scispace - formally typeset
Search or ask a question
Topic

Differential item functioning

About: Differential item functioning is a research topic. Over the lifetime, 4073 publications have been published within this topic receiving 123626 citations.


Papers
More filters
Book
01 Jul 1980
TL;DR: The application of item response theory to practical testing problems is discussed in this article, where the authors present an example of the application of the theory to real-world testing problems in a practical setting.
Abstract: Applications of Item response theory to practical testing problems , Applications of Item response theory to practical testing problems , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

4,701 citations

Book
01 Jul 1999
TL;DR: In this article, the authors introduce the concept of a scale and test homogeneity, reliability, and generalizability for total test scores, and propose a scaling theory for test scores.
Abstract: Contents: General Introduction. Items and Item Scores. Item and Test Statistics. The Concept of a Scale. Reliability Theory for Total Test Scores. Test Homogeneity, Reliability, and Generalizability. Reliability--Applications. Prediction and Multiple Regression. The Common Factor Model. Validity. Classical Item Analysis. Item Response Models. Properties of Item Response Models. Multidimensional Item Response Models. Comparing Populations. Alternate Forms and the Problem of Equating. An Introduction to Structural Equation Modeling. Some Scaling Theory. Retrospective. Appendix: Some Rules for Expected Values.

2,928 citations

Book
23 Jul 1991
TL;DR: This research attacked the mode-based approach to item response theory with a model- data fit approach, and found that the model-Data Fit approach proved to be more accurate than the other approaches.
Abstract: Background Concepts, Models, and Features Ability and Item Parameter Estimation Assessment of Model-Data Fit The Ability Scale Item and Test Information and Efficiency Functions Test Construction Identification of Potentially Biased Test Items Test Score Equating Computerized Adaptive Testing Future Directions of Item Response Theory

2,583 citations

Journal ArticleDOI
TL;DR: The Em procedure is shown to apply to general item-response models lacking simple sufficient statistics for ability, including models with more than one latent dimension, when computing procedures based on an EM algorithm are used.
Abstract: Maximum likelihood estimation of item parameters in the marginal distribution, integrating over the distribution of ability, becomes practical when computing procedures based on an EM algorithm are used By characterizing the ability distribution empirically, arbitrary assumptions about its form are avoided The Em procedure is shown to apply to general item-response models lacking simple sufficient statistics for ability This includes models with more than one latent dimension

