THE EFFECT OF CRITERION SCORE GROUPING UPON ITEM PARAMETER ESTIMATION

1967 ◽  
Vol 20 (2) ◽  
pp. 227-238 ◽  
Author(s):  
Frank B. Baker
Keyword(s):  
Parameter Estimation ◽  
Item Parameter ◽  
Criterion Score ◽  
Item Parameter Estimation

The effect of uncertainty of item parameter estimation on ability estimates

Psychometrika ◽  
1990 ◽  
Vol 55 (2) ◽  
pp. 371-390 ◽  
Author(s):  
Robert K. Tsutakawa ◽  
Jane C. Johnson
Keyword(s):  
Parameter Estimation ◽  
Item Parameter ◽  
Ability Estimates ◽  
Item Parameter Estimation

scholarly journals Conditional Maximum Likelihood Estimation in Probability-Branched Multistage Designs

2021 ◽  
Author(s):  
Jan Steinfeld ◽  
Alexander Robitzsch
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Parameter Estimate ◽  
Item Parameter ◽  
Parameter Estimates ◽  
Conditional Maximum Likelihood ◽  
Item Parameter Estimates ◽  
Item Parameter Estimation ◽  
Conditional Maximum

This article describes the conditional maximum likelihood-based item parameter estimation in probabilistic multistage designs. In probabilistic multistage designs, the routing is not solely based on a raw score j and a cut score c as well as a rule for routing into a module such as j < c or j ≤ c but is based on a probability p(j) for each raw score j. It can be shown that the use of a conventional conditional maximum likelihood parameter estimate in multistage designs leads to severely biased item parameter estimates. Zwitser and Maris (2013) were able to show that with deterministic routing, the integration of the design into the item parameter estimation leads to unbiased estimates. This article extends this approach to probabilistic routing and, at the same time, represents a generalization. In a simulation study, it is shown that the item parameter estimation in probabilistic designs leads to unbiased item parameter estimates.

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