This proceedings volume compiles and expands on selected and peer reviewed presentations given at the 81st Annual Meeting of the Psychometric Society (IMPS), organized by the University of North Carolina at Greensboro, and held in Asheville, North Carolina, July 11th to 17th, 2016
.
IMPS is one of the largest international meetings focusing on quantitative measurement in psychology, education, and the social sciences, both in terms of participants and number of presentations. The meeting built on the Psychometric Society’s mission to share quantitative methods relevant to psychology, addressing a diverse set of psychometric topics including item response theory, factor analysis, structural equation modeling, time series analysis, mediation analysis, cognitive diagnostic models, and multi-level models. Selected presenters were invited to revise and expand their contributions and to have them peer reviewed and published in this proceedings volume.
Previous volumes to showcase work from the Psychometric Society’s meetings are
New Developments in Quantitative Psychology: Presentations from the 77th Annual Psychometric Society Meeting (Springer, 2013),
Quantitative Psychology Research: The 78th Annual Meeting of the Psychometric Society (Springer, 2015),
Quantitative Psychology Research: The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014 (Springer, 2015), and
Quantitative Psychology Research: The 80th Annual Meeting of the Psychometric Society, Beijing, 2015 (Springer, 2016).
Spis treści
1. New Results on an Improved Algorithm Estimating Generalized Latent Variable Models (von Davier).- 2. Properties of second-order exponential models as multidimensional item response models (Anderson and Yu).- 3. Pseudo-likelihood Estimation of Multidimensional Response Models:Polytomous and Dichotomous Items (Pack and Anderson).- 4. Fitting Graded Response Models to Data with Nonnormal Latent Traits (Kuo and Sheng).- 5. An Extention of Rudner-based Consistency and Accuracy Indices for Multidimensional Item Response Theory (Wang, Song and Ding).- 6. Supporting diagnostic inferences using significance tests for subtest scores (Lorie).- 7. A comparison of two MCMC algorithms for the 2PL IRT model (Chang and Sheng).- 8. Similar DIFs: Differential Item Functioning and Factorial Invariance for Scales with Seven (’Plus or Minus Two’) Response Alternatives (Thissen).- 9. Finally! A Valid Test of Configural Invariance Using Permutation in Multiple-Group CFA (Jorgenson, Kite, Chen and Short).- 10. Outcries of Dual Scaling: The Key is Duality (Nishisato).- 11. The Most Predictable Criterion with Fallible Data (Kim).- 12. Asymmetric Multidimensional Scaling of Cognitive Similarities Among Occupational Categories (Okada and Hayashi).- 13. On the Relationship Between Squared Canonical Correlation and Matrix Norm (Hyashi, Yuan and Liang).- 14. Breaking through the sum score barrier (Ramsay and Wiberg).- 15. Overestimation of Reliability by Guttman’s Lambda 4, Lambda 5, and Lambda 6, and the Greatest Lower Bound (Oosterwijk, van der Ark and Sijtsma).- 16. The Performance of Five Reliability Estimates at Multidimensional Test Situations (Sha and Ackerman).- 17. Weighted Guttman errors: Handling ties and two-level data (Koopman, Zijlstra and Van der Ark).- 18. Measuring Cognitive Processing Capabilities in Solving Mathematical Problems (Embretson).- 19. Parameter Constraints of the Reduced RUM as a Logit Model (Kohn).- 20. Hypothesis Testing for Item Consistency Index in Cognitive Diagnosis Assessment (Song and Wang).- 21. The irreplaceability of a reachability matrix (Ding, Wang, Luo, Xiong and Meng).- 22. Ensuring test quality over time by monitoring the equating transformations (Wiberg).- 23. An illustration of the Epanechnikov and Adaptive continuization methods in Kernel equating (González and von Davier).- 24. (The Potential for) Accumulated Linking Error in Trend Measurement in Large-Scale Educational Assessments (Harrell).- 25. IRT observed-score equating with the non-equivalent groups with covariates design (Sansivieri and Wiberg).- 26. Causal Inference with Observational Multilevel Data: Investigating Selection and Outcome Heterogeneity (Kim, Lim and Steiner).- 27. Non-Equivalent Groups With Covariates Design Using Propensity Scores for Kernel Equating (Wallin and Wiberg).- 28. A mixture partial credit model analysis using a language-based covariate (Kim, Kwak and Cohen).- 29. Investigating Constraint-weighted Item Selection Procedures in Unfolding CAT (Su).- 30. Rating Scale Format and Item Sensitivity to Response Style in Large-Scale Assessment (Deng and Bolt).- 31. Mode Comparability Studies for a High-Stakes Testing Program (Li, Yi and Harris).- 32. Power analysis for t-test with non-normal data and unequal variances (Du, Zhang and Yuan).- 33. Statistical Power for One-Way ANOVA with Binary or Count Data (Mai and Zhang).- 34. Bayesian robust estimation in causal two-stage-least-squares modeling with instrumental variables (Shi and Tong).- 35. Measuring grit among first-generation college students: A psychometric analysis (Midkiff, Langer, Demetriou and Panter).- 36. A Comparison of Item Parameter and Standard Error Recovery Across Different R Packages for Popular Unidimensional IRT Models (Kim and Paek).
O autorze
L. Andries van der Ark is professor by special appointment in quantitative research methods at the Research Institute of Child Development and Education of the University of Amsterdam. His research interests include reliability analysis, nonparametric item response theory, Mokken scale analysis, and marginal models for the analysis of test and questionnaire data.
Marie Wiberg is professor of statistics with a specialty in psychometrics at Umeå University in Sweden. Her research interests include test equating, applied statistics, large scale assessments, and psychometrics in general.
Steven A. Culpepper is associate professor of statistics at the University of Illinois at Urbana-Champaign. His research interests include statistical modeling in the social sciences, Bayesian models and computation, cognitive diagnosis, and alternative standardized testing formats.
Jeffrey A. Douglas is a professor of statistics at the University of Illinois at Urbana-Champaign. He conducts research in latent variable analysis with a particular emphasis on item response models and cognitive diagnosis models with applications in educational testing.
Wen-Chung Wang is chair professor of educational and psychological measurement at the Educational University of Hong Kong. His research interests include Rasch measurement, item response theory, computerized adaptive testing, diagnostic classification models, and ipsative data analysis.
Więcej książek elektronicznych tego samego autora (ów) / Redaktor
927 Ebooki w tej kategorii
