Download Free Employment And Training Administration Eta Book in PDF and EPUB Free Download. You can read online Employment And Training Administration Eta and write the review.

State Data Book

United States. Rehabilitation Services Administration. Division of Monitoring and Program Analysis. Statistical Analysis and Systems Branch

Published: 1972

Total Pages: 56


Get eBook

To help guide the nation's workforce development system, the Department of Labor's (Labor) Employment and Training Administration (ETA) conducts research in areas related to job training and employment. Building upon earlier work, this report examined the following: (1) To what extent do ETA's research priorities reflect key national employment and training issues and how useful were the studies funded under them?; (2) What steps has ETA taken to improve its research program?; (3) How has ETA improved the availability of its research since the last review in January 2010? Charts and tables. This is a print on demand edition of an important, hard-to-find publication.
Information about the characteristics of jobs and the individuals who fill them is valuable for career guidance, reemployment counseling, workforce development, human resource management, and other purposes. To meet these needs, the U.S. Department of Labor (DOL) in 1998 launched the Occupational Information Network (O*NET), which consists of a content model-a framework for organizing occupational data-and an electronic database. The O*NET content model includes hundreds of descriptors of work and workers organized into domains, such as skills, knowledge, and work activities. Data are collected using a classification system that organizes job titles into 1,102 occupations. The National Center for O*NET Development (the O*NET Center) continually collects data related to these occupations. In 2008, DOL requested the National Academies to review O*NET and consider its future directions. In response, the present volume inventories and evaluates the uses of O*NET; explores the linkage of O*NET with the Standard Occupational Classification System and other data sets; and identifies ways to improve O*NET, particularly in the areas of cost-effectiveness, efficiency, and currency.