Document Type

Presented Paper

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publication Date



Non-randomly missing data has theoretically different implications for item parameter estimation depending on whether joint maximum likelihood or marginal maximum likelihood methods are used in the estimation. The objective of this paper is to illustrate what potentially can happen, under these estimation procedures, when there is an association between ability and the absence of response. In this example, data is missing because some students, particularly low-ability students, did not complete the test.