caa a22 4500 606242007 CHVBK 20210128101316.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s11336-014-9407-z doi (NATIONALLICENCE)springer-10.1007/s11336-014-9407-z Standard Error of Ability Estimates and the Classification Accuracy and Consistency of Binary Decisions [Elektronische Daten] [Ying Cheng, Cheng Liu, John Behrens] While estimation bias is a primary concern in psychological and educational measurement, the standard error is of equal importance in linking key aspects of the assessment structure, especially when the assessment goal concerns the classification of individuals into categories (e.g., master/non-mastery). In this paper, we show analytically how standard error of ability estimates affects expected classification accuracy and consistency when the decision is binary. When standard error decreases, the conditional classification accuracy and consistency increase. Given an examinee population and a cut score, smaller standard error over the entire latent trait continuum guarantees higher overall expected classification accuracy and consistency. We were also able to show the interrelationship between standard error, the expected classification consistency, and reliability. Utilizing the relationship between standard error and expected classification accuracy and consistency, we derive the upper bounds of the overall expected classification accuracy and consistency of a fixed-length computerized adaptive test. The lower bound of the expected classification accuracy and consistency is also derived given a number of stopping rules of variable-length computerized adaptive testing. Implications of these analytical results on operational tests are discussed. The Psychometric Society, 2014 standard error nationallicence classification accuracy nationallicence classification consistency nationallicence reliability nationallicence computerized adaptive testing nationallicence Cheng Ying Department of Psychology, University of Notre Dame, 118 Haggar Hall, 46556, Notre Dame, IN, USA aut Liu Cheng Center for Research Computing, University of Notre Dame, St Joseph County, IN, USA aut Behrens John Pearson, Mishawaka, IN, USA aut Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 645-664 0033-3123 80:3<645 2015 80 11336 https://doi.org/10.1007/s11336-014-9407-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11336-014-9407-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Cheng Ying Department of Psychology, University of Notre Dame, 118 Haggar Hall, 46556, Notre Dame, IN, USA aut NATIONALLICENCE 700 1- Liu Cheng Center for Research Computing, University of Notre Dame, St Joseph County, IN, USA aut NATIONALLICENCE 700 1- Behrens John Pearson, Mishawaka, IN, USA aut NATIONALLICENCE 773 0- Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 645-664 0033-3123 80:3<645 2015 80 11336