Experimental Design and Analysis (Quantitative Applications in the Social Sciences)
Language: English
Published by SAGE Publications, Inc, 1990
Series: Book 58 of 194 - Quantitative Applications in the Social Sciences
- Softcover
- Used

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- Title
- Experimental Design and Analysis (Quantitative Applications in the Social Sciences)
- Author
- Brown, Steven R.,Melamed, Lawrence E.
- Publisher
- SAGE Publications, Inc
- Publication year
- 1990
- Condition
- Very Good
- Binding
- paperback
- Language
- English
- ISBN 10
- 0803938543
- ISBN 13
- 9780803938540
- Series
- Book 58 of 194: Quantitative Applications in the Social Sciences
"Brown and Melamed′s book is one of the best concise treatments of the design and analysis of experiments that I have seen. The authors begin by showing the significance of variability (variance) for the analysis of experiments, and clearly illustrate the utility of the analysis of variance (ANOVA) model to the analysis of experimental data. They also provide a clear discussion of more advanced topics such as nested, factorial, split-plot, and repeated measures designs. Their book is comprehensive, handles each topic deftly, and should be readily accessible to researchers with a good grounding in basic statistics."
--Contemporary Sociology
"The book is well written and includes useful examples. . . . Useful to researchers in both the planning and analysis phases of an experimental study."
--ANNA Journal
"Introductory, well written, and has illustrative examples. Highly recommended for introductory courses and self study; the book can be supplemented easily with a treatment of covariates from other available study materials."
--Journal of Marketing Research
This volume introduces the reader to one of the most fundamental topics in social science statistics--experimental design. The authors clearly show how to select an experimental design based on the number of independent variables, the sources and number of extraneous variables, and the number of subjects. Other topics addressed include variability, hypothesis testing, how ANOVA can be extended to the multi-group situation, the logic of the t test, and completely randomized designs.
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