{"product_id":"the-analysis-of-covariance-and-alternatives-statistical-methods-for-experiments-quasi-experiments-and-single-case-studies-hardback-9780471748960","title":"The Analysis of Covariance and Alternatives; Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies (Hardback) 9780471748960","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eThe Analysis of Covariance and Alternatives\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eStatistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eBradley Huitema (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471748960, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 2 December 2011\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e688 pages, Tables: 0 B\u0026amp;W, 0 Color; Graphs: 51 B\u0026amp;W, 0 Color\u003cbr\u003e24.1 x 16.3 x 3.4 cm, 1.089 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eA complete guide to cutting-edge techniques and best practices for applying covariance analysis methods\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e   \u003cp\u003eThe \u003ci\u003eSecond Edition of Analysis of Covariance and Alternatives\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eThe 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 \u003ci\u003eSecond Edition\u003c\/i\u003e also features coverage of advanced methods including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eSimple and multiple analysis of covariance using both the Fisher approach and the general linear model approach\u003c\/li\u003e \u003cli\u003eMethods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments\u003c\/li\u003e \u003cli\u003ePower analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs\u003c\/li\u003e \u003cli\u003eMeasurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThoroughly 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003ePreface xv\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePART I BASIC EXPERIMENTAL DESIGN AND ANALYSIS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Review of Basic Statistical Methods 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction, 3\u003c\/p\u003e \u003cp\u003e1.2 Elementary Statistical Inference, 4\u003c\/p\u003e \u003cp\u003e1.3 Elementary Statistical Decision Theory, 7\u003c\/p\u003e \u003cp\u003e1.4 Effect Size, 10\u003c\/p\u003e \u003cp\u003e1.5 Measures of Association, 14\u003c\/p\u003e \u003cp\u003e1.6 A Practical Alternative to Effect Sizes and Measures of Association That Is Relevant to the Individual: \u003ci\u003ep\u003c\/i\u003e(\u003ci\u003eY\u003c\/i\u003eTx \u003ci\u003e\u0026gt; Y\u003c\/i\u003eControl), 17\u003c\/p\u003e \u003cp\u003e1.7 Generalization of Results, 19\u003c\/p\u003e \u003cp\u003e1.8 Control of Nuisance Variation, 20\u003c\/p\u003e \u003cp\u003e1.9 Software, 22\u003c\/p\u003e \u003cp\u003e1.10 Summary, 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Review of Simple Correlated Samples Designs and Associated Analyses 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction, 25\u003c\/p\u003e \u003cp\u003e2.2 Two-Level Correlated Samples Designs, 25\u003c\/p\u003e \u003cp\u003e2.3 Software, 32\u003c\/p\u003e \u003cp\u003e2.4 Summary, 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 ANOVA Basics for One-Factor Randomized Group, Randomized Block, and Repeated Measurement Designs 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction, 35\u003c\/p\u003e \u003cp\u003e3.2 One-Factor Randomized Group Design and Analysis, 35\u003c\/p\u003e \u003cp\u003e3.3 One-Factor Randomized Block Design and Analysis, 51\u003c\/p\u003e \u003cp\u003e3.4 One-Factor Repeated Measurement Design and Analysis, 56\u003c\/p\u003e \u003cp\u003e3.5 Summary, 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II ESSENTIALS OF REGRESSION ANALYSIS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Simple Linear Regression 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction, 63\u003c\/p\u003e \u003cp\u003e4.2 