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Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies 2nd edition [Kõva köide]

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(Western Michigan University)
  • Formaat: Hardback, 688 pages, kõrgus x laius x paksus: 241x163x34 mm, kaal: 1089 g, Tables: 0 B&W, 0 Color; Graphs: 51 B&W, 0 Color
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 02-Dec-2011
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 047174896X
  • ISBN-13: 9780471748960
Teised raamatud teemal:
Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies 2nd editionSuurem pilt
  • Formaat: Hardback, 688 pages, kõrgus x laius x paksus: 241x163x34 mm, kaal: 1089 g, Tables: 0 B&W, 0 Color; Graphs: 51 B&W, 0 Color
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 02-Dec-2011
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 047174896X
  • ISBN-13: 9780471748960
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471748960.html
Märksõnad:
A complete guide to cutting-edge techniques and best practices for applying covariance analysis methodsThe Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field.The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including:Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approachMethods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatmentsPower analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designsMeasurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trialsThoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.
Preface xv
PART I BASIC EXPERIMENTAL DESIGN AND ANALYSIS
1 Review of Basic Statistical Methods
3(22)
1.1 Introduction
3(1)
1.2 Elementary Statistical Inference
4(3)
1.3 Elementary Statistical Decision Theory
7(3)
1.4 Effect Size
10(4)
1.5 Measures of Association
14(3)
1.6 A Practical Alternative to Effect Sizes and Measures of Association That Is Relevant to the Individual: p(YTX > Y Control)
17(2)
1.7 Generalization of Results
19(1)
1.8 Control of Nuisance Variation
20(2)
1.9 Software
22(2)
1.10 Summary
24(1)
2 Review of Simple Correlated Samples Designs and Associated Analyses
25(10)
2.1 Introduction
25(1)
2.2 Two-Level Correlated Samples Designs
25(7)
2.3 Software
32(1)
2.4 Summary
32(3)
3 ANOVA Basics for One-Factor Randomized Group, Randomized Block, and Repeated Measurement Designs
35(28)
3.1 Introduction
35(1)
3.2 One-Factor Randomized Group Design and Analysis
35(16)
3.3 One-Factor Randomized Block Design and Analysis
51(5)
3.4 One-Factor Repeated Measurement Design and Analysis
56(4)
3.5 Summary
60(3)
PART II ESSENTIALS OF REGRESSION ANALYSIS
4 Simple Linear Regression
63(22)
4.1 Introduction
63(1)
4.2 Comparison of Simple Regression and ANOVA
63(5)
4.3 Regression Estimation, Inference, and Interpretation
68(12)
4.4 Diagnostic Methods: Is the Model Apt?
80(2)
4.5 Summary
82(3)
5 Essentials of Multiple Linear Regression
85(38)
5.1 Introduction
85(1)
5.2 Multiple Regression: Two-Predictor Case
86(19)
5.3 General Multiple Linear Regression: m Predictors
105(10)
5.4 Alternatives to OLS Regression
115(4)
5.5 Summary
119(4)
PART III ESSENTIALS OF SIMPLE AND MULTIPLE ANCOVA
6 One-Factor Analysis of Covariance
123(36)
6.1 Introduction
123(4)
6.2 Analysis of Covariance Model
127(1)
6.3 Computation and Rationale
128(5)
6.4 Adjusted Means
133(7)
6.5 ANCOVA Example 1: Training Effects
140(4)
6.6 Testing Homogeneity of Regression Slopes
144(4)
6.7 ANCOVA Example 2: Sexual Activity Reduces Lifespan
148(2)
6.8 Software
150(7)
6.9 Summary
157(2)
7 Analysis of Covariance Through Linear Regression
159(22)
7.1 Introduction
159(1)
7.2 Simple Analysis of Variance Through Linear Regression
159(13)
7.3 Analysis of Covariance Through Linear Regression
172(5)
7.4 Computation of Adjusted Means
177(1)
7.5 Similarity of ANCOVA to Part and Partial Correlation Methods
177(1)
7.6 Homogeneity of Regression Test Through General Linear Regression
178(1)
7.7 Summary
179(2)
8 Assumptions and Design Considerations
181(34)
8.1 Introduction
181(1)
8.2 Statistical Assumptions
182(18)
8.3 Design and Data Issues Related to the Interpretation of ANCOVA
200(13)
8.4 Summary
213(2)
9 Multiple Comparison Tests and Confidence Intervals
215(14)
9.1 Introduction
215(1)
9.2 Overview of Four Multiple Comparison Procedures
215(1)
9.3 Tests on All Pairwise Comparisons: Fisher-Hayter
216(3)
9.4 All Pairwise Simultaneous Confidence Intervals and Tests: Tukey-Kramer
219(3)
9.5 Planned Pairwise and Complex Comparisons: Bonferroni
222(3)
9.6 Any or All Comparisons: Scheffe
225(2)
