Experiment Design and Statistical Methods introduces the concepts, principles, and techniques for carrying out a practical research project either in real world settings or laboratories - relevant to studies in psychology, education, life sciences, social sciences, medicine, and occupational and management research. The text covers: repeated measures unbalanced and non-randomized experiments and surveys choice of design adjustment for confounding variables model building and partition of variance covariance multiple regression Experiment Design and Statistical Methods contains a unique…mehr
Experiment Design and Statistical Methods introduces the concepts, principles, and techniques for carrying out a practical research project either in real world settings or laboratories - relevant to studies in psychology, education, life sciences, social sciences, medicine, and occupational and management research. The text covers: repeated measures unbalanced and non-randomized experiments and surveys choice of design adjustment for confounding variables model building and partition of variance covariance multiple regression Experiment Design and Statistical Methods contains a unique extension of the Venn diagram for understanding non-orthogonal design, and it includes exercises for developing the reader's confidence and competence. The book also examines advanced techniques for users of computer packages or data analysis, such as Minitab, SPSS, SAS, SuperANOVA, Statistica, BMPD, SYSTAT, Genstat, and GLIM.
David R. Boniface, University of Hertfordshire, Hatfield, UK.
Inhaltsangabe
Preface Part One: Statistical Design and Analysis for Basic Experiments 1 Introduction 1.1 Structure and scope of Part One 1.2 Inference for descriptive and experimental research 1.3 What is experimental research? 1.4 Theory testing generalization and cost-effectiveness 2 Introduction to four basic designs 2.1 Single-factor independent groups design 2.2 Single-factor repeated measures design 2.3 Two-factor design 2.4 Single-factor independent groups design with use of covariate 3 Overview of concepts and techniques 3.1 Variance 3.2 Variance of means 3.3 Random sampling and randomization 3.4 Confidence intervals 3.5 Sampling fluctuation and sampling error 3.6 Statistical significance 3.7 Formulating decision-rnaking as a test of hypotheses 3.8 Power 3.9 Sensitivity 3.10 Efficiency 3.11 Bias 3.12 Logistical constraints 4 Single-factor independent groups design 4.1 Introduction 4.2 The principles of the analysis of variance 4.3 Analysis of variance and significance test 4.4 The summary table and the decomposition of the total SS 4.5 Computational formulae for degrees of freedom and SSs 4.6 Underlying model and assumptions for tests of significance 4.7 Concept linkage for analysis of variance 4.8 Exercises 5 Single-factor repeated measures design 5.1 Introduction 5.2 Variation present in the repeated measures design 5.3 The principles of the analysis of variance 5.4 Analysis of variance and significance test 5.5 Computational formulae for SS and degrees of freedom 5.6 Underlying model and assumptions for tests of significance 5.7 Exercises 6 Two-factor independent groups design 6.1 Introduction 6.2 Example of two-factor design 6.3 The effect of the interaction of the factors 6.4 The principles of the analysis of variance 6.5 The summary table and tests of significance 6.6 Formulae for hand calculation of SSs 6.7 Underlying model and assumptions for tests of significance 6.8 Exercises 7 Single-factor independent groups design with covariate 7.1 Introduction 7.2 The concept and technique of covariate adjustment 7.3 The effect of covariate adjustment on variance estimates 7.4 Underlying model and assumptions for tests of significance 7.5 Exercises 8 Contrasts and comparisons among means 8.1 Introduction 8.2 Formulating and testing a comparison among means 8.3 A posteriori tests of comparisons 8.4 Overview of decisions for contrasts and comparisons of means 8.5 Exercises 9 Power and sensitivity in design decisions 9.1 Introduction 9.2 Sensitivity and efficiency gains from a continuous covariate 9.3 Sensitivity and efficiency gains from a category covariate 9.4 Choice of sample size 9.5 Choice of within- or between-subjects design 9.6 Summary of influences on design decisions 9.7 Exercises Part Two: Unbalanced Non-Randomized and Survey Designs 10 Unbalanced and confounded designs 10.1 Introduction 10.2 Two-factor unbalanced design 10.3 Confounding in one-variable non-randomized designs 10.4 Exercises 11 Multiple regression 11.1 Introduction 11.2 Overview of designs variables and orthogonality 11.3 Comparison of models with category and continuous independent variables 11.4 Glossary of terms for multiple regression 11.5 Sequential model construction 11.6 Exercises Part Three: Analysis for Further Experiment Designs 12 Two-factor designs with between- and within-subjects factors 12.1 Introduction 12.2 Example of a BW design 12.3 Example of a WW design 12.4 Overview of rules for the ANOVA summary table for designs BB BW and WW 12.5 Tests of