Recent Advances on Sampling Methods and Educational Statistics: In Honor of S. Lynne Stokes

Awards, Honors, and Publications of S. Lynne Stokes
	Awards and Honors
	Publications
		Refereed Journals and Proceedings
		Book Chapters
Preface
Contents
Contributors
Part I Ranked-Set Sampling, Judgement Post-stratified Sampling, and Capture-Recapture Methods
	Predictive Modelling and Judgement Post-stratification
		1 Ranked Set Sampling and Judgement Post-stratification
		2 Multivariate Order Statistics and JPS
		3 Consistency of JPS Estimators
		4 Covariates or Ranks?
		5 The Predictive Rank Distribution
		6 Simulation Study
		7 Discussion
		References
	Judgment Post-stratified Sampling with Multiple Ranking: A Comparison with Ranked Set Sampling
		1 Introduction
		2  Sampling Designs with Multiple Ranking Methods
		3 Statistical Inference Using RSS and JPS
		4 Comparison of RSS and JPS Designs
		5 Application
		6 Concluding Remarks
		References
	Efficient Sample Allocation by Local Adjustment for Unbalanced Ranked Set Sampling
		1 Introduction
		2 More Efficient URSS than BRSS
		3 Graphical Illustration of the Sample Allocation Set N
		4 Sample Allocation Adjustment
			4.1 Local Ratio Consistent and Approximate Neyman Allocations
			4.2 Comparison via Simulation
		5 Data Example
		6 Conclusion
		References
	On the Versatility of Capture-Recapture Modeling: Counting What We Don't See
		1 Introduction
		2 Capture-Recapture
			2.1 2-Sample, Closed Population, Single State
			2.2 >2-Sample, Closed Population, Single State
			2.3 >2 Samples, Open Populations, Single State
			2.4 >2 Samples, Open Populations, Multiple States
			2.5 Occupancy Models, Closed System, Single State
			2.6 Occupancy Models, Open System, Single State
			2.7 Occupancy Models, Multiple States, False Positives
			2.8 Software
			2.9 Summary
		3 Beyond Traditional Applications
			3.1 Human Health and Epidemiology
				3.1.1 Population-Level Inferences
				3.1.2 Individual-Level Inferences
			3.2 Social Sciences
				3.2.1 Census
				3.2.2 Homeless
				3.2.3 Problem Drug Users
				3.2.4 Criminal Activities
				3.2.5 World Conflicts
			3.3 Quality Control
			3.4 Remote Sensing
			3.5 Paleobiology
			3.6 Miscellaneous Applications
		4 Discussion
		References
	Advances in the Use of Capture-Recapture Methodology in the Estimation of U.S. Census Coverage Error
