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Overview of O*NET Data Collection and Activities OIDAP Meeting Phil M. Lewis David R. Rivkin National Center for O*NET Development Pam Frugoli Employment and Training Administration, DOL May 4, 2011. Goals of the Update Briefing. Overview of O*NET Project Data Collection Program
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Overview of O*NET Data Collection and ActivitiesOIDAP Meeting Phil M. LewisDavid R. RivkinNational Center for O*NET DevelopmentPam FrugoliEmployment and Training Administration, DOLMay 4, 2011
Goals of the Update Briefing • Overview of O*NET Project • Data Collection Program • Address OIDAP Questions • Products & Tools • O*NET Users • Special Projects
OIDAP Questions • Please describe how the O*NET data collection strategy builds upon the BLS Occupational Employment Statistics (OES) database. • Are you still using Dun & Bradstreet data to find establishments? Were OES data not able to accomplish this, or is it an efficiency issue? • What was the original balance between the Establishment Method and the Occupational Expert Method for populating the O*NET database? Has that changed now that you are in maintenance mode? How are the occupational experts identified? • Can you describe the role of the state workforce development offices in data collection for O*NET? Has this changed over time? • Why did O*NET decide not to use field analysts to collect the data on occupations?
OIDAP Questions (cont.) • What are the major challenges associated with identifying employees in specific occupations to observe in the field? • How do Detailed Work Activities differ from Generalized Work Activities in the O*NET? How were they empirically derived? • We understand that a rule of thumb for statistical sufficiency was set at 15 observations for each domain. Are you still satisfied with that number? • Are you planning any changes in procedure or content as a result of the recent review of O*NET by the National Research Council? • How might your sampling parameters change if the results were subject to legal challenge? • What advice do you have for SSA as they begin to develop an OIS for disability adjudication purposes?
Why O*NET? 1980 Work, Jobs, and Occupations: a Critical Review of the Dictionary of Occupational Titles (National Research Council) 1993 Final report of the Advisory Panel on the DOT (APDOT) 1999 The Changing Nature of Work: Implications for Occupational Analysis 2000 O*NET Data Collection Program Survey Pretest 2001 Official OMB approved O*NET data collection using the O*NET survey questionnaires initiated
O*NET Project Team • U.S. Department of Labor, Employment & Training Administration • National Center for O*NET Development • North Carolina Employment Security • RTI; MCNC; HumRRO; NC State University; Maher & Maher
National Center for O*NET Development • Data Collection • Dissemination • Implementation • Research and Development • Technical Assistance/Customer Support
What is O*NET? • A “common language” and dynamic system for describing the world of work for both the public and private sectors • A comprehensive system for collecting and disseminating information on occupational and worker requirements
What is O*NET? • Flexible competency-based system with emphasis on skills transferability • Framework for organizing job and worker information • Data on occupations covering the entire U.S. Economy
What is O*NET? • Uses information technologies to facilitate the collection, storage, and distribution of quality data • A resource for businesses, educators, job seekers, HR professionals, and publicly funded government programs
O*NET Structure • The O*NET-SOC Occupational Taxonomy • The O*NET Content Model
SOC and O*NET-SOC • SOC mandated by US Office of Management and Budget • Developed by multi-agency initiative
Key characteristics of U.S. SOC • Structured for comparability • Unified classification structure • Four hierarchical levels to enable data collectors to choose a level of detail corresponding to their needs and ability to collect data on different occupations
SOC and O*NET-SOC • O*NET-SOC is a SOC based classification that provides a greater level of detail as needed • O*NET-SOC 2010 taxonomy released December 2010 • Currently data collected on 974 O*NET-SOCs • Adds 269 more specific occupations • New and emerging • Different tasks and KSAs
Content Model: Sub-Domains Worker Characteristics Abilities Interests Work Styles Abilities Cognitive Psychomotor Physical Sensory Cognitive Verbal Idea Generation & Reasoning Quantitative Memory Perceptual Spatial Attentiveness Verbal Oral Comprehension Written Comprehension Oral Expression Written Expression http://www.onetcenter.org/content.html
