| Autore |
Denis Daniel J. <1974->
|
| Edizione | [Second edition.] |
| Pubbl/distr/stampa |
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
|
| Descrizione fisica |
1 online resource (576 pages) : illustrations
|
| Disciplina |
519.53
|
| Soggetto topico |
Analysis of variance
Multivariate analysis
|
| Soggetto genere / forma |
Electronic books.
|
| ISBN |
1-119-58301-2
1-119-58300-4
1-119-58302-0
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
COVER -- TITLE PAGE -- COPYRIGHT PAGE -- CONTENTS -- PREFACE -- ABOUT THE COMPANION WEBSITE -- CHAPTER 1 PRELIMINARY CONSIDERATIONS -- 1.1 THE PHILOSOPHICAL BASES OF KNOWLEDGE: RATIONALISTIC VERSUS EMPIRICIST PURSUITS -- 1.2 WHAT IS A "MODEL"? -- 1.3 SOCIAL SCIENCES VERSUS HARD SCIENCES -- 1.4 IS COMPLEXITY A GOOD DEPICTION OF REALITY? ARE MULTIVARIATE METHODS USEFUL? -- 1.5 CAUSALITY -- 1.6 THE NATURE OF MATHEMATICS: MATHEMATICS AS A REPRESENTATION OF CONCEPTS -- 1.7 AS A SCIENTIST, HOW MUCH MATHEMATICS DO YOU NEED TO KNOW? -- 1.8 STATISTICS AND RELATIVITY -- 1.9 EXPERIMENTAL VERSUS STATISTICAL CONTROL -- 1.10 STATISTICAL VERSUS PHYSICAL EFFECTS -- 1.11 UNDERSTANDING WHAT "APPLIED STATISTICS" MEANS -- REVIEW EXERCISES -- FURTHER DISCUSSION AND ACTIVITIES -- CHAPTER 2 INTRODUCTORY STATISTICS -- 2.1 DENSITIES AND DISTRIBUTIONS -- 2.1.1 Plotting Normal Distributions -- 2.1.2 Binomial Distributions -- 2.1.3 Normal Approximation -- 2.1.4 Joint Probability Densities: Bivariate and Multivariate Distributions -- 2.2 CHI-SQUARE DISTRIBUTIONS AND GOODNESS-OF-FIT TEST -- 2.2.1 Power for Chi-Square Test of Independence -- 2.3 SENSITIVITY AND SPECIFICITY -- 2.4 SCALES OF MEASUREMENT: NOMINAL, ORDINAL, INTERVAL, RATIO -- 2.4.1 Nominal Scale -- 2.4.2 Ordinal Scale -- 2.4.3 Interval Scale -- 2.4.4 Ratio Scale -- 2.5 MATHEMATICAL VARIABLES VERSUS RANDOM VARIABLES -- 2.6 MOMENTS AND EXPECTATIONS -- 2.6.1 Sample and Population Mean Vectors -- 2.7 ESTIMATION AND ESTIMATORS -- 2.8 VARIANCE -- 2.9 DEGREES OF FREEDOM -- 2.10 SKEWNESS AND KURTOSIS -- 2.11 SAMPLING DISTRIBUTIONS -- 2.11.1 Sampling Distribution of the Mean -- 2.12 CENTRAL LIMIT THEOREM -- 2.13 CONFIDENCE INTERVALS -- 2.14 MAXIMUM LIKELIHOOD -- 2.15 AKAIKE'S INFORMATION CRITERIA -- 2.16 COVARIANCE AND CORRELATION -- 2.17 PSYCHOMETRIC VALIDITY, RELIABILITY: A COMMON USE OF CORRELATION COEFFICIENTS.
