David R. Boniface

Experiment Design and Statistical Methods For Behavioural and Social Research (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 272
• Erscheinungstermin: 20. Mai 2019
• Englisch
• ISBN-13: 9781351449298
• Artikelnr.: 69041982

Autorenporträt

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