😶‍🌫️
Psych
  • Preface
  • [4/9/2025] A One-Stop Calculator and Guide for 95 Effect-Size Variants
  • [4/9/2025] the people make the place
  • [4/9/2025] Personality predicts things
  • [3/31/2025] Response surface analysis with multilevel data
  • [3/11/2025] A Complete Guide to Natural Language Processing
  • [3/4/2025] Personality - Self and Identity
  • [3/1/2025] Updating Vocational Interests Information
  • [2/25/2025] Abilities & Skills
  • [2/22/2025] APA table format
  • [2/19/2025] LLM that replace human participants can harmfully misportray and flatt
  • [2/18/2025] Research Methods Knowledge Base
  • [2/17/2025] Personality - Motives/Interests
  • [2/11/2025] Trait structure
  • [2/10/2025] Higher-order construct
  • [2/4/2025] RL for CAT
  • [2/4/2025] DoWhy | An end-to-end library for causal inference
  • [2/4/2025] DAGitty — draw and analyze causal diagrams
  • [2/2/2025] Personality States
  • [2/2/2025] Psychometric Properties of Automated Video Interview Competency Assessments
  • [2/2/2025] How to diagnose abhorrent science
  • [1/28/2025] LLM and personality/interest items
  • [1/28/2025] Personality - Dispositions
  • [1/28/2025] Causal inference in statistics
  • [1/27/2025] Personality differences between birth order categories and across sibship sizes
  • [1/27/2025] nomological network meta-analysis.
  • [1/25/2025] Classic Papers on Scale Development/Validation
  • [1/17/2025] Personality Reading
  • [1/15/2025] Artificial Intelligence: Redefining the Future of Psychology
  • [1/13/2025] R for Psychometics
  • [12/24/2024] Comparison of interest congruence indices
  • [12/24/2024] Most recent article on interest fit measures
  • [12/24/2024] Grammatical Redundancy in Scales: Using the “ConGRe” Process to Create Better Measures
  • [12/24/2024] Confirmatory Factor Analysis with Word Embeddings
  • [12/24/2024] Can ChatGPT Develop a Psychometrically Sound Situational Judgment Test?
  • [12/24/2024] Using NLP to replace human content coders
  • [11/21/2024] AI Incident Database
  • [11/20/2024] Large Language Model-Enhanced Reinforcement Learning
  • [11/05/2024] Self-directed search
  • [11/04/2024] Interview coding and scoring
  • [11/04/2024] What if there were no personality factors?
  • [11/04/2024] BanditCAT and AutoIRT
  • [10/29/2024] LLM for Literature/Survey
  • [10/27/2024] Holland's Theory of Vocational Choice and Adjustment
  • [10/27/2024] Item Response Warehouse
  • [10/26/2024] EstCRM - the Samejima's Continuous IRT Model
  • [10/23/2024] Idiographic Personality Gaussian Process for Psychological Assessment
  • [10/23/2024] The experience sampling method (ESM)
  • [10/21/2024] Ecological Momentary Assessment (EMA)
  • [10/20/2024] Meta-Analytic Structural Equation Modeling
  • [10/20/2024] Structure of vocational interests
  • [10/17/2024] LLMs for psychological assessment
  • [10/16/2024] Can Deep Neural Networks Inform Theory?
  • [10/16/2024] Cognition & Decision Modeling Laboratory
  • [10/14/2024] Time-Invariant Confounders in Cross-Lagged Panel Models
  • [10/13/2024] Polynomial regression
  • [10/13/2024] Bayesian Mixture Modeling
  • [10/10/2024] Response surface analysis (RSA)
  • [10/10/2024] Text-Based Personality Assessment with LLM
  • [10/09/2024] Circular unidimensional scaling: A new look at group differences in interest structure.
  • [10/07/2024] Video Interview
  • [10/07/2024] Relationship between Measurement and ML
  • [10/07/2024] Conscientiousness × Interest Compensation (CONIC) model