significance for simple effects in BW and WW designs 12.6 Calculation pro forma for simple effects in two-factor designs 12.7 Contrasts and comparisons in the BW and WW designs 12.8 Exercises 13 Three-factor designs 13.1 Introduction 13.2 Example of a BBB design 13.3 Example of a BBW design 13.4 Example of a BWW design 13.5 Summary of rules for analysis of BBB BBW BWW and WWW designs 13.6 Exercises Appendix A: Hints on use of computer programs Appendix B: Additional exercises for Chapters 5-13 Appendix C: Solutions to exercises for Chapters 4-13 Appendix D: Approximate degrees of freedom for test of significance for simple effects in BW and WW designs Appendix E: Rationale for approximate sample size formula Appendix F: Tables of critical values References Index
Preface Part One: Statistical Design and Analysis for Basic Experiments 1 Introduction 1.1 Structure and scope of Part One 1.2 Inference for descriptive and experimental research 1.3 What is experimental research? 1.4 Theory testing generalization and cost-effectiveness 2 Introduction to four basic designs 2.1 Single-factor independent groups design 2.2 Single-factor repeated measures design 2.3 Two-factor design 2.4 Single-factor independent groups design with use of covariate 3 Overview of concepts and techniques 3.1 Variance 3.2 Variance of means 3.3 Random sampling and randomization 3.4 Confidence intervals 3.5 Sampling fluctuation and sampling error 3.6 Statistical significance 3.7 Formulating decision-rnaking as a test of hypotheses 3.8 Power 3.9 Sensitivity 3.10 Efficiency 3.11 Bias 3.12 Logistical constraints 4 Single-factor independent groups design 4.1 Introduction 4.2 The principles of the analysis of variance 4.3 Analysis of variance and significance test 4.4 The summary table and the decomposition of the total SS 4.5 Computational formulae for degrees of freedom and SSs 4.6 Underlying model and assumptions for tests of significance 4.7 Concept linkage for analysis of variance 4.8 Exercises 5 Single-factor repeated measures design 5.1 Introduction 5.2 Variation present in the repeated measures design 5.3 The principles of the analysis of variance 5.4 Analysis of variance and significance test 5.5 Computational formulae for SS and degrees of freedom 5.6 Underlying model and assumptions for tests of significance 5.7 Exercises 6 Two-factor independent groups design 6.1 Introduction 6.2 Example of two-factor design 6.3 The effect of the interaction of the factors 6.4 The principles of the analysis of variance 6.5 The summary table and tests of significance 6.6 Formulae for hand calculation of SSs 6.7 Underlying model and assumptions for tests of significance 6.8 Exercises 7 Single-factor independent groups design with covariate 7.1 Introduction 7.2 The concept and technique of covariate adjustment 7.3 The effect of covariate adjustment on variance estimates 7.4 Underlying model and assumptions for tests of significance 7.5 Exercises 8 Contrasts and comparisons among means 8.1 Introduction 8.2 Formulating and testing a comparison among means 8.3 A posteriori tests of comparisons 8.4 Overview of decisions for contrasts and comparisons of means 8.5 Exercises 9 Power and sensitivity in design decisions 9.1 Introduction 9.2 Sensitivity and efficiency gains from a continuous covariate 9.3 Sensitivity and efficiency gains from a category covariate 9.4 Choice of sample size 9.5 Choice of within- or between-subjects design 9.6 Summary of influences on design decisions 9.7 Exercises Part Two: Unbalanced Non-Randomized and Survey Designs 10 Unbalanced and confounded designs 10.1 Introduction 10.2 Two-factor unbalanced design 10.3 Confounding in one-variable non-randomized designs 10.4 Exercises 11 Multiple regression 11.1 Introduction 11.2 Overview of designs variables and orthogonality 11.3 Comparison of models with category and continuous independent variables 11.4 Glossary of terms for multiple regression 11.5 Sequential model construction 11.6 Exercises Part Three: Analysis for Further Experiment Designs 12 Two-factor designs with between- and within-subjects factors 12.1 Introduction 12.2 Example of a BW design 12.3 Example of a WW design 12.4 Overview of rules for the ANOVA summary table for designs BB BW and WW 12.5 Tests of significance for simple effects in BW and WW designs 12.6 Calculation pro forma for simple effects in two-factor designs 12.7 Contrasts and comparisons in the BW and WW designs 12.8 Exercises 13 Three-factor designs 13.1 Introduction 13.2 Example of a BBB design 13.3 Example of a BBW design 13.4 Example of a BWW design 13.5 Summary of rules for analysis of BBB BBW BWW and WWW designs 13.6 Exercises Appendix A: Hints on use of computer programs Appendix B: Additional exercises for Chapters 5-13 Appendix C: Solutions to exercises for Chapters 4-13 Appendix D: Approximate degrees of freedom for test of significance for simple effects in BW and WW designs Appendix E: Rationale for approximate sample size formula Appendix F: Tables of critical values References Index
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