9.7 Ignore Multiple Comparison Procedures?
227(1)
9.8 Summary
228(1)
10 Multiple Covariance Analysis
229(20)
10.1 Introduction
229(3)
10.2 Multiple ANCOVA Through Multiple Regression
232(2)
10.3 Testing Homogeneity of Regression Planes
234(2)
10.4 Computation of Adjusted Means
236(1)
10.5 Multiple Comparison Procedures for Multiple ANCOVA
237(6)
10.6 Software: Multiple ANCOVA and Associated Tukey-Kramer Multiple Comparison Tests Using Minitab
243(3)
10.7 Summary
246(3)
PART IV ALTERNATIVES FOR ASSUMPTION DEPARTURES
11 Johnson-Neyman and Picked-Points Solutions for Heterogeneous Regression
249(36)
11.1 Introduction
249(2)
11.2 J-N and PPA Methods for Two Groups, One Covariate
251(18)
11.3 A Common Method That Should Be Avoided
269(1)
11.4 Assumptions
270(2)
11.5 Two Groups, Multiple Covariates
272(5)
11.6 Multiple Groups, One Covariate
277(1)
11.7 Any Number of Groups, Any Number of Covariates
278(1)
11.8 Two-Factor Designs
278(1)
11.9 Interpretation Problems
279(2)
11.10 Multiple Dependent Variables
281(1)
11.1 J Nonlinear Johnson-Neyman Analysis
282(1)
11.12 Correlated Samples
282(1)
11.13 Robust Methods
282(1)
11.14 Software
283(1)
11.15 Summary
283(2)
12 Nonlinear ANCOVA
285(12)
12.1 Introduction
285(1)
12.2 Dealing with Nonlinearity
286(2)
12.3 Computation and Example of Fitting Polynomial Models
288(7)
12.4 Summary
295(2)
13 Quasi-ANCOVA: When Treatments Affect Covariates
297(14)
13.1 Introduction
297(1)
13.2 Quasi-ANCOVA Model
298(2)
13.3 Computational Example of Quasi-ANCOVA
300(4)
13.4 Multiple Quasi-ANCOVA
304(1)
13.5 Computational Example of Multiple Quasi-ANCOVA
304(4)
13.6 Summary
308(3)
14 Robust ANCOVA/Robust Picked Points
311(10)
14.1 Introduction
311(1)
14.2 Rank ANCOVA
311(3)
14.3 Robust General Linear Model
314(6)
14.4 Summary
320(1)
15 ANCOVA for Dichotomous Dependent Variables
321(12)
15.1 Introduction
321(2)
15.2 Logistic Regression
323(1)
15.3 Logistic Model
324(1)
15.4 Dichotomous ANCOVA Through Logistic Regression
325(3)
15.5 Homogeneity of Within-Group Logistic Regression
328(1)
15.6 Multiple Covariates
328(2)
15.7 Multiple Comparison Tests
330(1)
15.8 Continuous Versus Forced Dichotomy Results
331(1)
15.9 Summary
331(2)
16 Designs with Ordered Treatments and No Covariates
333(22)
16.1 Introduction
333(1)
16.2 Qualitative, Quantitative, and Ordered Treatment Levels
333(4)
16.3 Parametric Monotone Analysis
337(9)
16.4 Nonparametric Monotone Analysis
346(4)
16.5 Reversed Ordinal Logistic Regression
350(3)
16.6 Summary
353(2)
17 ANCOVA for Ordered Treatments Designs
355(12)
17.1 Introduction
355(1)
17.2 Generalization of the Abelson-Tukey Method to Include One Covariate
355(3)
17.3 Abelson-Tukey: Multiple Covariates
358(1)
17.4 Rank-Based ANCOVA Monotone Method
359(3)
17.5 Rank-Based Monotone Method with Multiple Covariates
362(1)
17.6 Reversed Ordinal Logistic Regression with One or More Covariates
362(1)
17.7 Robust R-Estimate ANCOVA Monotone Method
363(1)
17.8 Summary
364(3)
PART V SINGLE-CASE DESIGNS
18 Simple Interrupted Time-Series Designs
367(36)
18.1 Introduction
367(3)
18.2 Logic of the Two-Phase Design
370(1)
18.3 Analysis of the Two-Phase (AB) Design
371(3)
18.4 Two Strategies for Time-Series Regression Intervention Analysis
374(1)
18.5 Details of Strategy II
375(10)
18.6 Effect Sizes
385(4)
18.7 Sample Size Recommendations
389(4)
18.8 When the Model Is Too Simple
393(1)
18.9 Summary
394(9)
19 Examples of Single-Case AB Analysis
403(30)
19.1 Introduction
403(1)
19.2 Example I: Cancer Death Rates in the United Kingdom
403(8)
19.3 Example II: Functional Activity
411(3)
19.4 Example III: Cereal Sales
414(10)
19.5 Example IV: Paracetamol Poisoning
424(6)
19.6 Summary
430(3)
20 Analysis of Single-Case Reversal Designs
433(20)
20.1 Introduction
433(1)
20.2 Statistical Analysis of Reversal Designs
434(7)