  • [10/03/2024] Response modeling methodology
  • [10/02/2024] Conceptual Versus Empirical Distinctions Among Constructs
  • [10/02/2024] Construct Proliferation
  • [09/23/2024] Psychological Measurement Paradigm through Interactive Fiction Games
  • [09/20/2024] A Computational Method to Reveal Psychological Constructs From Text Data
  • [09/18/2024] H is for Human and How (Not) To Evaluate Qualitative Research in HCI
  • [09/17/2024] Automated Speech Recognition Bias in Personnel Selection
  • [09/16/2024] Congruency Effect
  • [09/11/2024] privacy, security, and trust perceptions
  • [09/10/2024] Measurement, Scale, Survey, Questionnaire
  • [09/09/2024] Reporting Systematic Reviews
  • [09/09/2024] Evolutionary Neuroscience
  • [09/09/2024] On Personality Measures and Their Data
  • [09/09/2024] Two Dimensions of Professor-Student Rapport Differentially Predict Student Success
  • [09/05/2024] The SAPA Personality Inventory
  • [09/05/2024] Moderated mediation
  • [09/03/2024] BiGGen Bench
  • [09/02/2024] LMSYS Chatbot Arena
  • [09/02/2024] Introduction to Measurement Theory Chapters 1, 2 (2.1-2.8) and 3.
  • [09/01/2024] HCI measurememt
  • [08/30/2024] Randomization Test
  • [08/30/2024] Interview Quantative Statistical
  • [08/29/2024] Cascading Model
  • [08/29/2024] Introduction: The White House (IS_202)
  • [08/29/2024] Circular unidimensional scaling
  • [08/28/2024] Sex and Gender Differences (Neur_542_Week2)
  • [08/26/2024] Workplace Assessment and Social Perceptions (WASP) Lab
  • [08/26/2024] Computational Organizational Research Lab
  • [08/26/2024] Reading List (Recommended by Bo)
  • [08/20/2024] Illinois NeuroBehavioral Assessment Laboratory (INBAL)
  • [08/14/2024] Quantitative text analysis
  • [08/14/2024] Measuring complex psychological and sociological constructs in large-scale text
  • [08/14/2024] LLM for Social Science Research
  • [08/14/2024] GPT for multilingual psychological text analysis
  • [08/12/2024] Questionable Measurement Practices and How to Avoid Them
  • [08/12/2024] NLP for Interest (from Dan Putka)
  • [08/12/2024] ONet Interest Profiler (Long and Short Scale)
  • [08/12/2024] ONet Interests Data
  • [08/12/2024] The O*NET-SOC Taxonomy
  • [08/12/2024] ML Ratings for O*Net
  • [08/09/2024] Limited ability of LLMs to simulate human psychological behaviours
  • [08/08/2024] A large-scale, gamified online assessment
  • [08/08/2024] Text-Based Traitand Cue Judgments
  • [08/07/2024] Chuan-Peng Lab
  • [08/07/2024] Modern psychometrics: The science of psychological assessment
  • [08/07/2024] Interactive Survey
  • [08/06/2024] Experimental History
  • [08/06/2024] O*NET Research reports
  • [07/30/2024] Creating a psychological assessment tool based on interactive storytelling
  • [07/24/2024] My Life with a Theory
  • [07/24/2024] NLP for Interest Job Ratings
  • [07/17/2024] Making vocational choices
  • [07/17/2024] Taxonomy of Psychological Situation
  • [07/12/2024] PathChat 2
  • [07/11/2024] Using games to understand the mind
  • [07/10/2024] Gamified Assessments
  • [07/09/2024] Poldracklab Software and Data
  • [07/09/2024] Consensus-based Recommendations for Machine-learning-based Science
  • [07/08/2024] Using AI to assess personal qualities
  • [07/08/2024] AI Psychometrics And Psychometrics Benchmark
  • [07/02/2024] Prompt Engineering Guide
  • [06/28/2024] Observational Methods and Qualitative Data Analysis 5-6
