Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

The CEFR Companion volume broadens the scope of language education. It reflects academic and societal developments since the publication of the Common European Framework of Reference for Languages (CEFR) and updates the 2001 version. It owes much to the contributions of members of the language teaching profession across Europe and beyond. This volume contains: ? an explanation of the key aspects of the CEFR for teaching and learning; ? a complete set of updated CEFR descriptors that replaces the 2001 set with: modality-inclusive and gender-neutral descriptors; added detail on listening and reading; a new Pre–A1 level, plus enriched description at A1 and C levels; a replacement scale for phonological competence; new scales for mediation, online interaction and plurilingual/pluricultural competence; new scales for sign language competence; ? a short report on the four-year development, validation and consultation processes. The CEFR Companion volume represents another step in a process of engagement with language education that has been pursued by the Council of Europe since 1971 and which seeks to: ? promote and support the learning and teaching of modern languages; ? enhance intercultural dialogue, and thus mutual understanding, social cohesion and democracy; ? protect linguistic and cultural diversity in Europe; and ? promote the right to quality education for all.

Common European Framework of Reference for Languages: learning, teaching, assessment

This book gives an up-to-date overview of methodological and data-analytical issues of cross-cultural studies. Written by leading experts in the field, it presents the most important tools for doing cross-cultural research and outlines design considerations, methods, and analytical techniques that can improve ecological validity and help researchers to avoid pitfalls in cross-cultural psychology. By focusing on the relevant research questions that can be tackled with particular methods, it provides practical guidance on how to translate conceptual questions into decisions on study design and statistical techniques. Featuring examples from cognitive and educational assessment, personality, health, and intercultural communication and management, and illustrating key techniques in feature boxes, this concise and accessible guide is essential reading for researchers, graduate students, and professionals who work with culture-comparative data.

Methods and Data Analysis for Cross-Cultural Research

Basics Introduction The problem of missing data Concepts of MCAR, MAR and MNAR Simple solutions that do not (always) work Multiple imputation in a nutshell Goal of the book What the book does not cover Structure of the book Exercises Multiple imputation Historic overview Incomplete data concepts Why and when multiple imputation works Statistical intervals and tests Evaluation criteria When to use multiple imputation How many imputations? Exercises Univariate missing data How to generate multiple imputations Imputation under the normal linear normal Imputation under non-normal distributions Predictive mean matching Categorical data Other data types Classification and regression trees Multilevel data Non-ignorable methods Exercises Multivariate missing data Missing data pattern Issues in multivariate imputation Monotone data imputation Joint Modeling Fully Conditional Specification FCS and JM Conclusion Exercises Imputation in practice Overview of modeling choices Ignorable or non-ignorable? Model form and predictors Derived variables Algorithmic options Diagnostics Conclusion Exercises Analysis of imputed data What to do with the imputed data? Parameter pooling Statistical tests for multiple imputation Stepwise model selection Conclusion Exercises Case studies Measurement issues Too many columns Sensitivity analysis Correct prevalence estimates from self-reported data Enhancing comparability Exercises Selection issues Correcting for selective drop-out Correcting for non-response Exercises Longitudinal data Long and wide format SE Fireworks Disaster Study Time raster imputation Conclusion Exercises Extensions Conclusion Some dangers, some do's and some don'ts Reporting Other applications Future developments Exercises Appendices: Software R S-Plus Stata SAS SPSS Other software References Author Index Subject Index

Flexible Imputation of Missing Data

Supercapacitors are electrochemical energy storage devices that operate on the simple mechanism of adsorption of ions from an electrolyte on a high-surface-area electrode. Over the past decade, the performance of supercapacitors has greatly improved, as electrode materials have been tuned at the nanoscale and electrolytes have gained an active role, enabling more efficient storage mechanisms. In porous carbon materials with subnanometre pores, the desolvation of the ions leads to surprisingly high capacitances. Oxide materials store charge by surface redox reactions, leading to the pseudocapacitive effect. Understanding the physical mechanisms underlying charge storage in these materials is important for further development of supercapacitors. Here we review recent progress, from both in situ experiments and advanced simulation techniques, in understanding the charge storage mechanism in carbon- and oxide-based supercapacitors. We also discuss the challenges that still need to be addressed for building better supercapacitors.

