This book includes presentations given at the 88th annual meeting of the Psychometric Society, held in Maryland, USA on July 24–28, 2023.
The proceeding covers a diverse set of psychometric topics. The topics include, but are not limited to item response theory, cognitive diagnostic models, Bayesian estimation, validity and reliability issues, and several applications within different fields. The authors are from all over the world, they work in different psychometrics areas, as well as having diverse professional and academic experiences.
Cuprins
Chapter 1. Repeated measurement analysis for non-linear data in small samples.- Chapter 2. Examining the Measurement Invariance of the Chinese Short Grit Scale.- Chapter 3. Data Preprocessing Techniques using Machine Learning Algorithms in Large-scale Assessment.- Chapter 4. A two-stage approach to a latent variable mixed-effects location scale model.- Chapter 5. Sparse Bayesian joint modal estimation for item factor analysis.- Chapter 6. Investigating the impact of equating on measurement error using generalizability theory.- Chapter 7. Method Effects of Item Wording: MIRT Estimation Based on Equivalence Method.- Chapter 8. Item Response Theory Modeling with Response Times: Some Issue.- Chapter 9. Validity evidence for the Teach ECE classroom observation tool.- Chapter 10. Application of topic modeling techniques in meta-analysis studies.- Chapter 11. Validation of the Household Food Security Survey Module (HFSSM) using Factor Analysis and Rasch Modeling.- Chapter 12. Testing CDM local independence assumptions using nested model selection criteria.- Chapter 13. The Impact of Generating Model on Pre-knowledge Detection in CAT.- Chapter 14. Exploring Attenuation of Reliability in Categorical Subscore Reporting.- Chapter 15. Diagnosing skills and misconceptions with Bayesian Networks applied to diagnostic multiple-choice tests.- Chapter 16. Investigating variable selection techniques under missing data: a simulation study .- Chapter 17. Optimal Implementation of Propensity-Score Matching Methods: A Monte Carlo Study on Estimating Binary Treatment Effects on Binary Outcomes.- Chapter 18. Using machine/deep learning algorithms for the fixed effect prediction in non-linear Mixed Effects Models – the mixed ML framework.- Chapter 19. Psychometric evaluation of Positive and Negative Symptom Scale (PANSS): Harmonizing Classical Item Response Theory with the Perspective from Network Approach.- Chapter 20. Fitting IRT Diffusion Model to complex cognition response times.- Chapter 21. Empirical evaluations for DIF detection methods.- Chapter 22. Fisher Information-Based Difficulty and Discrimination Measures in Binary IRT.- Chapter 23. Empirical comparisons among models in detecting extreme response styles.- Chapter 24. A Hierarchical Prior for Bayesian Variable Selection in Regression Model.- Chapter 25. Comparing Different Correlation Test Methods.- Chapter 26. Priors in Bayesian Estimation under the Graded Response Model.- Chapter 27. Information matrix test misspecification assessment in cognitive diagnostic models.- Chapter 28. Impact of Ignoring Rater Effects in Objective Structured Clinical Examinations.- Chapter 29. The Gumbel-Reverse Gumbel (GRG) Model for Binary Data: A New Asymmetric Item Response Model.- Chapter 30. Nonparametric estimation of the risk and odds ratio in rare events meta analysis with arm based and contrast based approaches.- Chapter 31. Differential Step Functioning with Scale Purification for Polytomous Items.- Chapter 32. Comparing maximum likelihood and MCMC estimation of the multivariate social relations model.- Chapter 33. Using Mantel-Haenszel for Detecting Testlet Effects: Testing it All at Once.- Chapter 34. Identifiability Conditions in Cognitive Diagnosis:Implications for Q-Matrix Estimation Algorithms.- Chapter 35. Psychometric Perspectives on Modeling and Assessing Synergies.- Chapter 36. Global validity of assessments: Location and currency.- Chapter 37. Gaussian graphical model for evaluating local item dependency in response times.- Chapter 38. Assessment Engineering Meets Generative AI: Unlocking New Opportunities for Digital Assessment.- Chapter 39. Bayesian Mixture Multilevel Vector Autoregressive (B-MMVAR) Modeling.- Chapter 40. Enhancing Learning and Assessment: Exploring the Potential of Performance Factor Analysis in attribute-oriented performance and difficulty parameters estimation.- Chapter 41. DIF Detection in a Response Time Measure: An LRT Method.- Chapter42. Nonparametric Estimation of CATE with Cluster-Robust Confidence Bands.- Chapter 43. A Causal Mediation Framework for Investigating Treatment Effects in Longitudinal Studies.- Chapter 44. Are we playing the same game: Translating fairness content.- Chapter 45. Revisiting the 1PL-AG item response model: Bayesian estimation and application.- Chapter 46. Maximum Likelihood Estimation using a Possibly Misspecified Parameter Redundant Model.- Chapter 47. Comparing non-parametric estimations of treatment effect heterogeneity in the context of clustered data.- Chapter 48. Extreme and Midpoint Response Styles: Two Sides of the Same Coin?- Chapter 49. A family of discrete kernels for presmoothing.- Chapter 50. The deconstruction of measurement invariance (and DIF).- Chapter 51. Efficient additive Gaussian process models for large-scale balanced multi-level data.- Chapter 52. An Investigation of Missing Data Analytical Methods in Longitudinal Research: Traditional and Machine Learning Approaches.- Chapter 53. Test Analysis Method using Piecewise Linear ICCs.- Chapter 54. Relationship among measurement invariance, differential item functioning andmean comparison.- Chapter 55. Generative Distractor Modeling with Generative AI.- Chapter 56. Q-matrix identification using text classification.- Chapter 57. The Nonparametric Item Selection Method for Multiple-Choice Items in CD-CAT.
Despre autor
Marie Wiberg is professor in Statistics with specialty in psychometrics at Umeå University in Sweden. Her research interests include test equating, applied statistics, parametric and nonparametric item response theory, large-scale assessments and educational measurement and psychometrics in general.
Jee-Seon Kim is a professor in the Department of Educational Psychology at the University of Wisconsin-Madison. Her research interests are concerned with the development and application of quantitative methods in the social and behavioral sciences, focusing on causal inference, heterogeneous treatment effects, omitted variable bias, multilevel models and clustered data analysis, latent variable and mixture modeling, and causal machine learning methods.
Heungsun Hwang is Professor of Quantitative Psychology at Mc Gill University in Canada. His research is devoted to the development of quantitative analytics tools for examining complex relationships of various data from psychology and other disciplines toward a better understanding of human behaviour and cognition. Methodologically, he is interested in a wide array of statistical methods in multivariate statistics, structural equation modeling, machine learning, functional data analysis, and genetic and neuroimaging data analysis.
Hao Wu is an associate professor of Quantitative Methods in Department of Psychology and Human Development of Vanderbilt University. His research focuses on the evaluation of statistical models used in psychology and education, especially structural equation models. This includes identifiability, the quantification of various sources of uncertainty, model fit and effect size. His research interest also includes robust and nonparametric methods.
Tracy Sweet is Associate Professor in the Measurement, Statistics and Evaluation program in the Department of Human Development and Quantitative Methodology. Her research focuses on methods for social network analysis with particular focus on multilevel social network models. Recent projects include network interference, measurement error, and missing data.
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