Comparison of Simple Regression and ANOVA, 63\u003c\/p\u003e \u003cp\u003e4.3 Regression Estimation, Inference, and Interpretation, 68\u003c\/p\u003e \u003cp\u003e4.4 Diagnostic Methods: Is the Model Apt?, 80\u003c\/p\u003e \u003cp\u003e4.5 Summary, 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Essentials of Multiple Linear Regression 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction, 85\u003c\/p\u003e \u003cp\u003e5.2 Multiple Regression: Two-Predictor Case, 86\u003c\/p\u003e \u003cp\u003e5.3 General Multiple Linear Regression: \u003ci\u003em\u003c\/i\u003e Predictors, 105\u003c\/p\u003e \u003cp\u003e5.4 Alternatives to OLS Regression, 115\u003c\/p\u003e \u003cp\u003e5.5 Summary, 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III ESSENTIALS OF SIMPLE AND MULTIPLE ANCOVA\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 One-Factor Analysis of Covariance 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction, 123\u003c\/p\u003e \u003cp\u003e6.2 Analysis of Covariance Model, 127\u003c\/p\u003e \u003cp\u003e6.3 Computation and Rationale, 128\u003c\/p\u003e \u003cp\u003e6.4 Adjusted Means, 133\u003c\/p\u003e \u003cp\u003e6.5 ANCOVA Example 1: Training Effects, 140\u003c\/p\u003e \u003cp\u003e6.6 Testing Homogeneity of Regression Slopes, 144\u003c\/p\u003e \u003cp\u003e6.7 ANCOVA Example 2: Sexual Activity Reduces Lifespan, 148\u003c\/p\u003e \u003cp\u003e6.8 Software, 150\u003c\/p\u003e \u003cp\u003e6.9 Summary, 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Analysis of Covariance Through Linear Regression 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction, 159\u003c\/p\u003e \u003cp\u003e7.2 Simple Analysis of Variance Through Linear Regression, 159\u003c\/p\u003e \u003cp\u003e7.3 Analysis of Covariance Through Linear Regression, 172\u003c\/p\u003e \u003cp\u003e7.4 Computation of Adjusted Means, 177\u003c\/p\u003e \u003cp\u003e7.5 Similarity of ANCOVA to Part and Partial Correlation Methods, 177\u003c\/p\u003e \u003cp\u003e7.6 Homogeneity of Regression Test Through General Linear Regression, 178\u003c\/p\u003e \u003cp\u003e7.7 Summary, 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Assumptions and Design Considerations 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction, 181\u003c\/p\u003e \u003cp\u003e8.2 Statistical Assumptions, 182\u003c\/p\u003e \u003cp\u003e8.3 Design and Data Issues Related to the Interpretation of ANCOVA, 200\u003c\/p\u003e \u003cp\u003e8.4 Summary, 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multiple Comparison Tests and Confidence Intervals 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction, 215\u003c\/p\u003e \u003cp\u003e9.2 Overview of Four Multiple Comparison Procedures, 215\u003c\/p\u003e \u003cp\u003e9.3 Tests on All Pairwise Comparisons: Fisher–Hayter, 216\u003c\/p\u003e \u003cp\u003e9.4 All Pairwise Simultaneous Confidence Intervals and Tests: Tukey–Kramer, 219\u003c\/p\u003e \u003cp\u003e9.5 Planned Pairwise and Complex Comparisons: Bonferroni, 222\u003c\/p\u003e \u003cp\u003e9.6 Any or All Comparisons: Scheff´e, 225\u003c\/p\u003e \u003cp\u003e9.7 Ignore Multiple Comparison Procedures?, 227\u003c\/p\u003e \u003cp\u003e9.8 Summary, 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multiple Covariance Analysis 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction, 229\u003c\/p\u003e \u003cp\u003e10.2 Multiple ANCOVA Through Multiple Regression, 232\u003c\/p\u003e \u003cp\u003e10.3 Testing Homogeneity of Regression Planes, 234\u003c\/p\u003e \u003cp\u003e10.4 Computation of Adjusted Means, 236\u003c\/p\u003e \u003cp\u003e10.5 Multiple Comparison Procedures for Multiple ANCOVA, 237\u003c\/p\u003e \u003cp\u003e10.6 Software: Multiple ANCOVA and Associated Tukey–Kramer Multiple Comparison Tests Using \u003ci\u003eMinitab\u003c\/i\u003e, 243\u003c\/p\u003e \u003cp\u003e10.7 Summary, 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV ALTERNATIVES FOR ASSUMPTION