  • [06/28/2024] Observational Methods and Qualitative Data Analysis 3-4
  • [06/28/2024] Interviewing Methods 5-6
  • [06/28/2024] Interviewing Methods 3-4
  • [06/28/2024] What is Qualitative Research 3
  • [06/27/2024] APA Style
  • [06/27/2024] Statistics in Psychological Research 6
  • [06/27/2024] Statistics in Psychological Research 5
  • [06/23/2024] Bayesian Belief Network
  • [06/18/2024] Fair Comparisons in Heterogenous Systems Evaluation
  • [06/18/2024] What should we evaluate when we use technology in education?
  • [06/16/2024] Circumplex Model
  • [06/12/2024] Ways of Knowing in HCI
  • [06/09/2024] Statistics in Psychological Research 1-4
  • [06/08/2024] Mathematics for Machine Learning
  • [06/08/2024] Vocational Interests SETPOINT Dimensions
  • [06/07/2024] How's My PI Study
  • [06/06/2024] Best Practices in Supervised Machine Learning
  • [06/06/2024] SIOP
  • [06/06/2024] Measurement, Design, and Analysis: An Integrated Approach (Chu Recommended)
  • [06/06/2024] Classical Test Theory
  • [06/06/2024] Introduction to Measurement Theory (Bo Recommended)
  • [06/03/2024] EDSL: AI-Powered Research
  • [06/03/2024] Perceived Empathy of Technology Scale (PETS)
  • [06/02/2024] HCI area - Quantitative and Qualitative Modeling and Evaluation
  • [05/26/2024] Psychometrics with R
  • [05/26/2024] Programming Grammer Design
  • [05/25/2024] Psychometric Network Analysis
  • [05/23/2024] Item Response Theory
  • [05/22/2024] Nature Human Behaviour (Jan - 20 May, 2024)
  • [05/22/2024] Nature Human Behaviour - Navigating the AI Frontier
  • [05/22/2024] Computer Adaptive Testing
  • [05/22/2024] Personality Scale (Jim Shard)
  • [05/22/2024] Reliability
  • [05/19/2024] Chatbot (Jim Shared)
  • [05/17/2024] GOMS and Keystroke-Level Model
  • [05/17/2024] The Psychology of Human-Computer Interaction
  • [05/14/2024] Computational Narrative (Mark's Group)
  • [05/14/2024] Validity Coding
  • [05/14/2024] LLM as A Evaluator
  • [05/14/2024] Social Skill Training via LLMs (Diyi's Group)
  • [05/14/2024] AI Persona
  • [05/09/2024] Psychological Methods Journal Sample Articles
  • [05/08/2024] Meta-Analysis
  • [05/07/2024] Mturk
  • [05/06/2024] O*NET Reports and Documents
  • [05/04/2024] NLP and Chatbot on Personality Assessment (Tianjun)
  • [05/02/2024] Reads on Construct Validation
  • [04/25/2024] Reads on Validity
  • [04/18/2024] AI for Assessment
  • [04/17/2024] Interest Assessment
  • [04/16/2024] Personality Long Reading List (Jim)
    • Personality Psychology Overview
      • Why Study Personality Assessment
    • Dimensions and Types
    • Reliability
    • Traits: Two Views
    • Validity--Classical Articles and Reflections
    • Validity-Recent Proposals
    • Multimethod Perspective and Social Desirability
    • Paradigm of Personality Assessment: Multivariate
    • Heritability of personality traits
    • Classical Test-Construction
    • IRT
    • Social desirability in scale construction
    • Traits and culture
    • Paradigms of personality assessment: Empirical
    • Comparison of personality test construction strategies
    • Clinical versus Actuarial (AI) Judgement and Diagnostics
    • Decisions: Importance of base rates
    • Paradigms of Personality Assessment: Psychodynamic
    • Paradigms of Assessment: Interpersonal
    • Paradigms of Personality Assessment: Personological
    • Retrospective reports
    • Research Paradigms
    • Personality Continuity and Change
Powered by GitBook
On this page

