiety responses: Empirical support for integration of two research traditions. Anxiety, Stress, and Coping, 10, 35-57.
39. Hambleton, R. K., & Rovinelli, R. J. (1986). Assessing the dimensionality of a set of test items. Applied Psychological Measurement, 10, 287-302.
40. Harwell, M. R., & Janosky, J. E. (1991). An empirical study of the effects of small datasets and varying prior variances on item parameter estimation in BILOG. Applied Psychological Measurement, 15, 279-291.
41. Hattie, J. (1984). An empirical study of various indices for determining unidimensionality. Multivariate Behavioral Research, 19, 49-78.
42. Hattie, J., Krakowski, K., Rogers, H. J., & Swaminathan, H. (1996). An assessment of Stout's index of essential unidimensionality. Applied Psychological Measurement, 20, 1-14.
43. Hulin, C. L., Lissak, R. I., & Drasgow, F. (1982). Recovery of two- and three-parameter logistic item characteristic curves: A Monte Carlo study. Applied Psychological Measurement, 20, 249-260.
44. Kang, T., & Chen, T. T. (2008). Performance of the generalized S-X2 item fit index for polytomous IRT models. Journal of Educational Measurement, 45, 391-406.
45. Kang, T., Cohen, A. S., & Sung, H. J. (2005, April). IRT model selection methods for polytomous items. Paper presented at the annual meeting of the National Council on Measurement in Education, Montreal.
46. Kang, T., & Cohen, A. S. (2007). IRT model selection methods for dichotomous items. Applied Psychological Measurement, 31, 331-358.
47. Karabatsos, G. (2003). Comparing the aberrant response detection performance of thirtysix person fit statistics. Applied Measurement in Education, 16, 277-298.
48. Karantonis, A., & Sireci, S. G. (2006). The Bookmark standard-setting method: A literature review. Educational Measurement: Issues and Practice, 25 (1) 4-12.
49. Li, S., & Wells, C. S. (2006, April). A model fit statistic for Samejima's Graded Response model. Paper presented at the annual meeting of the National Council on Measurement in Education, San Francisco.
50. Liang, T., & Wells, C. S. (2007, April). A model fit statistic for generalized partial credit model. Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago.
51. Linacre, J. M.. (2006). A user's guide to WINSTEPS and MINISTEPS Rasch-Model computer programs. Chicago: Winsteps.com.
52. Lord, F. M. (1968). An analysis of the verbal scholastic aptitude test using Birnbaum's three-parameter logistic model. Educational and Psychological Measurement, 28, 989-1020.
53. Lord, F. M. (1974). Estimation of latent ability and item parameters when there are omitted responses. Psychometrika, 39, 247-264.
54. Lord, F. M.. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Lawrence Erlbaum Associates.
55. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
56. Maydeu-Olivares, A. (2001). Multidimensional item response theory modeling of binary data: Large sample properties of NOHARM estimates. Journal of Educational and Behavioral Statistics, 26, 51-71.
57. Maydeu-Olivares, A., Drasgow, F., & Mead, A. D. (1994). Distinguishing among parametric item response models for polychotomous ordered data. Applied Psychological Measurement, 18, 245-256.
58. Mislevy, R. J. (1984). Estimating latent distributions. Psychometrika, 49, 359-381.
59. Mislevy, R. J. (1986). Bayes modal estimation in item response models. Psychometrika, 51, 177-195.
60. Mislevy, R. J., Beaton, A. E., Kaplan, B., & Sheehan, K. M. (1992). Estimating population characteristics from sparse matrix samples of item responses. Journal of Educational Measurement, 29, 133-161.
61. Muraki, E. (1990). Fitting a polytomous item response model to Likert-type data. Applied Psychological Measurement, 14, 59-71.
62. Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159-176.
63. Muraki, E., & Bock, D. (2003). PARSCALE 4.0 [Computer software and manual]. Lincolnwood, IL: Scientific Software International.
64. Nandakumar, R., & Stout, W. (1993). Refinements of Stout's procedure for assessing latent trait unidimensionality. Journal of Educational Statistics, 18, 41-68.
65. Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous item response theory models. Applied Psychological Measurement, 24, 50-64.
