item_1PL class represents a 1PL item.
item_2PL class represents a 2PL item.
item_3PL class represents a 3PL item.
item_PC class represents a partial credit item.
item_GPC class represents a generalized partial credit item.
item_GR class represents a graded response item.
slopea slope parameter value
difficultya difficulty parameter value
guessinga guessing parameter value
thresholda vector of threshold parameter values
categorya vector of category boundary values
ncatthe number of response categories
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.
Lord, F. M. (1952). A theory of test scores (Psychometric Monograph No. 7). Richmond, VA: Psychometric Corporation.
Birnbaum, A. (1957). Efficient design and use of tests of mental ability for various decision-making problems (Series Report No. 58-16. Project No. 7755-23). Randolph Air Force Base, TX: USAF School of Aviation Medicine.
Birnbaum, A. (1958). On the estimation of mental ability (Series Report No. 15. Project No. 7755-23). Randolph Air Force Base, TX: USAF School of Aviation Medicine.
Birnbaum, A. (1958). Further considerations of efficiency in tests of a mental ability (Series Report No. 17. Project No. 7755-23). Randolph Air Force Base, TX: USAF School of Aviation Medicine.
Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In Lord, F. M., Novick, M. R. (eds.), Statistical Theories of Mental Test Scores, 397-479. Reading, MA: Addison-Wesley.
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149-174.
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561-573.
Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159-176.
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 17.
item_1 <- new("item_1PL", difficulty = 0.5)
item_2 <- new("item_2PL", slope = 1.0, difficulty = 0.5)
item_3 <- new("item_3PL", slope = 1.0, difficulty = 0.5, guessing = 0.2)
item_4 <- new("item_PC", threshold = c(-0.5, 0.5), ncat = 3)
item_5 <- new("item_GPC", slope = 1.0, threshold = c(-0.5, 0.0, 0.5), ncat = 4)
item_6 <- new("item_GR", slope = 1.0, category = c(-2.0, -1.0, 0, 1.0, 2.0), ncat = 6)