Search found 1 item
- (-) sm.metadata.documentno="td/tnc 71.408"
With the advent of simple-to-use advanced statistical software packages, it is becoming increasingly easy to specify and estimate complex statistical models for addressing substantive questions in adult literacy and other areas of education. Such problems as the role of literacy in voting behaviour or the job market experiences of individuals with GEDs [General Educational Developments], can be addressed with relative ease. However, a thoughtful application of statistical models to educational data leads to the recognition that certain assumptions must be met for the model estimates to be useful for theoretical explanation and/or policy analysis. The purpose of this paper is to offer recommendations to the National Center for Education Statistics on the development of the background questionnaire for the National Assessment of Adult Literacy. The recommendations presented in this paper are from the viewpoint of a researcher interested in applying sophisticated statistical models to address important issues in adult literacy. This paper will focus on five issues: (1) sampling, (2) selection bias, (3) measurement, (4) policy analysis, and (5) cohort effects.
With the advent of simple-to-use advanced statistical software packages, it is becoming increasingly easy to specify and ... Show Full Abstract
|
Authors: Kaplan, David; White, Sheida Corporate authors: United States. Department of Education. National Center for Education Statistics (NCES) Date: 2000 Geographic subjects: North America; United States Resource type: Working paper Series name: NCES working paper Subjects: Literacy; Statistics; Outcomes; |
VITAL Object
VOCEDplus is produced by the National Centre for Vocational Education Research (NCVER), which together with TAFE South Australia, is a UNESCO regional Centre of Excellence in technical and vocational education and training (TVET). VOCEDplus receives funding from the Australian Government Department of Education, Employment and Workplace Relations (DEEWR).