Research Design and Statistical Analysis provides comprehensive
coverage of the design principles and statistical concepts
necessary to make sense of real data. The book s goal is to provide
a strong conceptual foundation to enable readers to generalize
concepts to new research situations. Emphasis is placed on the
underlying logic and assumptions of the analysis and what it tells
the researcher, the limitations of the analysis, and the
consequences of violating assumptions. Sampling, design efficiency,
and statistical models are emphasized throughout. As per APA
recommendations, emphasis is also placed on data exploration,
effect size measures, confidence intervals, and using power
analyses to determine sample size. "Real-world" data sets are used
to illustrate data exploration, analysis, and interpretation. The
book offers a rare blend of the underlying statistical assumptions,
the consequences of their violations, and practical advice on
dealing with them.
Changes in the New Edition:
- Each section of the book concludes with a chapter that provides
an integrated example of how to apply the concepts and procedures
covered in the chapters of the section. In addition, the advantages
and disadvantages of alternative designs are discussed.
- A new chapter (1) reviews the major steps in planning and
executing a study, and the implications of those decisions for
subsequent analyses and interpretations.
- A new chapter (13) compares experimental designs to reinforce
the connection between design and analysis and to help readers
achieve the most efficient research study.
- A new chapter (27) on common errors in data analysis and
interpretation.
- Increased emphasis on power analyses to determine sample size
using the G*Power 3 program.
- Many new data sets and problems.
- More examples of the use of SPSS (PASW) Version 17, although
the analyses exemplified are readily carried out by any of the
major statistical software packages.
- A companion website with the data used in the text and the
exercises in SPSS and Excel formats; SPSS syntax files for
performing analyses; extra material on logistic and multiple
regression; technical notes that develop some of the formulas; and
a solutions manual and the text figures and tables for instructors
only.
Part 1 reviews research planning, data exploration, and basic
concepts in statistics including sampling, hypothesis testing,
measures of effect size, estimators, and confidence intervals. Part
2 presents between-subject designs. The statistical models
underlying the analysis of variance for these designs are
emphasized, along with the role of expected mean squares in
estimating effects of variables, the interpretation of nteractions,
and procedures for testing contrasts and controlling error rates.
Part 3 focuses on repeated-measures designs and considers the
advantages and disadvantages of different mixed designs. Part 4
presents detailed coverage of correlation and bivariate and
multiple regression with emphasis on interpretation and common
errors, and discusses the usefulness and limitations of these
procedures as tools for prediction and for developing theory.
This is one of the few books with coverage sufficient for a
2-semester course sequence in experimental design and statistics as
taught in psychology, education, and other behavioral, social, and
health sciences. Incorporating the analyses of both experimental
and observational data provides continuity of concepts and
notation. Prerequisites include courses on basic research methods
and statistics. The book is also an excellent resource for
practicing researchers."
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