05406nam 2200685Ia 450 991101890360332120200520144314.09786613294845978128329484312832948429781118204214111820421297811181506101118150619(CKB)2550000000056381(EBL)818802(OCoLC)757511765(SSID)ssj0000550644(PQKBManifestationID)11408562(PQKBTitleCode)TC0000550644(PQKBWorkID)10509510(PQKB)11062412(MiAaPQ)EBC818802(Perlego)2776389(EXLCZ)99255000000005638120020522d2002 uy 0engur|n|---|||||txtccrStatistical group comparison /Tim Futing LiaoNew York Wiley-Intersciencec20021 online resource (240 p.)Wiley series in probability and statisticsDescription based upon print version of record.9780471386469 0471386464 Includes bibliographical references (p. 199-206) and index.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 Modeling1.4.8 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 Parameters3. 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 Tests4.1.1 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 Estimation6.2 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 Data7.3.2 Group Comparison with Heterogeneous DispersionAn 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 Wiley series in probability and statistics.Mathematical statisticsStatisticsMathematical statistics.Statistics.519.5Liao Tim Futing103953MiAaPQMiAaPQMiAaPQBOOK9911018903603321Statistical group comparison4416196UNINA