2.18 COVARIANCE AND CORRELATION MATRICES -- 2.19 OTHER CORRELATION COEFFICIENTS -- 2.20 STUDENT'S t DISTRIBUTION -- 2.20.1 t-Tests for One Sample -- 2.20.2 t-Tests for Two Samples -- 2.20.3 Two-Sample t-Tests in R -- 2.21 STATISTICAL POWER -- 2.21.1 Visualizing Power -- 2.22 POWER ESTIMATION USING R AND G*POWER -- 2.22.1 Estimating Sample Size and Power for Independent Samples t-Test -- 2.23 PAIRED-SAMPLES t-TEST: STATISTICAL TEST FOR MATCHED-PAIRS (ELEMENTARY BLOCKING) DESIGNS -- 2.24 BLOCKING WITH SEVERAL CONDITIONS -- 2.25 COMPOSITE VARIABLES: LINEAR COMBINATIONS -- 2.26 MODELS IN MATRIX FORM -- 2.27 GRAPHICAL APPROACHES -- 2.27.1 Box-and-Whisker Plots -- 2.28 WHAT MAKES A p-VALUE SMALL? A CRITICAL OVERVIEW AND PRACTICAL DEMONSTRATION OF NULL HYPOTHESIS SIGNIFICANCE TESTING -- 2.28.1 Null Hypothesis Significance Testing (NHST): A Legacy of Criticism -- 2.28.2 The Make-Up of a p-Value: A Brief Recap and Summary -- 2.28.3 The Issue of Standardized Testing: Are Students in Your School Achieving More Than the National Average? -- 2.28.4 Other Test Statistics -- 2.28.5 The Solution -- 2.28.6 Statistical Distance: Cohen's d -- 2.28.7 What Does Cohen's d Actually Tell Us? -- 2.28.8 Why and Where the Significance Test Still Makes Sense -- 2.29 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- FURTHER DISCUSSION AND ACTIVITIES -- CHAPTER 3 ANALYSIS OF VARIANCE: FIXED EFFECTS MODELS -- 3.1 WHAT IS ANALYSIS OF VARIANCE? FIXED VERSUS RANDOM EFFECTS -- 3.1.1 Small Sample Example: Achievement as a Function of Teacher -- 3.1.2 Is Achievement a Function of Teacher? -- 3.2 HOW ANALYSIS OF VARIANCE WORKS: A BIG PICTURE OVERVIEW -- 3.2.1 Is the Observed Difference Likely? ANOVA as a Comparison (Ratio) of Variances -- 3.3 LOGIC AND THEORY OF ANOVA: A DEEPER LOOK -- 3.3.1 Independent-Samples t-Tests Versus Analysis of Variance.
3.3.2 The ANOVA Model: Explaining Variation -- 3.3.3 Breaking Down a Deviation -- 3.3.4 Naming the Deviations -- 3.3.5 The Sums of Squares of ANOVA -- 3.4 FROM SUMS OF SQUARES TO UNBIASED VARIANCE ESTIMATORS: DIVIDING BY DEGREES OF FREEDOM -- 3.5 EXPECTED MEAN SQUARES FOR ONE-WAY FIXED EFFECTS MODEL: DERIVING THE F-RATIO -- 3.6 THE NULL HYPOTHESIS IN ANOVA -- 3.7 FIXED EFFECTS ANOVA: MODEL ASSUMPTIONS -- 3.8 A WORD ON EXPERIMENTAL DESIGN AND RANDOMIZATION -- 3.9 A PREVIEW OF THE CONCEPT OF NESTING -- 3.10 BALANCED VERSUS UNBALANCED DATA IN ANOVA MODELS -- 3.11 MEASURES OF ASSOCIATION AND EFFECT SIZE IN ANOVA: MEASURES OF VARIANCE EXPLAINED -- 3.11.1 .2 Eta-Squared -- 3.11.2 Omega-Squared -- 3.12 THE F-TEST AND THE INDEPENDENT SAMPLES t-TEST -- 3.13 CONTRASTS AND POST-HOCS -- 3.13.1 Independence of Contrasts -- 3.13.2 Independent Samples t-Test as a Linear Contrast -- 3.14 POST-HOC TESTS -- 3.14.1 Newman-Keuls and Tukey HSD -- 3.14.2 