DEPARTURES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Johnson–Neyman and Picked-Points Solutions for Heterogeneous Regression 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction, 249\u003c\/p\u003e \u003cp\u003e11.2 J–N and PPA Methods for Two Groups, One Covariate, 251\u003c\/p\u003e \u003cp\u003e11.3 A Common Method That Should Be Avoided, 269\u003c\/p\u003e \u003cp\u003e11.4 Assumptions, 270\u003c\/p\u003e \u003cp\u003e11.5 Two Groups, Multiple Covariates, 272\u003c\/p\u003e \u003cp\u003e11.6 Multiple Groups, One Covariate, 277\u003c\/p\u003e \u003cp\u003e11.7 Any Number of Groups, Any Number of Covariates, 278\u003c\/p\u003e \u003cp\u003e11.8 Two-Factor Designs, 278\u003c\/p\u003e \u003cp\u003e11.9 Interpretation Problems, 279\u003c\/p\u003e \u003cp\u003e11.10 Multiple Dependent Variables, 281\u003c\/p\u003e \u003cp\u003e11.11 Nonlinear Johnson-Neyman Analysis, 282\u003c\/p\u003e \u003cp\u003e11.12 Correlated Samples, 282\u003c\/p\u003e \u003cp\u003e11.13 Robust Methods, 282\u003c\/p\u003e \u003cp\u003e11.14 Software, 283\u003c\/p\u003e \u003cp\u003e11.15 Summary, 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Nonlinear ANCOVA 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction, 285\u003c\/p\u003e \u003cp\u003e12.2 Dealing with Nonlinearity, 286\u003c\/p\u003e \u003cp\u003e12.3 Computation and Example of Fitting Polynomial Models, 288\u003c\/p\u003e \u003cp\u003e12.4 Summary, 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Quasi-ANCOVA: When Treatments Affect Covariates 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction, 297\u003c\/p\u003e \u003cp\u003e13.2 Quasi-ANCOVA Model, 298\u003c\/p\u003e \u003cp\u003e13.3 Computational Example of Quasi-ANCOVA, 300\u003c\/p\u003e \u003cp\u003e13.4 Multiple Quasi-ANCOVA, 304\u003c\/p\u003e \u003cp\u003e13.5 Computational Example of Multiple Quasi-ANCOVA, 304\u003c\/p\u003e \u003cp\u003e13.6 Summary, 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Robust ANCOVA\/Robust Picked Points 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction, 311\u003c\/p\u003e \u003cp\u003e14.2 Rank ANCOVA, 311\u003c\/p\u003e \u003cp\u003e14.3 Robust General Linear Model, 314\u003c\/p\u003e \u003cp\u003e14.4 Summary, 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 ANCOVA for Dichotomous Dependent Variables 321\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction, 321\u003c\/p\u003e \u003cp\u003e15.2 Logistic Regression, 323\u003c\/p\u003e \u003cp\u003e15.3 Logistic Model, 324\u003c\/p\u003e \u003cp\u003e15.4 Dichotomous ANCOVA Through Logistic Regression, 325\u003c\/p\u003e \u003cp\u003e15.5 Homogeneity of Within-Group Logistic Regression, 328\u003c\/p\u003e \u003cp\u003e15.6 Multiple Covariates, 328\u003c\/p\u003e \u003cp\u003e15.7 Multiple Comparison Tests, 330\u003c\/p\u003e \u003cp\u003e15.8 Continuous Versus Forced Dichotomy Results, 331\u003c\/p\u003e \u003cp\u003e15.9 Summary, 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Designs with Ordered Treatments and No Covariates 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction, 333\u003c\/p\u003e \u003cp\u003e16.2 Qualitative, Quantitative, and Ordered Treatment Levels, 333\u003c\/p\u003e \u003cp\u003e16.3 Parametric Monotone Analysis, 337\u003c\/p\u003e \u003cp\u003e16.4 Nonparametric Monotone Analysis, 346\u003c\/p\u003e \u003cp\u003e16.5 Reversed Ordinal Logistic Regression, 350\u003c\/p\u003e \u003cp\u003e16.6 Summary, 353\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 ANCOVA for Ordered Treatments Designs 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction, 355\u003c\/p\u003e \u003cp\u003e17.2 Generalization of the Abelson–Tukey Method to Include One Covariate, 355\u003c\/p\u003e \u003cp\u003e17.3 Abelson–Tukey: Multiple Covariates, 358\u003c\/p\u003e \u003cp\u003e17.4 Rank-Based ANCOVA Monotone Method, 359\u003c\/p\u003e \u003cp\u003e17.5 Rank-Based Monotone Method with Multiple Covariates, 362\u003c\/p\u003e \u003cp\u003e17.6 Reversed Ordinal Logistic Regression with One or More Covariates, 362\u003c\/p\u003e \u003cp\u003e17.7 Robust \u003ci\u003eR\u003c\/i\u003e-Estimate ANCOVA Monotone Method, 363\u003c\/p\u003e \u003cp\u003e17.8 Summary, 364\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V SINGLE-CASE DESIGNS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Simple Interrupted Time-Series Designs 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction, 367\u003c\/p\u003e \u003cp\u003e18.2 Logic of the Two-Phase Design, 370\u003c\/p\u003e \u003cp\u003e18.3 Analysis of the Two-Phase (AB) Design, 371\u003c\/p\u003e \u003cp\u003e18.4 Two Strategies for Time-Series Regression Intervention Analysis, 374\u003c\/p\u003e \u003cp\u003e18.5 Details of Strategy II, 375\u003c\/p\u003e \u003cp\u003e18.6 Effect Sizes, 385\u003c\/p\u003e \u003cp\u003e18.7 Sample Size Recommendations, 389\u003c\/p\u003e \u003cp\u003e18.8 When the Model Is Too Simple, 393\u003c\/p\u003e \u003cp\u003e18.9 Summary, 394\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Examples of Single-Case AB Analysis 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction, 403\u003c\/p\u003e \u003cp\u003e19.2 Example I: Cancer Death Rates in the United Kingdom, 403\u003c\/p\u003e \u003cp\u003e19.3 Example II: Functional Activity, 411\u003c\/p\u003e \u003cp\u003e19.4 Example III: Cereal Sales, 414\u003c\/p\u003e \u003cp\u003e19.5 Example IV: Paracetamol Poisoning, 424\u003c\/p\u003e \u003cp\u003e19.6 Summary, 430\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Analysis of Single-Case Reversal Designs 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction, 433\u003c\/p\u003e \u003cp\u003e20.2 Statistical Analysis of Reversal Designs, 434\u003c\/p\u003e \u003cp\u003e20.3 Computational Example: Pharmacy Wait Time, 441\u003c\/p\u003e \u003cp\u003e20.4 Summary, 452\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Analysis of Multiple-Baseline Designs 453\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction, 453\u003c\/p\u003e \u003cp\u003e21.2 Case I Analysis: Independence of Errors Within and Between Series, 455\u003c\/p\u003e \u003cp\u003e21.3 Case II Analysis: Autocorrelated Errors Within Series, Independence Between Series, 461\u003c\/p\u003e \u003cp\u003e21.4 Case III Analysis: Independent Errors Within Series, Cross-Correlation Between Series, 461\u003c\/p\u003e \u003cp\u003e21.5 Intervention Versus Control Series Design, 467\u003c\/p\u003e \u003cp\u003e21.6 Summary, 471\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VI ANCOVA EXTENSIONS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Power Estimation 475\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction, 475\u003c\/p\u003e \u003cp\u003e22.2 Power Estimation for One-Factor ANOVA, 475\u003c\/p\u003e \u003cp\u003e22.3 Power Estimation for ANCOVA, 480\u003c\/p\u003e \u003cp\u003e22.4 Power Estimation for Standardized Effect Sizes, 482\u003c\/p\u003e \u003cp\u003e22.5 Summary, 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 ANCOVA for Randomized-Block Designs 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction, 483\u003c\/p\u003e \u003cp\u003e23.2 Conventional Design and Analysis Example, 484\u003c\/p\u003e \u003cp\u003e23.3 Combined Analysis (ANCOVA and Blocking Factor), 486\u003c\/p\u003e \u003cp\u003e23.4 Summary, 488\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Two-Factor Designs 489\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction, 489\u003c\/p\u003e \u003cp\u003e24.2 ANCOVA Model and Computation for Two-Factor Designs, 494\u003c\/p\u003e \u003cp\u003e24.3 Multiple Comparison Tests for Adjusted Marginal Means, 512\u003c\/p\u003e \u003cp\u003e24.4 Two-Factor ANOVA and ANCOVA for Repeated-Measurement Designs, 519\u003c\/p\u003e \u003cp\u003e24.5 Summary, 530\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Randomized Pretest–Posttest Designs 531\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction, 