20.3 Computational Example: Pharmacy Wait Time
441(11)
20.4 Summary
452(1)
21 Analysis of Multiple-Baseline Designs
453(22)
21.1 Introduction
453(2)
21.2 Case I Analysis: Independence of Errors Within and Between Series
455(6)
21.3 Case II Analysis: Autocorrelated Errors Within Series, Independence Between Series
461(1)
21.4 Case III Analysis: Independent Errors Within Series, Cross-Correlation Between Series
461(6)
21.5 Intervention Versus Control Series Design
467(4)
21.6 Summary
471(4)
PART VI ANCOVA EXTENSIONS
22 Power Estimation
475(8)
22.1 Introduction
475(1)
22.2 Power Estimation for One-Factor ANOVA
475(5)
22.3 Power Estimation for ANCOVA
480(2)
22.4 Power Estimation for Standardized Effect Sizes
482(1)
22.5 Summary
482(1)
23 ANCOVA for Randomized-Block Designs
483(6)
23.1 Introduction
483(1)
23.2 Conventional Design and Analysis Example
484(2)
23.3 Combined Analysis (ANCOVA and Blocking Factor)
486(2)
23.4 Summary
488(1)
24 Two-Factor Designs
489(42)
24.1 Introduction
489(5)
24.2 ANCOVA Model and Computation for Two-Factor Designs
494(18)
24.3 Multiple Comparison Tests for Adjusted Marginal Means
512(7)
24.4 Two-Factor ANOVA and ANCOVA for Repeated-Measurement Designs
519(11)
24.5 Summary
530(1)
25 Randomized Pretest-Posttest Designs
531(10)
25.1 Introduction
531(1)
25.2 Comparison of Three ANOVA Methods
531(3)
25.3 ANCOVA for Pretest-Posttest Designs
534(5)
25.4 Summary
539(2)
26 Multiple Dependent Variables
541(26)
26.1 Introduction
541(2)
26.2 Uncorrected Univariate ANCOVA
543(1)
26.3 Bonferroni Method
544(1)
26.4 Multivariate Analysis of Covariance (MANCOVA)
544(9)
26.5 MANCOVA Through Multiple Regression Analysis: Two Groups Only
553(1)
26.6 Issues Associated with Bonferroni F and MANCOVA
554(1)
26.7 Alternatives to Bonferroni and MANCOVA
555(2)
26.8 Example Analyses Using Minitab
557(7)
26.9 Summary
564(3)
PART VII QUASI-EXPERIMENTS AND MISCONCEPTIONS
27 Nonrandomized Studies: Measurement Error Correction
567(8)
27.1 Introduction
567(1)
27.2 Effects of Measurement Error: Randomized-Group Case
568(1)
27.3 Effects of Measurement Error in Exposure and Covariates: Nonrandomized Design
569(1)
27.4 Measurement Error Correction Ideas
570(3)
27.5 Summary
573(2)
28 Design and Analysis of Observational Studies
575(24)
28.1 Introduction
575(4)
28.2 Design of Nonequivalent Group/Observational Studies
579(8)
28.3 Final (Outcome) Analysis
587(5)
28.4 Propensity Design Advantages
592(2)
28.5 Evaluations of ANCOVA Versus Propensity-Based Approaches
594(2)
28.6 Adequacy of Observational Studies
596(1)
28.7 Summary
597(2)
29 Common ANCOVA Misconceptions
599(10)
29.1 Introduction
599(1)
29.2 SSAT Versus SSINTUITIVE at' Single Covariate Case
599(2)
29.3 SSAT Versus SSINTUITIVE at: Multiple Covariate Case
601(5)
29.4 ANCOVA Versus ANOVA on Residuals
606(1)
29.5 ANCOVA Versus Y/X Ratio
606(1)
29.6 Other Common Misconceptions
607(1)
29.7 Summary
608(1)
30 Uncontrolled Clinical Trials
609(10)
30.1 Introduction
609(1)
30.2 Internal Validity Threats Other Than Regression
610(3)
30.3 Problems with Conventional Analyses
613(2)
30.4 Controlling Regression Effects
615(1)
30.5 Naranjo-Mckean Dual Effects Model
616(1)
30.6 Summary
617(2)
Appendix: Statistical Tables 619(24)
References 643(12)
Index 655
Bradley E. Huitema, PhD, is Professor of Psychology in the Industrial/Organizational Program at Western Michigan University. He also serves as a statistical consultant in the behavioral sciences for Western Michigan University and Children's Memorial Hospital, the pediatric training center of the Northwestern University Feinberg School of Medicine. Dr. Huitema has published extensively in his areas of research interest, which include applied time series analysis, single-case and quasi-experimental design, and the evaluation of health practices.