Premium
Outlier Detection in High‐Stakes Certification Testing
Author(s) -
Meijer Rob R.
Publication year - 2002
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2002.tb01175.x
Subject(s) - certification , outlier , computerized adaptive testing , item response theory , computer science , anomaly detection , statistical hypothesis testing , test theory , artificial intelligence , sample (material) , test (biology) , process (computing) , statistics , econometrics , data mining , psychometrics , mathematics , paleontology , chemistry , chromatography , political science , law , biology , operating system
Recent developrnents of person‐Jit analysis in computerized adaptive testing (CAT) are discussed. Methods from stutistical process control are presented that have been proposed to classify an item score pattern as fitting or misjitting the underlying item response theory model in CAT. Most person‐fit research in CAT is restricted to simulated data. In this study, empirical data from a certification test were used, Alternatives are discussed to generate norms so that bounds can be determined to classify an item score pattern as fitting or misfitting. Using bounds determined from a sample of a high‐stakes certification test, the empirical analysis showed that dizerent types of misfit can be distinguished. Further applications using statistical process control methods to detect misfitting item score patterns are discussed.