Methodology for Multilevel Modeling in Educational Research: Concepts and Applications

✍ Scribed by Myint Swe Khine (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 419
• Category: Library

✦ Synopsis

This edited volume documents attempts to conduct systematic and prodigious research using multilevel analysis in educational settings, and present their findings and identify future research directions. It showcases the versatility of multilevel analysis, and elucidates the unique advantages in examining complex and wide-ranging educational issues. This book brings together leading experts around the world to share their works in the field, highlighting recent advances, creative and unique approaches, and innovative methods using multilevel modeling and theoretical and practical aspects of multilevel analysis in culturally and linguistically-diverse educational contexts.

✦ Table of Contents

Contents
Editor and Contributors
Part I Introduction
1 Hierarchical Linear Modeling and Multilevel Modeling in Educational Research
1.1 Introduction
1.2 Theoretical Foundations and Conceptual Frameworks
1.3 Methodology for Multilevel Modeling
1.4 Multilevel Analysis of PISA and TIMSS Data
1.5 Multilevel Modeling in Educational Research
1.6 Conclusion
Part II Theoretical Foundations and Conceptual Frameworks
2 A Primer for Using Multilevel Confirmatory Factor Analysis Models in Educational Research
2.1 Introduction
2.1.1 Conceptual Principles
2.1.2 Benefits of Using the MCFA Framework
2.2 Analysis Choices for MCFA
2.2.1 Ignoring Nesting
2.2.2 Accounting for Nested Data
2.2.3 Evaluating Model Fit in MCFA
2.3 Applied Example
2.3.1 Areas for Future Research
References
3 Multilevel Model Selection: Balancing Model Fit and Adequacy
3.1 Introduction
3.2 Conceptual Overview of Estimation in MLM
3.3 Reliability of Cluster J
3.4 Maximum Likelihood Estimation
3.4.1 FIML
3.4.2 REML
3.4.3 FIML vs. REML
3.5 Model Selection
3.6 Criteria for Evaluating Model Fit
3.6.1 Likelihood Ratio Test (LRT)
3.6.2 Recommendations for Testing Random Effects
3.6.3 Other Issues with Modeling Random Slopes
3.6.4 Information Criteria (ICs)
3.7 Model Adequacy
3.7.1 Proportional Reduction in Variance Statistics
3.7.2 Variance Decomposition Framework for MLM
3.8 Conclusion
References
4 Concepts and Applications of Multivariate Multilevel (MVML) Analysis and Multilevel Structural Equation Modeling (MLSEM)
4.1 Introduction of Multilevel Modeling
4.2 Multilevel Structural Equation Modeling (MLSEM)
4.3 Examples of MLSEM Application
4.4 Issues That Should Be Considered When Reporting MLSEM Applications
4.5 Multivariate Multilevel Model (MVML)
4.6 Basic Concepts and Assumptions of the Multivariate Multilevel Model
4.7 Multivariate Random Intercept Model and Examples
4.8 Examples of MVML Applications
4.9 Summary
References
5 Data Visualization for Pattern Seeking in Multilevel Modeling
5.1 Introduction
5.2 Data Source
5.3 Preliminary Data Visualization
5.3.1 Profile Analysis by Linking and Brushing
5.3.2 Binning and Median Smoothing for Examining the Relationship between SES and Math Achievement
5.3.3 Data Reduction for Examining the Relationship between SES and Math Achievement in Bubble Plot
5.3.4 Examining Variation between Schools by ANOM Plot
5.4 Multilevel Modeling
5.4.1 Computing ICC to Decompose Variance Components by Random Effects Modeling
5.5 Running a Mixed Model for Fixed and Random Effects using SES
5.5.1 Using School Mean and Locally Centered SES to Disentangle Within and between Groups
5.6 Conclusion
References
6 Doubly Latent Multilevel Structural Equation Modeling: An Overview of Main Concepts and Empirical Illustration
6.1 Introduction
6.2 Doubly Latent Multilevel Models
6.2.1 Contextual vs. Climate Effects in Doubly Latent Multilevel Models
6.2.2 Measurement and Sampling Errors in Doubly Latent Multilevel Models
6.2.3 Centering in Doubly Latent Multilevel Models
6.3 Testing Mediation in Multilevel Structural Equation Modeling Framework
