Statistical group comparison / / Tim Futing Liao

• Record Nr.: UNINA9911018903603321
• Autore: Liao Tim Futing
• Pubbl/distr/stampa: New York, : Wiley-Interscience, c2002
• ISBN: 9786613294845; 9781283294843; 1283294842; 9781118204214; 1118204212; 9781118150610; 1118150619
• Descrizione fisica: 1 online resource (240 p.)
• Collana: Wiley series in probability and statistics
• Disciplina: 519.5
• Soggetti: Mathematical statistics; Statistics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 199-206) and index.
• Nota di contenuto: Statistical Group Comparison; Contents; Preface; 1. Introduction; 1.1 Rationale for Statistical Comparison; 1.2 Comparative Research in the Social Sciences; 1.3 Focus of the Book; 1.4 Outline of the Book; 1.4.1 Chapter 2-Statistical Foundation for Comparison; 1.4.2 Chapter 3-Comparison in Linear Regression; 1.4.3 Chapter 4-Nonparametric Comparison; 1.4.4 Chapter 5-Comparing Rates; 1.4.5 Chapter 6-Comparison in Generalized Linear Models; 1.4.6 Chapter 7-Additional Topics of Comparison in Generalized Linear Models; 1.4.7 Chapter 8-Comparison in Structural Equation Modeling; 1.4.8 Chapter 9-Comparison with Categorical Latent Variables1.4.9 Chapter 10-Comparison in Multilevel Analysis; 1.4.10 Summary; 2. Statistical Foundation for Comparison; 2.1 A System for Statistical Comparison; 2.2 Test Statistics; 2.2.1 The x2 Test; 2.2.2 The t-Test; 2.2.3 The F-test; 2.2.4 The Likelihood Ratio Test; 2.2.5 The Wald Test; 2.2.6 The Lagrange Multiplier Test; 2.2.7 A Summary Comparison of LRT WT and LMT; 2.3 What to Compare?; 2.3.1 Comparing Distributions; 2.3.2 Comparing Data Structures; 2.3.3 Comparing ; Model Structures; 2.3.4 Comparing Model Parameters; 3. Comparison in Linear Models3.1 Introduction; 3.2 An Example; 3.3 Some Preliminary Considerations; 3.4 The Linear Model; 3.5 Comparing Two Means; 3.6 ANOVA; 3.7 Multiple Comparison Methods; 3.7.1 Least Significance Difference Test; 3.7.2 Tukey's Model; 3.7.3 Scheffé's Method; 3.7.4 Bonferroni's Method; 3.8 ANCOVA; 3.9 Multiple Linear Regression; 3.10 Regression Decomposition; 3.10.1 Rationale; 3.10.2 Algebraic Presentation; 3.10.3 Interpretation; 3.10.4 Extension to Multiple Regression; 3.11 Which Linear Method to Use?; 4. Nonparametric Comparison; 4.1 Nonparametic Tests; 4.1.1 Kolmogorov-Smirnov Two-Sample Test4.1.2 Mann-Whitney U-Test; 4.2 Resampling Methods; 4.2.1 Permutation Methods; 4.2.2 Bootstrapping Methods; 4.3 Relative Distribution Methods; 5. Comparison of Rates; 5.1 The Data; 5.2 Standardization; 5.2.1 Direct Standardization; 5.2.2 Indirect Standardization; 5.2.3 Model-Based Standardization; 5.3 Decomposition; 5.3.1 Arithmetic Decomposition; 5.3.2 Model-Based Decomposition; 6. Comparison in Generalized Linear Models; 6.1 Introduction; 6.1.1 The Exponential Family of Distributions; 6.1.2 The Link Function; 6.1.3 Maximum Likelihood Estimation; 6.2 Comparing Generalized Linear Models6.2.1 The Null Hypothesis; 6.2.2 Comparisons Using Likelihood Ratio Tests; 6.2.3 The Chow Test as a Special Case; 6.3 A Logit Model Example; 6.3.1 The Data; 6.3.2 The Model Comparison; 6.4 A Hazard Rate Model Example; 6.4.1 The Model; 6.4.2 The Data; 6.4.3 The Model Comparison; 6.A Data Used in Section 6.4; 7. Additional Topics of Comparison in Generalized Linear Models; 7.1 Introduction; 7.2 GLM for Matched Case-Control Studies; 7.2.1 The 1 : 1 Matched Study; 7.2.2 The 1 : m Design; 7.2.3 The n : m Design; 7.3 Dispersion Heterogeneity; 7.3.1 The Data; 7.3.2 Group Comparison with Heterogeneous Dispersion
• Sommario/riassunto: An incomparably useful examination of statistical methods for comparisonThe nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not