		1 Introduction
		2 Background
		3 Post-Enumeration Survey in 1950, 1960, and 1980
			3.1 PES in 1950 and 1960
			3.2 PES 1980 and Dual System Estimation
				3.2.1 Dual System Estimation
				3.2.2 1980 PES
		4 1990 PES
			4.1 Dr. Stokes's Contributions to Interviewer Quality Control
			4.2 Outcome of the 1990 PES
		5 2000 Census Accuracy and Coverage Evaluation
		6 Innovations in the 2010 PES Methodology
			6.1 Components of Census Coverage Error
			6.2 Correction for Correlation Bias
			6.3 Logistic Regression Instead of Post-stratification
			6.4 Consultation with Dr. Stokes and Other Experts About 2010 PES Methodology
			6.5 2010 PES Estimates
		7 Current Research
		8 Summary
		References
Part II Nonsampling Errors in Statistical Sampling
	Measurement Issues in Synthesizing Survey-Item Responses
		1 Overview
		2 Introduction to Survey Synthesis
		3 The Process of Meta-analysis
			3.1 Step 1: Problem Formulation for Survey Synthesis
			3.2 Step 3: Data Evaluation
		4 Harmonization
			4.1 What Is Harmonization?
			4.2 Conceptual Harmonization
			4.3 Item Features Critical to Harmonization
				4.3.1 Wording of Item Stems
				4.3.2 Number of Response Options
				4.3.3 Nature of Response Options
				4.3.4 Item Polarity or Direction
			4.4 Statistical Harmonization
				4.4.1 Linear Transformations
				4.4.2 Linear Stretching
				4.4.3 Linear Transformation to a Target Mean and Variance
				4.4.4 Assumptions
				4.4.5 Example
				4.4.6 Nonlinear Transformations
				4.4.7 Comparisons of Approaches
		5 Conclusion
		References
	Two Sources of Nonsampling Error in Fishing Surveys
		1 Introduction
		2 The Fishing Effort Survey
		3 Methods of Assessing Bias
			3.1 Nonresponse Bias
			3.2 Noncoverage Bias
		4 Bounds on Bias Estimates
		5 Discussion
		References
	Triple System Estimation with Erroneous Enumerations
		1 Background
		2 Models
			2.1 Model Assumptions and Notation
			2.2 Models with No Erroneous Enumerations
				2.2.1 Model M0: Equal Catchability Model
				2.2.2 Model Mt: Schnabel's Model
				2.2.3 Model Mb: The Trap Response Model
				2.2.4 Model MtAB: Non-stationary Behavioral Response Models
			2.3 Models with Erroneous Enumerations in the ARL
				2.3.1 Model L0: Equal Catchability LCM
				2.3.2 Model Lt: Non-stationary, Independent LCM
				2.3.3 Model LtAB: Non-stationary, Behavioral Response Latent Class Model
		3 Estimation
			3.1 Estimating N
			3.2 Illustration Using Real Data
		4 Assessing Estimation Accuracy Using Artificial Data
			4.1 Simulation Methodology
			4.2 Results for the Mt, MtAB, and Lt Models
		5 Summary and Discussion
		Appendix 1: Derivation of MtAB and LtAB Estimators of n000
		Appendix 2: Derivation of the Estimator
˜ˆN
MtAB (γ )
		References
	Record Linkage in Statistical Sampling: Past, Present, and Future
		1 Introduction
		2 Past Uses of Record Linkage
		3 Current Research and Uses of Record Linkage
		4 Future Uses of Record Linkage and Open Questions
		References
Part III Educational and Behavioral Statistics
	A Bayesian Latent Variable Model for Analysis of Empathic Accuracy
		1 Introduction
		2 Methodology
		3 Applications
			3.1 Study on Social Empathic Accuracy
			3.2 Study on Musical Empathic Accuracy
		4 Conclusion
		References
	Variance Estimation for Random-Groups Linking in Large-Scale Survey Assessments
		1 Introduction
		2 Variance Estimation in Large-Scale Survey Assessments
		3 Variance Estimation to Incorporate Uncertainty in Random-Groups Linking
			3.1 Estimation of Sampling Variance
			3.2 Estimation of Latency Variance
			3.3 Properties of the Proposed Variance Estimation Method
		4 Applications
			4.1 Empirical Results
			4.2 Further Considerations on Latency Variance Estimation
		5 Conclusion
		References
	Item Response Theory and Fisher Information for Small Tests
		1 Basics
		2 Estimation
		3 Test Information
		4 Shapes of TIFs
		5 Uses
			5.1 Standard Error
			5.2 Test Construction and Selection
		6 Small Sample Information of Ability Estimates from IRT Models
		7 Exact Method for Information Calculation
			7.1 Constraint on the Use of the Exact Method
			7.2 Example: Standard Errors
			7.3 Example: Test Construction/Selection
		8 Conclusion
		References
	Statistical Evaluation of Process Variables: A Case Study on Writing Tool Usage in Educational Survey Assessment
		1 Introduction
		2 Method
			2.1 Participants
			2.2 Assignment of Writing Tasks to Sampled Students
			2.3 Process Variable Definitions
			2.4 Data Analysis Procedures
		3 Results
		4 Summary and Discussion
		5 Some Final Comments
		Appendix
		References
Index