LEADER 05291nam 2200625Ia 450 001 9910830017203321 005 20230607223612.0 010 $a1-283-29484-2 010 $a9786613294845 010 $a1-118-20421-2 010 $a1-118-15061-9 035 $a(CKB)2550000000056381 035 $a(EBL)818802 035 $a(OCoLC)757511765 035 $a(SSID)ssj0000550644 035 $a(PQKBManifestationID)11408562 035 $a(PQKBTitleCode)TC0000550644 035 $a(PQKBWorkID)10509510 035 $a(PQKB)11062412 035 $a(MiAaPQ)EBC818802 035 $a(EXLCZ)992550000000056381 100 $a20020522d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical group comparison$b[electronic resource] /$fTim Futing Liao 210 $aNew York $cWiley-Interscience$dc2002 215 $a1 online resource (240 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-38646-4 320 $aIncludes bibliographical references (p. 199-206) and index. 327 $aStatistical 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 327 $a1.4.8 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 327 $a3. 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 Scheffe?'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 327 $a4.1.1 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 327 $a6.2 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 327 $a7.3.2 Group Comparison with Heterogeneous Dispersion 330 $aAn incomparably useful examination of statistical methods for comparisonThe nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not 410 0$aWiley series in probability and statistics. 606 $aMathematical statistics 606 $aStatistics 615 0$aMathematical statistics. 615 0$aStatistics. 676 $a519.5 700 $aLiao$b Tim Futing$0103953 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830017203321 996 $aStatistical group comparison$94040267 997 $aUNINA