| Description |
1 online resource (531 p.) |
| Series |
Contemporary Trends and Issues in Science Education ; v.57 |
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Contemporary trends and issues in science education ; v. 57.
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| Contents |
Intro -- Foreword -- Contents -- Chapter 1: Introduction to Advances in Applications of Rasch Measurement in Science Education -- 1.1 Item Response Theory Models vs. Rasch Models -- 1.2 Theory of Construct -- 1.3 Sample Size for Rasch Analysis -- 1.4 Uses of Fit Statistics -- 1.5 Local Independence and Dimensionality -- 1.6 Wright Map -- 1.7 Linking Rasch Measures -- 1.8 Using Rasch Measures for Subsequent Analyses -- References -- Chapter 2: Rasch Measurement in Discipline-Based Physics Education Research -- 2.1 Motivation and Introduction -- 2.2 Scope and Structure of Review |
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2.3 Diverse Use of Rasch Measurement in Physics Education Research -- 2.3.1 Assessment Revalidation and Assessment Development -- 2.3.2 Diverse Constructs, Assessment Formats and Scoring Schemes -- 2.3.3 Diverse Models and Analytical Techniques -- 2.4 Confusions and Improper Practices of Rasch Measurement in Physics Education Research -- 2.4.1 Theory-Driven Nature of Rasch Measurement -- 2.4.2 Principles and Operations of Rasch Measurement -- 2.4.3 Confirmatory Bias in Practice -- 2.4.4 Inconsistent Benchmarks for Analysis -- 2.5 Discussion and Implication |
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Appendix: A Summary of Reviewed Studies of Rasch Measurement in Discipline-Based Physics Education Research -- References -- Chapter 3: Using R Software for Rasch Model Calibrations -- 3.1 Introduction -- 3.2 What Is R? -- 3.2.1 Installation of the R Software and RStudio -- 3.2.2 Loading Data -- 3.3 Rasch Model Applications in R -- 3.3.1 R Programs/Packages for Rasch Modeling -- 3.3.2 Package Installation -- 3.3.3 Unidimensional Rasch Application for Dichotomous Data (Using the ̀̀eRm ́́package) -- Fitting the Rasch Model -- Item Parameter Estimation -- Item Characteristic Curve (ICC) |
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Person Ability Parameter Estimation -- Model Evaluation -- 3.3.4 Unidimensional Rasch Application for Polytomous Data (Using ̀̀TAM ́́for PCM) -- Fitting the Partial Credit Model -- Item Parameter Estimation -- Category Characteristic Curve -- Person Ability Parameter Estimation -- Model Evaluation -- 3.3.5 Multidimensional Rasch Application for Dichotomous Data (Using ̀̀mirt)́́ -- Fitting the Two-Dimensional Rasch Model -- Item Parameter Estimation -- Item Characteristic Surface -- Person Ability Parameter Estimation -- Model Evaluation -- Epilogue -- R Code -- References |
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Chapter 4: Bayesian Partial Credit Model and Its Applications in Science Education -- 4.1 Introduction -- 4.1.1 Rasch Measurement in Science Education -- 4.1.2 Different Estimation Approaches to Rasch Analyses in Science Education -- 4.1.3 The Bayesian Approach -- 4.1.4 Programme for International Student Assessment -- 4.2 Objectives -- 4.3 Methods -- 4.4 Formulation of the PCM in Stan -- 4.5 Data Simulation for Parameter Recovery -- 4.6 Empirical Application Results and Model Checking -- 4.7 Convergence and Efficiency Diagnostics -- 4.8 Estimated Parameters |
| Summary |
This edited volume presents latest development in applications of Rasch measurement in science education. It includes a conceptual introduction chapter and a set of individual chapters. The introductory chapter reviews published studies applying Rasch measurement in the field of science education and identify important principles of Rasch measurement and best practices in applications of Rasch measurement in science education. The individual chapters, contributed by authors from Canada, China, Germany, Philippines and the USA, cover a variety of current topics on measurement concerning science conceptual understanding, scientific argumentation, scientific reasoning, three-dimensional learning, knowledge-in-use and cross-cutting concepts of the Next Generation Science Standards, medical education learning experiences, machine-scoring bias, formative assessment, and teacher knowledge of argument. There are additional chapters on advances in Rasch analysis techniques and technology including R, Bayesian estimation, comparison between joint maximum likelihood (JML) and marginal maximum likelihood (MML) estimations on model-data-fit, and enhancement to Rasch models by Cognitive Diagnostic Models and Latent Class Analysis. The volume provides readers who are new and experienced in applying Rasch measurement with advanced and exemplary applications in the forefront of various areas of science education research |
| Notes |
4.9 Comparisons Between Frequentist and Bayesian Methods |
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Includes indexes |
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Online resource; title from PDF title page (SpringerLink, viewed August 11, 2023) |
| Subject |
Science -- Study and teaching -- Statistical methods
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Rasch models.
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Rasch models.
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Ensenyament científic.
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Estadística.
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Tests i proves en educació.
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| Genre/Form |
Llibres electrònics.
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| Form |
Electronic book
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| Author |
Liu, Xiufeng, 1962-
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Boone, William J. (William John)
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| ISBN |
9783031287763 |
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3031287762 |
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