/pdf/efficient-storage-mechanisms-for-building-better-45mhaouxcn.pdf

Efficient storage mechanisms for building better supercapacitors

Test equating, scaling, and linking: Methods and practices, 3rd ed.

/pdf/test-equating-scaling-and-linking-5bs0hqjt50.pdf

Test Equating, Scaling, and Linking

Contents Preface Notation 1 Introduction and Concepts 1.1 Equating and Related Concepts 1.1.1 Test Forms and Test Specifications 1.1.2 Equating 1.1.3 Processes That Are Related to Equating 1.1.4 Equating and Score Scales 1.1.5 Equating and the Test Score Decline of the 1960s and 1970s 1.2 Equating and Scaling in Practice-A Brief Overview of This Book 1.3 Properties of Equating 1.3.1 Symmetry Property 1.3.2 Same Specifications Property 1.3.3 Equity Properties 1.3.4 Observed Score Equating Properties 1.3.5 Group Invariance Property 1.4 Equating Designs 1.4.1 Random Groups Design 1.4.2 Single Group Design 1.4.3 Single Group Design with Counterbalancing 1.4.4 ASVAB Problems with a Single Group Design 1.4.5 Common-Item Nonequivalent Groups Design 1.4.6 NAEP Reading Anomaly-Problems with Common Items 1.5 Error in Estimating Equating Relationships 1.6 Evaluating the Results of Equating 1.7 Testing Situations Considered 1.8 Preview 1.9 Exercises 2 Observed Score Equating Using the Random Groups Design 2.1 Mean Equating 2.2 Linear Equating 2.3 Properties of Mean and Linear Equating 2.4 Comparison of Mean and Linear Equating 2.5 Equipercentile Equating 2.5.1 Graphical Procedures 2.5.2 Analytic Procedures 2.5.3 Properties of Equated Scores in Equipercentile Equating 2.6 Estimating Observed Score Equating Relationships 2.7 Scale Scores 2.7.1 Linear Conversions 2.7.2 Truncation of Linear Conversions 2.7.3 Nonlinear Conversions 2.8 Equating Using Single Group Designs 2.9 Equating Using Alternate Scoring Schemes 2.10 Preview of What Follows 2.11 Exercises 3 Random Groups-Smoothing in Equipercentile Equating 3.1 A Conceptual Statistical Framework for Smoothing 3.2 Properties of Smoothing Methods 3.3 Presmoothing Methods 3.3.1 Polynomial Log-linear Method 3.3.2 Strong True Score Method 3.3.3 Illustrative Example 3.4 Postsmoothing Methods 3.4.1 Illustrative Example 3.5 Practical Issues in Equipercentile Equating 3.5.1 Summary of Smoothing Strategies 3.5.2 Equating Error and Sample Size 3.6 Exercises 4 Nonequivalent Groups-Linear Methods 4.1 Tucker Method 4.1.1 Linear Regression Assumptions 4.1.2 Conditional Variance Assumptions 4.1.3 Intermediate Results 4.1.4 Final Results 4.1.5 Special Cases 4.2 Levine Observed Score Method 4.2.1 Correlational Assumptions 4.2.2 Linear Regression Assumptions 4.2.3 Error Variance Assumptions 4.2.4 Intermediate Results 4.2.5 General Results 4.2.6 Classical Congeneric Model Results 4.3 Levine True Score Method 4.3.1 Results 4.3.2 First-Order Equity 4.4 Illustrative Example and Other Topics 4.4.1 Illustrative Example 4.4.2 Synthetic Population Weights 4.4.3 Mean Equating 4.4.4 Decomposing Observed Di.erences in Means and Variances 4.4.5 Relationships Among Tucker and Levine Equating Methods 4.4.6 Scale Scores 4.5 Appendix Proof that o2 s (TX) = a2 1o2 s (TV ) Under the Classical Congeneric Model 4.6 Exercises 5 Nonequivalent Groups-Equipercentile Methods 5.1 Frequency Estimation Equipercentile Equating 5.1.1 Conditional Distributions 5.1.2 Frequency Estimation Method 5.1.3 Evaluating the Frequency Estimation Assumption 5.1.4 Numerical Example 5.1.5 Estimating the Distributions 5.2 Braun-Holland Linear Method 5.3 Chained Equipercentile Equating 5.4 Illustrative Example 5.4.1 Illustrative Results 5.4.2 Comparison Among Methods 5.4.3 Practical Issues in Equipercentile Equating with Common Items 5.5 Exercises 6 Item Response Theory Methods 6.1 Some Necessary IRT Concepts 6.1.1 Unidimensionality and Local Independence Assumptions 6.1.2 IRT Models 6.1.3 IRT Parameter Estimation 6.2 Transformations of IRT Scales 6.2.1 Transformation Equations 6.2.2 Demonstrating the Appropriateness of Scale Transformations 6.2.3 Expressing A and B Constants 6.2.4 Expressing A and B Constants in Terms of Groups of Items and/or Persons 6.3 Transforming IRT Scales When Parameters Are Estimated 6.3.1 Designs 6.3.2 Mean/Sigma and Mean/Mean Transformation Methods 6.3.3 Characteristic Curve Transformation Methods 6.3.4 Comparisons Among Scale Transformation Methods 6.4 Equating and Scaling 6.5 Equating True Scores 6.5.1 Test Characteristic Curves 6.5.2 True Score Equating Process 6.5.3 The Newton-Raphson Method 6.5.4 Using True Score Equating with Observed Scores 6.6 Equating Observed Scores 6.7 IRT True Score Versus IRT Observed Score Equating 6.8 Illustrative Example 6.8.1 Item Parameter Estimation and Scaling 6.8.2 IRT True Score Equating 6.8.3 IRT Observed Score Equating 6.8.4 Rasch Equating 6.9 Using IRT Calibrated Item Pools 6.9.1 Common-Item Equating to a Calibrated Pool 6.9.2 