O*NET 15.1 Database The O*NET Database: Version 15.1 • Occupation data • Cross-Occupational + Occupation Specific • 230+ variables • Importance, level, frequency • @ 500 ratings per occupation • @ 3500 metadata per occupation Abilities Scales Reference Content Model Reference Skills Educ, Trng & Exp Categories Survey Booklet Locations Task Categories Educ, Trng & Exp Task Ratings Interests Task Statements Job Zone Reference Supplemental Files Work Activities Job Zones • Related Occupations • Crosswalks • Detailed Work Activities • Emerging Tasks • Lay Titles • Tools and Technology • O*NET-SOC 2000 to O*NET-SOC 2006 • Tasks (Release 5.1 File Layout) • Work Needs • Crosswalks • Detailed Work Activities • Emerging Tasks • In-Demand Occupations • Lay Titles • Related Occupations • Tools and Technology • Work Needs Work Context Categories Knowledge Work Context Level Scale Anchors Work Styles Occupation Data Occupation Level Metadata Work Values
Occupational Level Statistics O*NET-SOC Establishment Response Rate O*NET-SOC Employee Response Rate O*NET-SOC Case Completeness Rate Total Completes for O*NET-SOC Occupational Level Distribution Statistics Data Collection Mode Current Job Tenure Industry The O*NET Database:Metadata • Ratings Level Statistics • Confidence Intervals • Lower and upper 95% bounds • Standard Deviation • Standard Error • Sample Size • Flags • Not Relevant for the Occupation • Recommended Suppression
O*NET Data Availability • 874 occupations • Comprehensive data • 359 second update • 100 occupations • Description, task list, lay titles • Majority also have interests, work values, and tools & technology
O*NET Data Publication Goals • New database released annually • Minimum of 100 occupations updated • Average currency of all occupations = 2.59 years • Priority established by DOL • Maximum 5 years-old • Bright Outlook • Green occupations • Linked to technology, math, and science, computers, engineering, and innovation
O*NET Data Collection Overview • Proven successful and cost effective methodology designed to collect and yield high quality occupational data • Multi-method approach to provide flexibility within a framework of standardized procedures • Establishment, Occupational Expert, Supplemental Frames, Analyst Ratings, Web-Based • Minimizes public burden and costs • Approved by Office of Management & Budget (OMB)
O*NET Data Collection Overview • Continuous data collection since June 2001 • Three successful OMB Clearances • Comprehensive update by job incumbents and occupational experts of the 2006 O*NET-SOC Taxonomy • Transition to 2010 O*NET-SOC • New & Emerging occupations • Unparalleled partnership between Department of Labor and private/public community • 40,000+ business/organizations • 160,000+ job incumbents/experts • 450+ National Associations
Data Collection Overview (cont.) • High quality data from a national sample of job incumbents/occupational experts • Strong business participation • 76% plus response rate • Strong employee participation • 65% plus response rate • Strong occupation expert participation • 82% response
Data Collection Overview (cont.) • Web-based case management system used to control all sampling and data collection systems • Case management, BL contacts with establishments, questionnaire and informational mailings, questionnaire processing, inventory control, etc. • Finely tuned procedures, systems and infrastructure capable of surveying multiple occupations simultaneously • Capability developed, tested, and enhanced over 11 years
Sources of Occupational Data • Job Incumbents and Occupation Experts • Education, Job Titles, Knowledge, Tasks, Work Activities, Work Context, Work Experience, Work Styles • Occupation Analysts • Abilities, Skills • Web-based Research • Detailed Work Activities, Green, Tasks, Tools and Technologies (T2)
Establishment Method • Two stage sample • Business establishments - POC • Job incumbents within business establishments
Establishment Method (cont.) • Job incumbents complete one of three survey questionnaires (25 -30 minutes) • Generalized Work Activities, Knowledge/Work Styles, or Work Context • Task List • Background Info • Incumbents choose response option • Paper-and-pencil • Web-based (approximately 25%)
Design of Collection Waves • Identify ~50 primary occupations to target in a sample wave • Wave X.1: Designed to get 34% of sample • Wave X.2: Designed to get 33% of sample • Wave X.3: Designed to get 33% of sample • Wave X.4: Sample residual
Design of Collection Waves (cont.) • Each wave is a cluster of similar occupations • Secondary occupations which are found across industries are also included to maximize efficiency • Multiple sub-waves allow for greater precision • Locating occupations • Controlling public burden and project resources
Stage One Sampling • OES data from BLS used to determine the initial industry distribution for each occupation • Sample business establishments selected from database of business locations