6.4 An Empirical Illustration Using Doubly Latent Multilevel Structural Equation Modeling
6.4.1 Theoretical Background and Description of the Study
6.4.2 Estimating Measurement Model and Reliability
6.4.3 Estimating Structural Model
6.5 Conclusions
References
Part III Methodology for Multilevel Modeling
7 Analyzing Large-Scale Assessment Data with Multilevel Analyses: Demonstration Using the Programme for International Student Assessment (PISA) 2018 Data
7.1 Introduction
7.2 PISA 2018 Data
7.3 Complex Survey Design
7.3.1 Sampling Weights
7.3.2 Plausible Values
7.3.3 Replicate Weights
7.4 Specifying a Multilevel Model
7.5 Example Analysis
7.6 Obtaining the Data
7.7 Model Specification
7.8 Analysis
7.9 Results
7.9.1 Descriptive Statistics
7.9.2 Math Achievement Outcome
7.9.3 MASTGOAL Outcome
7.10 Conclusion
Appendix: R syntax
References
8 Multilevel Modelling of International Large-Scale Assessment Data
8.1 Introduction
8.2 Sampling in ILSAs and the Use of Multilevel Models
8.3 Outcome Variables in ILSAs
8.3.1 Plausible Values
8.3.2 Continuous and Non-continuous Performance Outcomes
8.4 Configuration of Multilevel Models
8.5 Sampling Weights in ILSAs
8.6 Software
8.7 Summary
References
9 Transparency and Replicability of Multilevel Modeling Applications: A Guideline for Improved Reporting Practices
9.1 Introduction
9.2 Statement of Research Questions and Hypotheses
9.3 Description of the Sampling Procedures
9.4 Sample Size, Power, and Precision
9.5 Psychometrics
9.6 Missing data Treatment
9.7 Model Specifications
9.8 Estimation Methods and Software
9.9 Statistical Inference
9.10 Interpretation of Regression Coefficients
9.11 Effect Sizes
9.12 Assumption Checking
9.13 Discussion
9.14 Summary and Checklist
References
10 Application of Multilevel Models to International Large-Scale Student Assessment Data
10.1 Introduction
10.2 Example of Typical Use—Modeling Relationship Between Socioeconomic Background and Student Achievement in PISA 2018
10.3 Applications
10.4 Plausible Values and Multilevel Models
10.5 Survey Weights Adjustments for Multilevel Models
10.6 Estimation of Standard Errors
10.7 Multilevel Models with Additional Layers
10.8 Summary
References
Part IV Multilevel Analysis of PISA and TIMSS Data
11 Changing Trends in the Role of South African Math Teachers’ Qualification for Student Achievement: Findings from TIMSS 2003, 2011, 2015
11.1 Introduction
11.2 Teacher Education of Mathematics Teachers in Post-apartheid South Africa
11.3 Teacher Allocation and Placement in Schools
11.4 Research on Teacher Participation in Professional Development Programs
11.5 Research on the Relationship Between Teacher Qualification and Learning Outcomes
11.6 Research the Relationship Between Teacher Qualification and Learning Outcomes in Mathematics in South Africa
11.7 Research Questions
11.8 Data, Population, and Sample
11.9 Measures
11.9.1 Teacher Qualification
11.9.2 Teacher Covariates
11.9.3 Student Level Control Variables
11.9.4 Classroom Context Control Variables
11.9.5 Student Outcomes
11.9.6 Analytic Strategy
11.9.7 Limitations
11.10 Results
11.10.1 Percentage of Learners by Qualification Levels
11.10.2 Teacher Allocations by Qualification Profile and School Student Composition
11.10.3 Intensity and Focus of Teacher Participation in Formal Professional Development Activities
11.10.4 Relationship Between Teacher Qualification and Student Outcomes
11.11 Summary of Findings
References
12 Revisiting the Relationship Between Science Teaching Practice and Scientific Literacy: Multi-level Analysis Using PISA
12.1 Introduction
12.2 Literature Review
12.2.1 Benefits and Challenges of DI and IBT
12.2.2 Review of Impacts of Teaching Practices (Inquiry Versus Direct) on Science Achievement
12.2.3 Relationship Between Inquiry Teaching and Science Achievement in PISA Studies
12.3 Methods
12.3.1 Data and Sample
12.3.2 Measures
12.3.3 Multivariate Multilevel Model
12.4 Results
12.5 Discussion
12.5.1 Implications for Teacher Education and Professional Development