Tukey HSD -- 3.14.3 Scheffé Test -- 3.14.4 Other Post-Hoc Tests -- 3.14.5 Contrast versus Post-Hoc? Which Should I Be Doing? -- 3.15 SAMPLE SIZE AND POWER FOR ANOVA: ESTIMATION WITH R AND G*POWER -- 3.15.1 Power for ANOVA in R and G*Power -- 3.15.2 Computing f -- 3.16 FIXED EFFECTS ONE-WAY ANALYSIS OF VARIANCE IN R: MATHEMATICS ACHIEVEMENT AS A FUNCTION OF TEACHER -- 3.16.1 Evaluating Assumptions -- 3.16.2 Post-Hoc Tests on Teacher -- 3.17 ANALYSIS OF VARIANCE VIA R´s lm -- 3.18 KRUSKAL-WALLIS TEST IN R AND THE MOTIVATION BEHIND NONPARAMETRIC TESTS -- 3.19 ANOVA IN SPSS: ACHIEVEMENT AS A FUNCTION OF TEACHER -- 3.20 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- FURTHER DISCUSSION AND ACTIVITIES -- CHAPTER 4 FACTORIAL ANALYSIS OF VARIANCE -- 4.1 WHAT IS FACTORIAL ANALYSIS OF VARIANCE? -- 4.2 THEORY OF FACTORIAL ANOVA: A DEEPER LOOK -- 4.2.1 Deriving the Model for Two-Way Factorial ANOVA -- 4.2.2 Cell Effects.
4.2.3 Interaction Effects -- 4.2.4 Cell Effects Versus Interaction Effects -- 4.2.5 A Model for the Two-Way Fixed Effects ANOVA -- 4.3 COMPARING ONE-WAY ANOVA TO TWO-WAY ANOVA: CELL EFFECTS IN FACTORIAL ANOVA VERSUS SAMPLE EFFECTS IN ONE-WAY ANOVA -- 4.4 PARTITIONING THE SUMS OF SQUARES FOR FACTORIAL ANOVA: THE CASE OF TWO FACTORS -- 4.4.1 SS Total: A Measure of Total Variation -- 4.4.2 Model Assumptions: Two-Way Factorial Model -- 4.4.3 Expected Mean Squares for Factorial Design -- 4.4.4 Recap of Expected Mean Squares -- 4.5 INTERPRETING MAIN EFFECTS IN THE PRESENCE OF INTERACTIONS -- 4.6 EFFECT SIZE MEASURES -- 4.7 THREE-WAY, FOUR-WAY, AND HIGHER MODELS -- 4.8 SIMPLE MAIN EFFECTS -- 4.9 NESTED DESIGNS -- 4.9.1 Varieties of Nesting: Nesting of Levels Versus Subjects -- 4.10 ACHIEVEMENT AS A FUNCTION OF TEACHER AND TEXTBOOK: EXAMPLE OF FACTORIAL ANOVA IN R -- 4.10.1 Comparing Models Through AIC -- 4.10.2 Visualizing Main Effects and Interaction Effects Simultaneously -- 4.10.3 Simple Main Effects for Achievement Data: Breaking Down Interaction Effects -- 4.11 INTERACTION CONTRASTS -- 4.12 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- CHAPTER 5 INTRODUCTION TO RANDOM EFFECTS AND MIXED MODELS -- 5.1 WHAT IS RANDOM EFFECTS ANALYSIS OF VARIANCE? -- 5.2 THEORY OF RANDOM EFFECTS MODELS -- 5.3 ESTIMATION IN RANDOM EFFECTS MODELS -- 5.3.1 Transitioning from Fixed Effects to Random Effects -- 5.3.2 Expected Mean Squares for MS Between and MS Within -- 5.4 DEFINING NULL HYPOTHESES IN RANDOM EFFECTS MODELS -- 5.4.1 F-Ratio for Testing H0 -- 5.5 COMPARING NULL HYPOTHESES IN FIXED VERSUS RANDOM EFFECTS MODELS: THE IMPORTANCE OF ASSUMPTIONS -- 5.6 ESTIMATING VARIANCE COMPONENTS IN RANDOM EFFECTS MODELS: ANOVA, ML, REML ESTIMATORS -- 5.6.1 ANOVA Estimators of Variance Components -- 5.6.2 Maximum Likelihood and Restricted Maximum Likelihood.
