Power analysis for experimental research : a practical guide for the biological, medical, and social sciences / R. Barker Bausell, Yu-Fang Li.

By:

Bausell, R. Barker, 1942-

Contributor(s):

Li, Yu-Fang, 1966-

Material type: Text

TextPublication details: Cambridge ; New York : Cambridge University Press, 2002.Description: 1 online resource (xi, 363 pages) : illustrationsContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

0511064039
9780511064036
0511057709
9780511057700
051107249X
9780511072499
0511170416
9780511170416
0511541937
9780511541933
9780521809160
0521809169
1107131820
9781107131828
9786610433797
6610433798
0511331258
9780511331251
1139147560
9781139147569
9780521024563
0521024560

Subject(s): Genre/Form:

Additional physical formats: Print version:: Power analysis for experimental research.DDC classification:

610/.7/2 21

LOC classification:

R853.S7 B375 2002eb

NLM classification:

2002 K-964
W 20.5

Other classification:

R-33
R195

Online resources:

Click here to access online

Contents:

Cover; Half-title; Title; Copyright; Dedication; Contents; Introduction; 1 The conceptual underpinnings of statistical power; 2 Strategies for increasing statistical power; 3 General guidelines for conducting a power analysis; 4 The t-test for independent samples; 5 The paired t-test; 6 One-way between subjects analysis of variance; 7 One-way between subjects analysis of covariance; 8 One-way repeated measures analysis of variance; 9 Interaction effects for factorial analysis of variance; 10 Power analysis for more complex designs.

Summary: Power analysis is an essential tool for determining whether a statistically significant result can be expected in an experiment prior to the experiment being performed. This book provides researchers in the medical and biological sciences with an easy-to-use guide to the application of power analysis in the design of experiments.

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Includes bibliographical references (pages 358-361) and index.

Power analysis is an essential tool for determining whether a statistically significant result can be expected in an experiment prior to the experiment being performed. This book provides researchers in the medical and biological sciences with an easy-to-use guide to the application of power analysis in the design of experiments.