12.5.2 Limitations and Future Directions
12.6 Conclusion
References
13 Family Meals and Academic Performance: A Multilevel Analysis for Spain
13.1 Introduction
13.2 Literature Review
13.3 Methodological Approach
13.3.1 Variables
13.3.2 Multilevel Modeling
13.4 Results
13.5 Conclusions
References
14 Multilevel Modeling of Nordic Students’ Mathematics Achievements in TIMSS 2019
14.1 Introduction
14.2 Method
14.2.1 Participants
14.2.2 Statistical analyses
14.3 Results
14.4 Discussion
References
15 Teachers’ Perceptions of School Ethical Culture: The Implicit Meaning of TIMSS
15.1 Introduction
15.2 Theoretical Background
15.2.1 Ethics in the Context of National and Universal Culture
15.2.2 Confusion Around the Definitions of Culture and Climate in the Context of Ethics
15.2.3 School Ethical Culture
15.2.4 The Ethical Aspects of TIMSS
15.3 Method
15.3.1 Context
15.3.2 Sample
15.3.3 Overview of Procedures and Analyses
15.4 Results
15.5 Discussion
15.6 Conclusions
Appendix: Selection rule for relevant items (Expert judgment)
References
Part V Multilevel Modeling in Educational Research
16 Why They Want to Leave? A Three-Level Hierarchical Linear Modeling Analysis of Teacher Turnover Intention
16.1 Introduction
16.2 Multilevel Modeling and Teacher Turnover Research
16.3 Teacher Turnover Intention
16.4 Teacher- and School-Level Characteristics and Teacher Turnover Intention
16.5 Country-Level Variables and Teacher Turnover Intentions
16.5.1 Cross-Level Interactions (Moderation Effects)
16.6 Method
16.7 Findings
16.7.1 The Effect of Individual and School Characteristics (Compositional Effects)
16.7.2 The Effects of Country Variables
16.7.3 The Cross-Level Interaction (The Moderation Effect of Country Variables)
16.8 Conclusion and Implications
16.8.1 Limitations
References
17 Daycare Centers’ Composition and Non-native Children’s Language Skills at School Entry: Exploring the Nature of Context Effects Using Multilevel Modeling
17.1 Introduction
17.2 Purpose of the Present Study
17.3 Background
17.3.1 Theoretical Framework
17.3.2 Summary of Previous Research on Composition Effects
17.3.3 Research Question
17.4 Multilevel Modeling as an Analytical Strategy for Context Effects
17.4.1 Five-Step Analytical Process
17.4.2 Model Fits and Indices
17.4.3 Assumptions and Data Requirements
17.5 Applying MLM: Composition Effects and Non-native Children’s Language Skills
17.5.1 Sample
17.5.2 Variables
17.5.3 Modeling Approach
17.5.4 Results
17.6 Discussion
References
18 Gender Effect at the Beginning of Higher Education Careers in STEM Studies: Does Female Recover Better Than Male?
18.1 Introduction
18.2 Empirical Framework
18.2.1 Internal Student Mobility in HE in Italy
18.2.2 A Brief Overview on Students’ Performance, Transfer Shock, and Gender
18.2.3 Using Multilevel Modelling in HE in Italy
18.2.4 Measuring Academic Performance in This Study
18.3 Data, Variables, and Method
18.3.1 Data
18.3.2 Variables
18.3.3 Method
18.4 Results
18.5 Conclusion
References
19 Service Satisfaction and Service Quality: A Longitudinal and Multilevel Study of User Satisfaction with Kindergartens in Norway
19.1 Introduction
19.1.1 Norwegian Kindergartens: Institutional Setting
19.2 Service Satisfaction and Quality: Expectations
19.3 Data and Variables
19.4 Statistical Approach
19.5 Results
19.6 Conclusion
Appendix
References
20 Multilevel Modeling and Assessment of the Study-Relevant Knowledge of First-Year Students in a Master's Program in Business and Economics
20.1 Introduction and Research Focus
20.2 Theoretical Foundation and Hypotheses
20.3 The Assessment of Economic Knowledge: Study Design, Instruments, and Sampling
20.4 Methods and Results of Structural Equation Models in the Multilevel Approach
20.4.1 The Multilevel Approach in a Structural Equation Model
20.4.2 Confirmatory Factor Analysis (CFA)
20.4.3 Two-Dimensional Multilevel Model with Covariates (MMIMIC Models)
20.5 Discussion and Conclusion
References
Index