5.7 IS ACHIEVEMENT A FUNCTION OF TEACHER? ONE-WAY RANDOM EFFECTS MODEL IN R -- 5.7.1 Proportion of Variance Accounted for by Teacher -- 5.8 R ANALYSIS USING REML -- 5.9 ANALYSIS IN SPSS: OBTAINING VARIANCE COMPONENTS -- 5.10 Factorial Random Effects: A Two-Way Model -- 5.11 FIXED EFFECTS VERSUS RANDOM EFFECTS: A WAY OF CONCEPTUALIZING THEIR DIFFERENCES -- 5.12 CONCEPTUALIZING THE TWO-WAY RANDOM EFFECTS MODEL: THE MAKE-UP OF A RANDOMLY CHOSEN OBSERVATION -- 5.13 SUMS OF SQUARES AND EXPECTED MEAN SQUARES FOR RANDOM EFFECTS: THE CONTAMINATING INFLUENCE OF INTERACTION EFFECTS -- 5.13.1 Testing Null Hypotheses -- 5.14 YOU GET WHAT YOU GO IN WITH: THE IMPORTANCE OF MODEL ASSUMPTIONS AND MODEL SELECTION -- 5.15 MIXED MODEL ANALYSIS OF VARIANCE: INCORPORATING FIXED AND RANDOM EFFECTS -- 5.15.1 Mixed Model in R -- 5.16 MIXED MODELS IN MATRICES -- 5.17 MULTILEVEL MODELING AS A SPECIAL CASE OF THE MIXED MODEL: INCORPORATING NESTING AND CLUSTERING -- 5.18 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- CHAPTER 6 RANDOMIZED BLOCKS AND REPEATED MEASURES -- 6.1 WHAT IS A RANDOMIZED BLOCK DESIGN? -- 6.2 RANDOMIZED BLOCK DESIGNS: SUBJECTS NESTED WITHIN BLOCKS -- 6.3 THEORY OF RANDOMIZED BLOCK DESIGNS -- 6.3.1 Nonadditive Randomized Block Design -- 6.3.2 Additive Randomized Block Design -- 6.4 TUKEY TEST FOR NONADDITIVITY -- 6.5 ASSUMPTIONS FOR THE COVARIANCE MATRIX -- 6.6 INTRACLASS CORRELATION -- 6.7 REPEATED MEASURES MODELS: A SPECIAL CASE OF RANDOMIZED BLOCK DESIGNS -- 6.8 INDEPENDENT VERSUS PAIRED-SAMPLES t-TEST -- 6.9 THE SUBJECT FACTOR: FIXED OR RANDOM EFFECT? -- 6.10 MODEL FOR ONE-WAY REPEATED MEASURES DESIGN -- 6.10.1 Expected Mean Squares for Repeated Measures Models -- 6.11 ANALYSIS USING R: ONE-WAY REPEATED MEASURES: LEARNING AS A FUNCTION OF TRIAL -- 6.12 ANALYSIS USING SPSS: ONE-WAY REPEATED MEASURES: LEARNING AS A FUNCTION OF TRIAL.
6.12.1 Which Results Should Be Interpreted?.
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| Record Nr. | UNINA-9910555019303321 |
Info
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