66. Orlando, M., & Thissen, D. (2003). Further investigation of the performance of S-X2: An item fit index for use with dichotomous item response theory models. Applied Psychological Measurement, 27, 289-298.
67. Oshima, T. C., & Morris, S. B. (2008). An NCME instructional module on Raju's differential functioning of items and tests (DFIT). Educational Measurement: Issues and Practice, 27 (3), 43-50.
68. Parshall, C. G., Kromrey, J. D., Chason, W. M., & Yi, Q. (1997, June). Evaluation of parameter estimation under modified IRT models and small samples. Paper presented at the annual meeting of the Psychometric Society, Gatlinburg, TN. (ERIC Document Reproduction Service No ED421535)
69. Patz, R. J., & Junker, B. W. (1999). Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses. Journal of Educational and Behavioral Statistics, 24, 342-366.
70. Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests (Exp. ed.). Chicago: University of Chicago Press. (Original work published 1960, Copenhagen: Danish Institute for Educational Research)
71. Reckase, M. D. (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of Educational Statistics, 4, 207-230.
72. Reckase, M. D., Ackerman, T. A., & Carlson, J. E. (1988). Building a unidimensional test using multidimensional items. Journal of Educational Measurement, 25, 193-203.
73. Reise, S. P. (1990). A comparison of item- and person-fit methods of assessing model-data fit in IRT. Applied Psychological Measurement, 14, 127-137.
74. Reise, S. P., & Yu, J. (1990). Parameter recovery in the graded response model using MULTILOG. Journal of Educational Measurement, 27, 133-144.
75. Roid, G. H. (2006). Designing ability tests. In S. M. Downing & T. M. Haladyna, Handbook of Test Development (pp. 527-542). Mahwah, NJ: LEA.
76. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplements, 17.
77. Sass, D. A., Schmitt, T. A., & Walker, C. M. (2008). Estimating non-normal latent trait distributions within item response theory using true and estimated item parameters. Applied Measurement in Education, 21, 65-88.
78. Schumacker, R. E. (2004). Rasch measurement: The dichotomous model. In E. V. Smith, Jr., & R. M. Smith (Eds.), Introduction to Rasch measurement: Theory, models, and applications (pp. 226-257). Maple Grove, MN: JAM Press.
79. Sijtsma, K., & Meijer, R. R. (2007). Nonparametric item response theory and special topics. In C. R. Rao & S. Sinharary (Eds.), Handbook of Statistics, Vol. 26: Psychometrics (pp. 719-746). Amsterdam: Elsevier.
80. Sinharay, S., & Lu, Y. (2008). A further look at the correlation between item parameters and item fit statistics. Journal of Educational Measurement, 45, 1-15.
81. Sireci, S. G., Thissen, D., & Wainer, H. (1991). On the reliability of testlet-based tests. Journal of Educational Measurement, 28, 237-247.
82. Smith, R. M., Schumacker, R. E., and Bush, M. J. (1998). Using item mean squares to evaluate fit to the Rasch model. Journal of Outcome Measurement, 2, 66-78.
83. Stone, C. A. (1992). Recovery of marginal maximum likelihood estimates in the twoparameter logistic response model: An evaluation of MULTILOG. Applied Psychological Measurement, 16, 1-16.
84. Stone, C. A. (2000). Monte Carlo based null distribution for an alternative goodness-of-fit test statistic in IRT models. Journal of Educational Measurement, 37, 58-75.
85. Stone, C. A. (2003). Empirical power and Type I error rates for an IRT fit statistic that considers the precision of ability estimates. Educational and Psychological Measurement, 63, 566-583.
86. Stone, C. A., & Hansen, M. A. (2000). The effect of errors in estimating ability on goodness-of-fit tests for IRT models. Educational and Psychological Measurement, 60, 974-991.
87. Stout, W. (1987). A nonparametric approach for assessing latent trait unidimensionality. Psychometrika, 52, 589-617.
88. Stout, W. (1999). Poly-DIMTEST [Computer software]. Champaign, IL: The William Stout Institute for Measurement.
89. Stout, W. (2005). DIMTEST (Version 2.0) [Computer software]. Champaign, IL: The William Stout Institute for Measurement.