Stage One Sampling (cont.) • OES data from BLS used to determine the initial industry distribution for each occupation • Indicates which industries occupations are employed in and the share and distribution of occupational employment across industries • Does not contain information on establishments
Stage One Sampling (cont.) • Industry information for each occupation is refined by O*NET Center analysts • Review and face validity checks • For example, religious institution sub-section removed from service industry if sampling for bartenders • Determine industries to include based on overall distribution and population coverage goals • Refined/target by experience from previous updates, when available
Stage One Sampling (cont.) • Sample business establishments selected from a frame of business locations • Dunn & Bradstreet (D&B) database • ~15 Million establishments • Info obtained from multiple sources • Tax records, credit reports, telephone directories • Updated continuously on a monthly basis • Links to SIC and NAICS industry information
Population Coverage • Gather data on the “core” of the occupation • Where the majority of incumbents employed • Average coverage level is 85%
Stage Two Sampling • Led by highly qualified O*NET Business Liaisons (O*NET BL) • Full time staff working in dedicated call center • Educational and work experience criteria higher than typical telephone interviewer • The sampled establishment’s Point-of-Contact (POC) works with the O*NET BL to identify the a list of eligible employees • Identification Profiles (ID Profile) are used when asking POC if occupations are present • Helps ensure accuracy in matching employees to occupations
Stage Two Sampling (cont.) • Automated, random selection of job incumbents from the POC’s eligible list • Limits placed on POC burden • No more than 5 occupations sampled • Never more than 20 employees selected • Can only be included within the data collection once each year • Selected employees • Asked to complete the questionnaire on their own time • Responses remain anonymous and confidential from both the employer and the public • Complete via the web or mail back directly • All individual identifiers removed
Model-Aided Sampling (MAS) • Innovative sampling approach that reduces data collection cost and burden to the public by preventing occupations from greatly exceeding their targeted sample • Builds on existing sampling paradigms: traditional and model based • For each occupation, a targeted sample size for specific demographic domains is modeled • Census region • Establishment size • Industry division • Data collection is halted in a MAS cell when the targeted respondent sample size is projected to be achieved
Other Key Features • Incentives • Employer • Toolkit for Business • POC • Clock, Certificate of Appreciation • Employee • $10 • Outreach to professional/trade associations increase awareness • Endorsement list provided to POC
Supplemental Frame Method • Multiple strategies to augment the establishment data collection • Supplemental Frame Incumbent • Job incumbents are directly accessed via an association listing to complete the data collection • e.g. Industrial Organizational Psychologists • Supplemental Frame Establishment • Targeted employer sample developed via expert contact/associations where the sampling frame coverage is significantly high but is not adequate by itself • e.g. Freight and Cargo Inspectors • Special Frame Establishment • Targeted employer sample completely developed via expert contact/associations where coverage is extremely high • Normal establishment method is bypassed • e.g. Nuclear Power Reactor Operators; Flight Attendants
Occupational Experts (OE) Method • Used when occupation is difficult to locate in establishments • Small employment size • Job incumbents inaccessible due to work in remote locations • New and emerging occupations
OE Method (cont.) 47 • Data collected from experts in target occupation • Supervisors, trainers, others with extensive knowledge of occupation • Identify appropriate source organizations (e.g. professional associations) • Good coverage of occupation • Can identify members who are occupation experts • Willing to provide lists of experts
OE Method (cont.) 48 Select sample from membership lists Contact, screen, and survey OEs directly – no establishment or POC OEs complete all three domain questionnaires, background and task questionnaires OE incentives – clock, Certificate of Appreciation, $40
Analyst Ratings Method • Occupational Analysts Rate the Ability and Skill Domains • Updated occupation information collected from job incumbents used to describe occupation and assist with the rating process • Extensive training and quality assurance procedures