Item Preequating 6.9.3 Robustness to Violations of IRT Assumptions 6.10 Equating with Polytomous IRT 6.10.1 Polytomous IRT Models for Ordered Responses 6.10.2 Scoring Function, Item Response Function, and Test Characteristic Curve 6.10.3 Parameter Estimation and Scale Transformation with Polytomous IRT Models 6.10.4 True Score Equating 6.10.5 Observed Score Equating 6.10.6 Example using the Graded Response Model 6.11 Practical Issues and Caveat 6.12 Exercises 7 Standard Errors of Equating 7.1 De.nition of Standard Error of Equating 7.2 The Bootstrap 7.2.1 Standard Errors Using the Bootstrap 7.2.2 Standard Errors of Equating 7.2.3 Parametric Bootstrap 7.2.4 Standard Errors of Smoothed Equipercentile Equating 7.2.5 Standard Errors of Scale Scores 7.2.6 Standard Errors of Equating Chains 7.2.7 Mean Standard Error of Equating 7.2.8 Caveat 7.3 The Delta Method 7.3.1 Mean Equating Using Single Group and Random Groups Designs 7.3.2 Linear Equating Using the Random Groups Design 7.3.3 Equipercentile Equating Using the Random Groups Design 7.3.4 Standard Errors for Other Designs 7.3.5 Approximations 7.3.6 Standard Errors for Scale Scores 7.3.7 Standard Errors of Equating Chains 7.3.8 Using Delta Method Standard Errors 7.4 Using Standard Errors in Practice 7.5 Exercises 8 Practical Issues in Equating 8.1 Equating and the Test Development Process 8.1.1 Test Speci.cations 8.1.2 Characteristics of Common-item Sets 8.1.3 Changes in Test Specifications 8.2 Data Collection: Design and Implementation 8.2.1 Choosing Among Equating Designs 8.2.2 Developing Equating Linkage Plans 8.2.3 Examinee Groups Used in Equating 8.2.4 Sample Size Requirements 8.3 Choosing From Among the Statistical Procedures 8.3.1 Equating Criteria in Research Studies 8.3.2 Characteristics of Equating Situations 8.4 Choosing From Among Equating Results 8.4.1 Equating Versus Not Equating 8.4.2 Use of Robustness Checks 8.4.3 Choosing From Among Results in the Random Groups Design 8.4.4 Choosing From Among Results in the Common-Item Nonequivalent Groups Design 8.4.5 Use of Consistency Checks 8.4.6 Equating and Score Scales 8.4.7 Assessing First and Second Order Equity for Scale Scores 8.5 Importance of Standardization Conditions and Quality Control 8.5.1 Test Development 8.5.2 Test Administration and Standardization Conditions 8.5.3 Quality Control 8.5.4 Reequating 8.6 Conditions Conducive to Satisfactory Equating 8.7 Comparability Issues in Special Circumstances 8.7.1 Comparability Issues with Computer-Based Tests 8.7.2 Comparability of Performance Assessments 8.7.3 Score Comparability with Optional Test Sections 8.8 Conclusion 8.9 Exercises 9 Score Scales 9.1 Scaling Perspectives 9.2 Score Transformations 9.3 Incorporating Normative Information 9.3.1 Linear Transformations 9.3.2 Nonlinear Transformations 9.3.3 Example: Normalized Scale Scores 9.3.4 Importance of Norm Group in Setting the Score Scale 9.4 Incorporating Score Precision Information 9.4.1 Rules of Thumb for Number of Distinct Score Points 9.4.2 Linearly Transformed Score Scales with a Given Standard Error of Measurement 9.4.3 Score Scales with Approximately Equal Conditional Standard Errors of Measurement 9.4.4 Example: Incorporating Score Precision 9.4.5 Evaluating Psychometric Properties of Scale Scores 9.4.6 The IRT e-Scale as a Score Scale 9.5 Incorporating Content Information 9.5.1 Item Mapping 9.5.2 Scale Anchoring 9.5.3 Standard Setting 9.5.4 Numerical Example 9.5.5 Practical Usefulness 9.6 Maintaining Score Scales 9.7 Scales for Test Batteries and Composites 9.7.1 Test Batteries 9.7.2 Composite Scores 9.7.3 Maintaining Scales for Batteries and Composites 9.8 Vertical Scaling and Developmental Score Scales 9.8.1 Structure of Batteries 9.8.2 Type of Domain Being Measured 9.8.3 Definition of Growth 9.8.4 Designs for Data Collection for Vertical Scaling 9.8.5 Test Scoring 9.8.6 Hieronymus Statistical Methods 9.8.7 Thurstone Statistical Methods 9.8.8 IRT Statistical Methods 9.8.9 Thurstone Illustrative Example 9.8.10 IRT Illustrative Example 9.8.11 Statistics for Comparing Scaling Results 9.8.12 Some Limitations of Vertically Scaled Tests 9.8.13 Research on Vertical Scaling 9.9 Exercises 10 Linking 10.1 Linking Categorization Schemes and Criteria 10.1.1 Types of Linking 10.1.2 Mislevy/Linn Taxonomy 10.1.3 Degrees of Similarity 10.2 Group Invariance 10.2.1 Statistical Methods Using Observed Scores 10.2.2 Statistics for Overall Group Invariance 10.2.3 Statistics for Pairwise Group Invariance 10.2.4 Example: ACT and ITED Science Tests 10.3 Additional Examples 10.3.1 Extended Time 10.3.2 Test Adaptations and Translated Tests 10.4 Discussion 10.5 Exercises 11 Current and Future Challenges 11.1 Score Scales 11.2 Equating 11.3 Vertical Scaling 11.4 Linking 11.5 Summary References Appendix A: Answers to Exercises Appendix B: Computer Programs Index

