😶‍🌫️
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
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[06/08/2024] Vocational Interests SETPOINT Dimensions

Previous[06/08/2024] Mathematics for Machine LearningNext[06/07/2024] How's My PI Study

Last updated 1 year ago

Measuring Vocational Interests on SETPOINT Dimensions

Abstract

Vocational interest research has seen a resurgence in organizational research, given that it is predictive of key work outcomes. Current research has focused on vocational interests at the broadband interest level. While the RIASEC model has been the typology for broad interest assessment of six interest types, more recent work with the setpoint model suggests eight interest dimensions need to be assessed. To date, there is no assessment tool available for the broad setpoint dimensions. This current study focuses on developing three short scales to assess the eight dimensions of the setpoint model and complements current assessments with three shorter but reliable and valid measurement tools. Implications for research and practice are discussed.

Measuring SETPOINT vocational interest dimensions: The development and validation of three short scales

Highlight

  • Three short scales were created based on the SETPOINT interest model and validated through rigorous psychometric analyses.

  • The structure and dimensions of the SETPOINT model are further validated with the newly created short measures.

  • Compared with the RIASEC measure, results from the SETPOINT short measures showed better psychometric properties.

  • The SETPOINT model showed stronger predictive power than the RIASEC model in predicting occupational membership groups.

  • Short scales measuring at the dimensional level of vocational interests can be useful in measuring heterogeneous factors.

Abstract

Vocational interest research has seen a resurgence in the applied psychology literature, given evidence showing its predictive validity for key work outcomes. There is a need for integrative, reliable, and valid measures to advance research in this space. While the RIASEC model of vocational interests (Holland, 1997) has been the most widely used and studied typology for the assessment of six broad interest types, more recent work with the SETPOINT model (Su et al., 2019) suggests that eight interest dimensions provide better fit to interest data and demonstrates stronger criterion-related validity evidence. However, to date, no short scales are available for measuring the broad SETPOINT dimensions. We developed three short scales with 8, 24, and 41 items, respectively, that capture the eight dimensions of the SETPOINT model in an integrative manner. Using a sample of 972 full-time working adults assessed across two-time points over five weeks, we validated these three short scales following robust psychometric analyses. These scales are shown to have good psychometric properties. The development and validation of the three short scales help close the operational gap for the SETPOINT model and further facilitate the study of interests and use of interest measures in academic and applied settings.

Toward a dimensional model of vocational interests.

Abstract

Growing evidence on the predictive validity of vocational interests for job performance calls for greater consideration of interest assessment in organizations. However, a consensus on the fundamental dimensions of interests that are aligned with the contemporary world of work is still lacking. In the current research, we developed an organizing framework of vocational interests and empirically validated an 8-dimension model (SETPOINT: Health Science, Creative Expression, Technology, People, Organization, Influence, Nature, and Things). We propose that interests are structured hierarchically, with preferences for specific work activities at the lowest level (assessed using interest items), basic interests for homogeneous classes of activities at the intermediate level (assessed using basic interest scales), and broad-band interest dimensions describing general tendencies of individuals to be drawn to or motivated by broad types of work environments at the top. To derive broad-band interest dimensions, it is necessary to base it on a comprehensive range of content-specific basic interest constructs. In Study 1, we conducted an extensive review of existing basic interest scales and developed a new assessment of basic interests with 41 homogeneous scales across two samples. In Study 2, we demonstrated the structural validity of the proposed dimensional model using second-order confirmatory factor analysis and exploratory structural equation modeling with a large, diverse sample of working adults and supported its predictive validity for occupational membership in new and traditional sectors of work. We discuss implications from the current findings for building interest theory, using interest assessment for organizational research, and evaluating interest structure with appropriate methods. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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