Premium
Defining and Measuring College and Career Readiness: A Validation Framework
Author(s) -
Camara Wayne
Publication year - 2013
Publication title -
educational measurement: issues and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.158
H-Index - 52
eISSN - 1745-3992
pISSN - 0731-1745
DOI - 10.1111/emip.12016
Subject(s) - argument (complex analysis) , construct (python library) , test (biology) , psychology , construct validity , accountability , selection (genetic algorithm) , medical education , mathematics education , higher education , applied psychology , psychometrics , computer science , political science , clinical psychology , medicine , paleontology , artificial intelligence , law , biology , programming language
This article reviews the intended uses of these college‐ and career‐readiness assessments with the goal of articulating an appropriate validity argument to support such uses. These assessments differ fundamentally from today's state assessments employed for state accountability. Current assessments are used to determine if students have mastered the knowledge and skills articulated in state standards; content standards, performance levels, and student impact often differ across states. College‐ and career‐readiness assessments will be used to determine if students are prepared to succeed in postsecondary education. Do students have a high probability of academic success in college or career‐training programs? As with admissions, placement, and selection tests, the primary interpretations that will be made from test scores concern future performance. Statistical evidence between test scores and performance in postsecondary education will become an important form of evidence. A validation argument should first define the construct (college and career readiness) and then define appropriate criterion measures. This article reviews alternative definitions and measures of college and career readiness and contrasts traditional standard‐setting methods with empirically based approaches to support a validation argument.