531\u003c\/p\u003e \u003cp\u003e25.2 Comparison of Three ANOVA Methods, 531\u003c\/p\u003e \u003cp\u003e25.3 ANCOVA for Pretest–Posttest Designs, 534\u003c\/p\u003e \u003cp\u003e25.4 Summary, 539\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Multiple Dependent Variables 541\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction, 541\u003c\/p\u003e \u003cp\u003e26.2 Uncorrected Univariate ANCOVA, 543\u003c\/p\u003e \u003cp\u003e26.3 Bonferroni Method, 544\u003c\/p\u003e \u003cp\u003e26.4 Multivariate Analysis of Covariance (MANCOVA), 544\u003c\/p\u003e \u003cp\u003e26.5 MANCOVA Through Multiple Regression Analysis: Two Groups Only, 553\u003c\/p\u003e \u003cp\u003e26.6 Issues Associated with Bonferroni \u003ci\u003eF\u003c\/i\u003e and MANCOVA, 554\u003c\/p\u003e \u003cp\u003e26.7 Alternatives to Bonferroni and MANCOVA, 555\u003c\/p\u003e \u003cp\u003e26.8 Example Analyses Using \u003ci\u003eMinitab\u003c\/i\u003e, 557\u003c\/p\u003e \u003cp\u003e26.9 Summary, 564\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VII QUASI-EXPERIMENTS AND MISCONCEPTIONS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Nonrandomized Studies: Measurement Error Correction 567\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction, 567\u003c\/p\u003e \u003cp\u003e27.2 Effects of Measurement Error: Randomized-Group Case, 568\u003c\/p\u003e \u003cp\u003e27.3 Effects of Measurement Error in Exposure and Covariates: Nonrandomized Design, 569\u003c\/p\u003e \u003cp\u003e27.4 Measurement Error Correction Ideas, 570\u003c\/p\u003e \u003cp\u003e27.5 Summary, 573\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Design and Analysis of Observational Studies 575\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction, 575\u003c\/p\u003e \u003cp\u003e28.2 Design of Nonequivalent Group\/Observational Studies, 579\u003c\/p\u003e \u003cp\u003e28.3 Final (Outcome) Analysis, 587\u003c\/p\u003e \u003cp\u003e28.4 Propensity Design Advantages, 592\u003c\/p\u003e \u003cp\u003e28.5 Evaluations of ANCOVA Versus Propensity-Based Approaches, 594\u003c\/p\u003e \u003cp\u003e28.6 Adequacy of Observational Studies, 596\u003c\/p\u003e \u003cp\u003e28.7 Summary, 597\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Common ANCOVA Misconceptions 599\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction, 599\u003c\/p\u003e \u003cp\u003e29.2 SSAT Versus SSIntuitive AT: Single Covariate Case, 599\u003c\/p\u003e \u003cp\u003e29.3 SSAT Versus SSIntuitive AT: Multiple Covariate Case, 601\u003c\/p\u003e \u003cp\u003e29.4 ANCOVA Versus ANOVA on Residuals, 606\u003c\/p\u003e \u003cp\u003e29.5 ANCOVA Versus \u003ci\u003eY\u003c\/i\u003e\/\u003ci\u003eX\u003c\/i\u003e Ratio, 606\u003c\/p\u003e \u003cp\u003e29.6 Other Common Misconceptions, 607\u003c\/p\u003e \u003cp\u003e29.7 Summary, 608\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Uncontrolled Clinical Trials 609\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction, 609\u003c\/p\u003e \u003cp\u003e30.2 Internal Validity Threats Other Than Regression, 610\u003c\/p\u003e \u003cp\u003e30.3 Problems with Conventional Analyses, 613\u003c\/p\u003e \u003cp\u003e30.4 Controlling Regression Effects, 615\u003c\/p\u003e \u003cp\u003e30.5 Naranjo–Mckean Dual Effects Model, 616\u003c\/p\u003e \u003cp\u003e30.6 Summary, 617\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Statistical Tables 619\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 643\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 655\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52298050208024,"sku":"9780471748960","price":96.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471748960.jpg?v=1781732832","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/the-analysis-of-covariance-and-alternatives-statistical-methods-for-experiments-quasi-experiments-and-single-case-studies-hardback-9780471748960","provider":"Freshly Printed Books","version":"1.0","type":"link"}