😶‍🌫️
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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Last updated 1 year ago

Coefficient alpha and the internal structure of tests

Abstract

A general formula (α) of which a special case is the Kuder-Richardson coefficient of equivalence is shown to be the mean of all split-half coefficients resulting from different splittings of a test. α is therefore an estimate of the correlation between two random samples of items from a universe of items like those in the test. α is found to be an appropriate index of equivalence and, except for very short tests, of the first-factor concentration in the test. Tests divisible into distinct subtests should be so divided before using the formula. The index𝑟¯𝑖𝑗, derived from α, is shown to be an index of inter-item homogeneity. Comparison is made to the Guttman and Loevinger approaches. Parallel split coefficients are shown to be unnecessary for tests of common types. In designing tests, maximum interpretability of scores is obtained by increasing the first-factor concentration in any separately-scored subtest and avoiding substantial group-factor clusters within a subtest. Scalability is not a requisite.

Main point

Introduces Cronbach's alpha, a measure of internal consistency reliability for a set of items or test scores.

Making sense of Cronbach's alpha

Main point

A practical guide to understanding and interpreting Cronbach's alpha.

The Design and Analysis of Clinical Experiments

Main point

Discusses the design and analysis of studies involving repeated measures, including test-retest reliability.

Measurement error in psychological research: Lessons from 26 research scenarios

Main point

Provides a detailed discussion of measurement error and its implications for test-retest reliability.

A coefficient of agreement for nominal scales.

Main point

Introduces Cohen's kappa, a statistical measure of inter-rater reliability for categorical data.

Intraclass correlations: Uses in assessing rater reliability.

Main point

Discusses intraclass correlation coefficients (ICCs) and their use in assessing inter-rater reliability.

Scale reliability, Cronbach’s coefficient alpha, and violations of essential tau-equivalence with fixed congeneric components

Abstract

The population discrepancy between Cronbach's Coefficient Alpha and scale reliability with fixed congeneric measures, uncorrelated errors, and sampling of subjects is studied. This difference is expressed in terms of individual component violations of the assumption of essential T-equivalence that is necessary and sufficient for Alpha to equal composite reliability. An upper bound of the discrepancy is obtained and its magnitude assessed in practical contexts of informed scale development. As an alternative when the difference may be considerable, a latent variable model is recommended for estimating scale reliability.

Test Theory: A Unified Treatment

Book review

https://onlinelibrary.wiley.com/doi/book/10.1002/9781118032923
https://pubmed.ncbi.nlm.nih.gov/18839484/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4205511/
Test TheoryGoogle Books
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