[08/06/2024] O*NET Research reports

Previous[08/06/2024] Experimental HistoryNext[07/30/2024] Creating a psychological assessment tool based on interactive storytelling

Last updated 10 months ago

See Rounds Putka Lewis for Holland RIASEC descriptions and the new SETPOINT/CABIN model

See Rounds Smith Hubert for RIASEC Rating instructions 5.3.1 Page 14

Updating Vocational Interests Information for the O*NET Content Model

  • Introduction

    • The occupational information collected is guided by the ONET Content Model, the conceptual foundation of ONET (Mumford & Peterson, 1999)

    • The O*NET Content Model provides a framework that identifies the most important types of information about work and integrates them into a theoretically and empirically sound system.

    • The present report describes updating and enhancing the RIASEC descriptions, and development of 41 basic interest descriptions based on the Comprehensive Assessment of Basic Interests (CABIN; Su et al., 2019) for inclusion in the O*NET Content Model.

  • Definition of Vocational Interests

    • general interest scales and basic interest scales.

      • General occupational interest scales assess broad themes of preferences (broad interest dimensions) that include a heterogeneous set of work activities or settings. The most familiar general interest scales are the Realistic-Investigative-Artistic-Social-Enterprising-Conventional theoretical model (acronym RIASEC, Holland, 1997).

      • Basic occupational interest scales are specific, homogeneous facets of interests that group together work activities that share similar properties and represent the same abstract object, such as engineering, medical science, music, or accounting. An important characteristic of basic interests is that they rely on everyday language and definitions and therefore, they are easy to communicate to a variety of individuals (e.g., students, adults).

  • Updating the RIASEC Interest Descriptions for the O*NET Content Model

    • The current RIASEC descriptions within the ONET Content Model (heretofore called, the “ONET RIASEC descriptions”) were initially developed at the onset of the ONET Content Model and then refined during the development of the ONET Interest Profiler Long Form (Lewis & Rivkin, 1999; Rounds et al.,1999a; Rounds et al., 1999c). Given their age, the ONET RIASEC descriptions may be dated due to changes in industry and occupations and foremost, changes in required work tasks. To ensure these descriptions reflect the current world of work, we used a variety of information to update them for the ONET Content Model.

    • The updated RIASEC descriptions we developed broadly model Holland’s (1997) RIASEC definitions. However, a major exception is that our updated ONET RIASEC descriptions, conceptualize Holland’s model as a vocational interest model of six multidimensional traits, with each of these RIASEC traits related to multiple basic interests. To update the ONET RIASEC descriptions, we used basic interests drawn from comprehensive assessment of basic interests (CABIN; Su et al., 2019). CABIN consists of 41 basic interests and their corresponding scales. Each basic interest scale is content-specific and unidimensional. Items are representative of the occupations and work tasks covered by each scale. To ensure that, at the item level, the basic interest assessment covered the full range of the world of work, Su et al. (2019) extracted information about work tasks for all the occupations in the ONET system to develop the items. Another difference between Holland’s RIASEC definitions and the updated ONET RIASEC descriptions is that the latter do not include Holland’s surplus meaning drawn from correlates (e.g., abilities, competencies, values, and personality traits). In general, the updated O*NET RIASEC descriptions follow closely the idea that vocational interests are grounded in work activities and these activities have two elements: an action and a target object or context (Rounds & Su, 2014). For example, the activity, “study the effects of a new medicine”, has the action of “study” and an object of “new medicine”. Another example, “teach a pre-school class”, has the action of “teach” and the object and context of “pre-school class”.

  • Writing Updated O*NET RIASEC Descriptions

    • For each RIASEC interest, we developed:

      • a written definition,

      • lists of illustrative work activities,

      • lists of illustrative occupations (drawn from the O*NET-SOC 2019 taxonomy),

      • lists of keywords.

    • Process and Results

      • To convey the meaning of the 41 basic interests to prospective users of the ONET Content Model, we developed:

        • written definitions,

        • lists of illustrative work activities,

        • lists of illustrative occupations (drawn from the ONET-SOC 2019 taxonomy) for each basic interest,

        • linkages from each basic interest to the RIASEC themes.

https://www.onetcenter.org/content.html
595KB
Rounds Putka Lewis Updating Vocational Interests Information for the ONET Content Model .pdf
pdf
491KB
Rounds Smith Hubert 1999 Development of Occupational Interest Profiles for ONET.pdf
pdf