2,137 citations

Book
01 Jan 2007
TL;DR: The aim of this second edition is to provide a history of quality of life research in the context of clinical trials and to clarify the role of factor analysis in this research.
Abstract: Preface to the first edition. Preface to the second edition. List of abbreviations. Part A. Introduction. 1. Introduction. 1.1 Patient-reported outcomes? 1.2 What is quality of life? 1.3 Historical development. 1.4 Why measure quality of life? 1.5 Which clinical trials should assess quality of life? 1.6 How to measure quality of life. 1.7 Instruments. 1.8 Conclusions. 2. Principles of measurement scales. 2.1 Introduction. 2.2 Scales and items. 2.3 Constructs and latent variables. 2.4 Indicator variables and causal variables. 2.5 Single global questions versus multi-item scales. 2.6 Single-item versus multi-item scales. 2.7 Psychometrics and item response theory. 2.8 Psychometric versus clinimetric scales. 2.9 Sufficient causes and necessary causes. 2.10 Discriminative, evaluative and predictive instruments. 2.11 Measuring quality of life: indicator or causal items? 2.12 Conclusions. Part B. Developing and Testing Questionnaires. 3. Developing a questionnaire. 3.1 Introduction. 3.2 General issues. 3.3 Defining the target population. 3.4 Item generation. 3.5 Qualitative methods. 3.6 Forming scales. 3.7 Multi-item scales. 3.8 Wording of questions. 3.9 Face and content validity of the proposed questionnaire. 3.10 Pre-testing the questionnaire. 3.11 Strategies for validation. 3.12 Translation. 3.13 Field testing. 3.14 Conclusions. 3.15 Further reading. 4. Scores and measurements: validity, reliability, sensitivity. 4.1 Introduction. 4.2 Content validity. 4.3 Criterion validity. 4.4 Construct validity. 4.5 Reliability. 4.6 Sensitivity and responsiveness. 4.7 Conclusions. 5. Multi-item scales. 5.1 Introduction. 5.2 Significance tests. 5.3 Correlations. 5.4 Construct validity. 5.5 Cronbach's &alpha and internal consistency. 5.6 Implications for causal items. 5.7 Conclusions. 6. Factor analysis and structural equation modelling. 6.1 Introduction. 6.2 Correlation patterns. 6.3 Path diagrams. 6.4 Factor analysis. 6.5 Factor analysis of the HADS questionnaire. 6.6 Uses of factor analysis. 6.7 Applying factor analysis: choices and decisions. 6.8 Assumptions for factor analysis. 6.9 Factor analysis in QoL research. 6.10 Limitations of correlation-based analysis. 6.11 Causal models. 6.12 Confirmatory factor analysis and structural equation modelling. 6.13 Conclusions. 6.14 Further reading and software. 7. Item response theory and differential item functioning. 7.1 Introduction. 7.2 Item characteristic curves . 7.3 Logistic models. 7.4 Fitting item response theory models: tips. 7.5 Test design. 7.6 IRT versus traditional and Guttman scales. 7.7 Polytomous item response theory models. 7.8 Differential item functioning. 7.9 Quantifying differential item functioning. 7.10 Exploring differential item functioning: tips. 7.11 Conclusions. 7.12 Further reading and software. 8. Item banks, item listing and computer-adaptive tests. 8.1 Introduction. 8.2 Item bank. 8.3 Item calibration. 8.4 Item linking and test equating. 8.5 Test information. 8.6 Computer-adaptive testing. 8.7 Stopping rules and simulations. 8.8 Computer-adaptive testing software. 8.9 Unresolved issues. 8.10 Computer-assisted tests. 8.11 Conclusions. 8.12 Further reading. Part C. Clinical Trials. 9. Choosing and scoring questionnaires. 9.1 Introduction. 9.2 Generic versus specific. 9.3 Finding instruments. 9.4 Choice of instrument. 9.5 Adding ad-hoc items. 9.6 Scoring multi-item scales. 9.7 Conclusions. 9.8 Further reading. 10. Clinical trials. 10.1 Introduction. 10.2 Basic design issues. 10.3 Compliance. 10.4 Administering a quality-of-life assessment. 10.5 Recommendations for writing protocols. 10.6 Standard operating procedures. 10.7 Summary and checklist. 11. Sample sizes. 11.1 Introduction. 11.2 Significance tests, p-values and power. 11.3 Estimating sample size. 11.4 Comparing two groups. 11.5 Comparison with a reference population. 11.6 Equivalence studies. 11.7 Choice of sample size method. 11.8 Multiple endpoints. 11.9 Specifying the target difference. 11.10 Sample size estimation is pre-study. 11.11 Attrition. 11.12 Conclusion. 11.13 Further reading. Part D. Analysis of QoL Data. 12. Cross-sectional analysis. 12.1 Types of data. 12.2 Comparing two groups. 12.3 Adjusting for covariates. 12.4 Changes from baseline. 12.5 Analysis of variance. 12.6 Analysis of variance models. 12.7 Graphical summaries. 12.8 Endpoints. 12.9 Conclusions. 13. Exploring longitudinal data. 13.1 Area under the curve. 13.2 Graphical presentations. 13.3 Tabular presentations. 13.4 Reporting. 13.5 Conclusions. 14. Modelling longitudinal data. 14.1 Preliminaries. 14.2 Auto-correlation. 14.3 Repeated measures. 14.4 Other situations. 14.5 Modelling versus area under the curve. 14.6 Conclusions. 15. Missing data. 15.1 Introduction. 15.2 Types of missing data. 15.3 Why do missing data matter? 15.4 Missing items. 15.5 Methods for missing items within a form. 15.6 Missing forms. 15.7 Methods for missing forms. 15.8 Comments. 15.9 Degrees of freedom. 15.10 Sensitivity analysis. 15.11 Conclusions. 15.12 Further reading. 16. Practical and reporting issues. 16.1 Introduction. 16.2 The reporting of design issues. 16.3 Data analysis. 16.4 Elements of good graphics. 16.5 Some errors. 16.6 Guidelines for reporting. 16.7 Further reading . Part E. Beyond Clinical Trials. 17. Quality-adjusted survival. 17.1 Introduction. 17.2 Preferences and utilities. 17.3 Multi-attribute utility measures. 17.4 Utility-based instruments. 17.5 Quality-adjusted life years. 17.6 Q-TWiST. 17.7 Sensitivity analysis. 17.8 Prognosis and variation with time. 17.9 Healthy-years equivalent. 17.10 Conclusions. 18. Clinical interpretation. 18.1 Introduction. 18.2 Statistical significance. 18.3 Absolute levels and changes over time. 18.4 Threshold values: percentages. 18.5 Population norms. 18.6 Minimal clinically important difference. 18.7 Impact of state of quality of life. 18.8 Changes in relation to life events. 18.9 Effect size. 18.10 Effect sizes and meta-analysis. 18.11 Patient variability. 18.12 Number needed to treat. 18.13 Conclusions. 18.14 Further reading. 19. Meta-analysis. 19.1 Introduction. 19.2 Defining objectives. 19.3 Defining outcomes. 19.4 Literature searching. 19.5 Assessing quality. 19.6 Summarising results. 19.7 Measures of treatment effect. 19.8 Combining studies. 19.9 Forest plot. 19.10 Heterogeneity. 19.11 Publication bias and funnel plots. 19.12 Conclusions. 19.13 Further reading. Appendix Examples of Instruments. Generic instruments. [L, flush left]E1 Sickness Impact Profile (SIP). E2 Nottingham Health Profile (NHP). E3 Health Survey Standard Version (SF-36v2). E4 EuroQoL (EQ-5D). E5 Patient Generated Index (PGI). Disease-specific instruments. E6 European Organisation for Research and Treatment of Cancer (EORTC QLQ-C30). E7 EORTC Head and Neck Module (EORTC H&N35). E8 Functional Assessment of Cancer - General version (FACT-G). E9 Rotterdam Symptom Checklist (RSCL). E10 Quality of Life in Epilepsy (QOLIE-89). E11 Paediatric Asthma Quality of Life Questionnaire (PAQLQ). Domain-specific instruments. E12 Hospital Anxiety and Depression Scale (HADS). E13 Short Form McGill Pain Questionnaire (SF-MPQ). E14 Multidimensional Fatigue Inventory (MFI-20). ADL and disability. E15 Barthel Index of disability (modified) (BI). Statistical tables. T1 Normal distribution. T2 Normal distribution - percentage points. T3 t-distribution. T4 ?2 distribution. T5 F-distribution. References. Index.

1,743 citations


Network Information
Related Topics (5)
Qualitative research
39.9K papers, 2.3M citations
76% related
Psychological intervention
82.6K papers, 2.6M citations
75% related
Coping (psychology)
48.1K papers, 1.6M citations
75% related
Psychosocial
66.7K papers, 2M citations
74% related
Anxiety
141.1K papers, 4.7M citations
74% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023149
2022264
2021283
2020216
2019223
2018193