90. Swaminathan, H., Hambleton, R. K., & Rogers, H. J. (2007). Assessing the fit of item response theory models. In C. R. Rao & S. Sinharay (Eds.), Handbook of Statistics, Vol. 26: Psychometrics (pp. 683-718). Amsterdam: Elsevier.
91. Tate, R. (2003). A comparison of selected empirical methods for assessing the structure of responses to test items. Applied Psychological Measurement, 27, 159-203.
92. Thissen, D., & Orlando, M. (2001). Item response theory for items scored in two categories. In D. Thissen & H. Wainer (Eds.), Test Scoring (pp. 73-140). Mahwah, NJ: Lawrence Erlbaum.
93. Thissen, D., Steinberg, L, & Gerrard, M. (1986). Beyond group differences: The concept of item bias. Psychological Bulletin, 99, 118-128.
94. Traub, R. E.. (1994). Reliability for the Social Sciences. Newbury Park, CA: Sage.
95. Wainer, H., Bradlow, E. T., & Wang, X.. (2007). Testlet response theory and its applications. New York: Cambridge University Press.
96. Wainer, H., & Wang, X. (2000). Using a new statistical model for testlets to score TOEFL. Journal of Educational Measurement, 37, 203-220.
97. Weiss, D. J. (1983). Polychotomous or polytomous? Applied Psychological Measurement, 7, 4.
98. Wells, C. S., & Bolt, D. M. (2008). Investigation of a nonparametric procedure for assessing goodness-of-fit in item response theory. Applied Measurement in Education, 21, 22-40.
99. Wilson, M. (2004). On choosing a model for measuring. In E. V. Smith, Jr., & R. M. Smith (Eds.), Introduction to Rasch Measurement: Theory, Models, and Applications (pp. 123-142). Maple Grove, MN: JAM Press.
100. Wilson, M.. (2005). Constructing measures: An item response modeling approach. Mahwah, NJ: Lawrence Erlbaum Associates.
101. Wingersky, M. S. (1983). LOGIST: A program for computing maximum likelihood procedures for logistic test models. In R. K. Hambleton (Ed.), Applications of Item Response Theory (pp. 45-56). British Columbia: Educational Research Institute of British Columbia.
102. Wolf, L. F., & Smith, J. K. (1995). The consequence of consequence: Motivation, anxiety, and test performance. Applied Measurement in Education, 8, 227-242.
103. Woods, C. M. (2006). Ramsay-curve item response theory to detect and correct for nonnormal latent variables. Psychological Methods, 11, 253-270.
104. Woods, C. M. (2008a). Consequences of ignoring guessing when estimating the latent density in item response theory. Applied Psychological Measurement, 32, 371-384.
105. Woods, C. M. (2008b). Ramsay-curve item response theory for the three-parameterlogistic item response model. Applied Psychological Measurement, 32, 447-465.
106. Wright, B. D., & Linacre, J. M. (1994). Reasonable mean-square fit values. Rasch Measurement Transactions, 8, 370.
107. Wright, B. D. (1997). A history of social science measurement. Educational Measurement: Issues and Practice, 16 (4) 33-45, 52.
108. Wright, B. D., & Masters, G. N.. (1982). Rating scale analysis. Chicago: MESA Press.
109. Wright, B. D., & Stone, M.. (1979). Best test design. Chicago: MESA Press.
110. Yen, W. M. (1981). Using simulation results to choose a latent trait model. Applied Psychological Measurement, 5, 245-262.
111. Yen, W. M. (1984). Effects of local item dependence on the fit and equating performance of the three-parameter logistic model. Applied Psychological Measurement, 8, 125-145.
112. Yen, W. M. (1993). Scaling performance assessments: Strategies for managing local item dependence. Journal of Educational Measurement, 30, 187-213.
113. Zimowski, M., Muraki, E., Mislevy, R., & Bock, D. (2003). BILOG-MG [Computer software and manual]. Lincolnwood, IL: Scientific Software International.
114. Zwick, R., Senturk, D., Wang, J., & Loomis, S. C. (2001). An investigation of alternative methods for item mapping in the National Assessment of Educational Progress. Educational Measurement: Issues and Practice, 20(2), 15-25.
115. Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432-442.