Test Equating, Scaling, and Linking: Methods and Practices

Observed score equating using the random groups design random groups - smoothing in equipercentile equating nonequivalent groups - linear methods nonequivalent groups - equipercentile methods item response theory methods standard errors of equating practical issues in equating in Scaling to achieve comparability. Appendices: answers to exercises computer programs and data sets.

Test Equating: Methods and Practices

This chapter describes the concepts of scaling, norming, and equating, as they pertain to assessment in higher education. Issues associated with scaling, norming, and equating in admissions testing, placement testing, and assessments used for outcomes and accountability are then discussed. Detailed examples of how scaling, norming, and equating procedures are used in the ACT (ACT, 2015a) and SAT (College Board, 2016c) admissions testing programs, and the Advanced Placement program are provided. A variety of scores and scales are used with educational tests. Raw scores can be as simple as a sum of the item scores, or so complicated that they depend on the entire pattern of item responses, as is sometimes the case with item response theory (IRT)-based scoring. Theoretical considerations for scoring, scaling, norming, and equating are described. Incorporating normative information begins with the administration of the test to a norm group. The implementation of test equating requires a process for collecting data, referred to as an equating design.

Michael J. Kolen

Papers

Test equating, scaling, and linking: Methods and practices, 3rd ed.

Test Equating, Scaling, and Linking

Test Equating, Scaling, and Linking: Methods and Practices

Test Equating: Methods and Practices

Scaling, Norming, and Equating